rtdetr_v2_r50vd

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1 language

license:apache-2.0

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PekingU

Image Model

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12K downloads

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Quick Summary

The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu.

Code Examples

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

**How to use**pythontransformers

import torch
import requests

from PIL import Image
from transformers import RTDetrV2ForObjectDetection, RTDetrImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_v2_r50vd")
model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/rtdetr_v2_r50vd")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
     outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([(image.height, image.width)]), threshold=0.5)

for result in results:
     for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
         score, label = score.item(), label_id.item()
         box = [round(i, 2) for i in box.tolist()]
         print(f"{model.config.id2label[label]}: {score:.2f} {box}")

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