sam-vit-large

Name: sam-vit-large
Author: facebook

131.9K

2 languages

license:apache-2.0

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132K downloads

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Quick Summary

AI model with specialized capabilities.

Code Examples

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

Usagepythontransformers

from PIL import Image
import requests
from transformers import SamModel, SamProcessor

model = SamModel.from_pretrained("facebook/sam-vit-large")
processor = SamProcessor.from_pretrained("facebook/sam-vit-large")

img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
input_points = [[[450, 600]]] # 2D localization of a window

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

pythontransformers

from transformers import pipeline
generator =  pipeline("mask-generation", device = 0, points_per_batch = 256)
image_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
outputs = generator(image_url, points_per_batch = 256)

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