Instructions to use kobe/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kobe/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kobe/vit-base-beans") 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("kobe/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("kobe/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from kobe/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 202 Bytes
-
https://huggingface.co/kobe/vit-base-beans/resolve/main/eval_results.json
- Command line
-
hf download hf://kobe/vit-base-beans/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/kobe/vit-base-beans/resolve/main/eval_results.json
202 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9849624060150376, | |
| "eval_loss": 0.0866488441824913, | |
| "eval_runtime": 1.3516, | |
| "eval_samples_per_second": 98.403, | |
| "eval_steps_per_second": 12.578 | |
| } |