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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from autoevaluate/image-multi-class-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/autoevaluate/image-multi-class-classification/resolve/refs%2Fpr%2F2/training_args.bin
- Command line
-
hf download hf://autoevaluate/image-multi-class-classification@refs/pr/2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/autoevaluate/image-multi-class-classification/resolve/refs%2Fpr%2F2/training_args.bin
3.31 kB
- Xet hash:
- aa1edcda032a69a561c7b1fa6b9dbb04f3af8a9083d37e70aeb48d65d3898e71
- Size of remote file:
- 3.31 kB
- SHA256:
- 25b3008f2df2db06844e1c7300c08af1eec38c6580f96246ac4dc0758c823eb2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.