Instructions to use ar5entum/vit-base-patch16-224-leaf-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ar5entum/vit-base-patch16-224-leaf-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ar5entum/vit-base-patch16-224-leaf-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("ar5entum/vit-base-patch16-224-leaf-classification") model = AutoModelForImageClassification.from_pretrained("ar5entum/vit-base-patch16-224-leaf-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from ar5entum/vit-base-patch16-224-leaf-classification: direct link, hf CLI and curl.
- Browser
- Download file 201 Bytes
-
https://huggingface.co/ar5entum/vit-base-patch16-224-leaf-classification/resolve/main/eval_results.json
- Command line
-
hf download hf://ar5entum/vit-base-patch16-224-leaf-classification/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/ar5entum/vit-base-patch16-224-leaf-classification/resolve/main/eval_results.json
201 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9514767932489452, | |
| "eval_loss": 0.8745647668838501, | |
| "eval_runtime": 51.5176, | |
| "eval_samples_per_second": 9.201, | |
| "eval_steps_per_second": 1.165 | |
| } |