Instructions to use ArSenic04/Sports_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArSenic04/Sports_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ArSenic04/Sports_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ArSenic04/Sports_Classification") model = AutoModelForImageClassification.from_pretrained("ArSenic04/Sports_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - image-classification | |
| - pytorch | |
| - huggingpics | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: Sports_Classification | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.8315789699554443 | |
| # Sports_Classification | |
| Autogenerated by HuggingPics🤗🖼️ | |
| Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). | |
| Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). | |
| ## Example Images | |
| #### baseball | |
|  | |
| #### cricket ball | |
|  | |
| #### football | |
|  | |
| #### tennisball | |
|  | |
| #### volleyball | |
|  |