Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use autoevaluate/image-multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/image-multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="autoevaluate/image-multi-class-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("autoevaluate/image-multi-class-classification") model = AutoModelForImageClassification.from_pretrained("autoevaluate/image-multi-class-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f66f01dc23f486a9dc5ed38ce983d8e60e43d92b5d1d628a88be9d260c87b5cb
- Size of remote file:
- 110 MB
- SHA256:
- 510b80c9a6460776c9e7767ad440b20f3200aebb49de7cec206d1bfc5f9af16e
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