Instructions to use mcity-data-engine/fisheye8k_hustvl_yolos-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcity-data-engine/fisheye8k_hustvl_yolos-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="mcity-data-engine/fisheye8k_hustvl_yolos-base")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("mcity-data-engine/fisheye8k_hustvl_yolos-base") model = AutoModelForObjectDetection.from_pretrained("mcity-data-engine/fisheye8k_hustvl_yolos-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, detailed description, and usage
#3
by nielsr HF Staff - opened
This PR significantly enhances the model card for fisheye8k_hustvl_yolos-base by:
- Adding the
pipeline_tag: object-detectionto the metadata, making the model easily discoverable for object detection tasks. - Expanding the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections using details from the associated paper and project information.
- Including a Python code snippet for quick inference.
- Adding direct links to the research paper, the project homepage, and the GitHub repository for easy access to more resources.
danielbogdoll changed pull request status to merged