Instructions to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_microsoft_conditional-detr-resnet-50") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, update license, and expand content
#4
by nielsr HF Staff - opened
This PR significantly improves the model card for fisheye8k_microsoft_conditional-detr-resnet-50 by:
- Adding the
pipeline_tag: object-detectionto the metadata, which enhances discoverability on the Hugging Face Hub. - Updating the
licensefromapache-2.0tomit, aligning with the explicit declaration in the associated paper abstract and GitHub repository. - Providing a comprehensive "Model description" that contextualizes the model within the Mcity Data Engine project.
- Including direct links to the paper, the project page, and the GitHub repository for easy access to related resources.
- Adding a clear Python code example for performing object detection inference using the
transformerslibrary. - Expanding on "Intended uses & limitations" and "Training and evaluation data" sections.
- Including the academic citation and acknowledgements from the project's GitHub README.
These updates aim to provide researchers and users with a more complete and accurate understanding of the model.
danielbogdoll changed pull request status to merged