Instructions to use Andron00e/ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/ViTForImageClassification") 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("Andron00e/ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("Andron00e/ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Andron00e/ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
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https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/eval_results.json
- Command line
-
hf download hf://Andron00e/ViTForImageClassification/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/Andron00e/ViTForImageClassification/resolve/main/eval_results.json
204 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 0.9678333333333333, | |
| "eval_loss": 0.11992065608501434, | |
| "eval_runtime": 169.7399, | |
| "eval_samples_per_second": 35.348, | |
| "eval_steps_per_second": 0.554 | |
| } |