fisheye8k_Omnifact_conditional-detr-resnet-101-dc5

Name: fisheye8k_Omnifact_conditional-detr-resnet-101-dc5
Author: mcity-data-engine

license:mit

mcity-data-engine

Image Model

OTHER

2504.21614B params

New

11 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

5598GB+ RAM

Mobile

Laptop

Server

Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2333GB+ RAM

Code Examples

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Usagepythontransformers

from transformers import pipeline
from PIL import Image
import requests
from io import BytesIO

# Load the object detection pipeline
model_id = "mcity-data-engine/fisheye8k_Omnifact_conditional-detr-resnet-101-dc5"
detector = pipeline("object-detection", model=model_id)

# Example image (replace with your fisheye image or a relevant ITS image)
# This example uses a generic image. For best results, use an image from the model's domain.
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/conditional_detr_image.png"
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")

# Perform inference
predictions = detector(image)

# Print detected objects
for pred in predictions:
    print(f"Label: {pred['label']}, Score: {pred['score']:.2f}, Box: {pred['box']}")

# Example output format:
# [{'box': {'xmin': 10, 'ymin': 20, 'xmax': 100, 'ymax': 120}, 'score': 0.98, 'label': 'Car'}]

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.