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 train_results.json from kobe/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 167 Bytes
-
https://huggingface.co/kobe/vit-base-beans/resolve/main/train_results.json
- Command line
-
hf download hf://kobe/vit-base-beans/train_results.json
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curl -L -o train_results.json https://huggingface.co/kobe/vit-base-beans/resolve/main/train_results.json
167 Bytes
| { | |
| "epoch": 5.0, | |
| "train_loss": 0.23454355464531826, | |
| "train_runtime": 147.3622, | |
| "train_samples_per_second": 35.084, | |
| "train_steps_per_second": 4.411 | |
| } |