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
File size: 936 Bytes
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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
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