Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Ahmeng/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ahmeng/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Ahmeng/image_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("Ahmeng/image_classification") model = AutoModelForImageClassification.from_pretrained("Ahmeng/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ahmeng/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Ahmeng/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Ahmeng/image_classification@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ahmeng/image_classification/resolve/refs%2Fpr%2F1/training_args.bin
3.96 kB
- Xet hash:
- 54a7ab7609cdbcec0ca3f2b0f07b5303ea8816be59a8e454a16de71b5d2f5116
- Size of remote file:
- 3.96 kB
- SHA256:
- c5a6bab0a126fcf26913814d374892ae34c17432708e3f31e6afcda3eda113de
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