Object Detection
Transformers
PyTorch
TensorBoard
deformable_detr
Generated from Trainer
computer-vision
deformable-detr
detr
Instructions to use mcity-data-engine/fisheye8k_SenseTime_deformable-detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_SenseTime_deformable-detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_SenseTime_deformable-detr")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_SenseTime_deformable-detr") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_SenseTime_deformable-detr") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, links, description, and usage
#3
by nielsr HF Staff - opened
This PR significantly improves the model card for fisheye8k_SenseTime_deformable-detr by:
- Adding the
pipeline_tag: object-detectionto the metadata, which enhances discoverability on the Hub. - Connecting the model to its foundational paper, Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection.
- Adding direct links to the project homepage and the GitHub repository for easier access to more information and code.
- Providing a clear sample usage code snippet using the
transformerslibrary. - Expanding the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections with details extracted from the paper abstract and GitHub repository information.
This makes the model card much more informative and user-friendly.
danielbogdoll changed pull request status to merged