Object Detection
Transformers
PyTorch
TensorBoard
deformable_detr
Generated from Trainer
computer-vision
autonomous-driving
data-centric-ai
open-vocabulary
deformable-detr
Instructions to use mcity-data-engine/fisheye8k_facebook_deformable-detr-box-supervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_facebook_deformable-detr-box-supervised with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_facebook_deformable-detr-box-supervised")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_facebook_deformable-detr-box-supervised") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_facebook_deformable-detr-box-supervised", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, links, usage, and expand details
#3
by nielsr HF Staff - opened
This PR significantly enhances the model card for fisheye8k_facebook_deformable-detr-box-supervised by:
- Adding the
pipeline_tag: object-detectionto ensure better discoverability on the Hugging Face Hub. - Updating the
licensetomit, as specified in the associated paper and GitHub repository. - Including more descriptive
tags(e.g.,computer-vision,autonomous-driving,data-centric-ai,open-vocabulary,deformable-detr) for improved searchability and context. - Adding direct links to the related paper (
2504.21614), the main GitHub repository (mcity/mcity_data_engine), and the project's documentation page (mcity.github.io/mcity_data_engine). - Expanding the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections with information from the paper abstract and GitHub README.
- Providing a clear "Sample Usage" code snippet for easy inference with the
transformerslibrary. - Including the "Acknowledgements" and "Citation" sections from the GitHub README for proper attribution.
- Removing auto-generated placeholder comments and irrelevant file information.
These updates provide a more comprehensive and user-friendly model card, making it easier for researchers and developers to understand and utilize this model within the context of the Mcity Data Engine.
danielbogdoll changed pull request status to merged