Instructions to use mcity-data-engine/fisheye8k_jozhang97_deta-swin-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_jozhang97_deta-swin-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_jozhang97_deta-swin-large")# Load model directly from transformers import AutoModelForObjectDetection model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_jozhang97_deta-swin-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add object-detection pipeline tag, license, links, and usage example
#3
by nielsr HF Staff - opened
This PR significantly improves the model card for fisheye8k_jozhang97_deta-swin-large by:
- Adding the
pipeline_tag: object-detectionto the metadata, ensuring better discoverability (e.g., via https://huggingface.co/models?pipeline_tag=object-detection). - Adding the
license: mitto the metadata, as explicitly stated in the paper and GitHub repository. - Adding direct links to the associated research paper, the project homepage, and the GitHub repository.
- Providing a clear, runnable Python code snippet for inference using the
transformerslibrary, replacing the generic "More information needed" sections. - Enriching the model description, intended uses, and training/evaluation data sections with context from the research paper and project details.
- Removing redundant auto-generated comments and technical file information.
- Adding an Acknowledgements and Citation section for proper attribution.
danielbogdoll changed pull request status to merged