Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jeraldflowers/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeraldflowers/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jeraldflowers/vit_model") 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("jeraldflowers/vit_model") model = AutoModelForImageClassification.from_pretrained("jeraldflowers/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from jeraldflowers/vit_model: direct link, hf CLI and curl.
- Browser
- Download file 187 Bytes
-
https://huggingface.co/jeraldflowers/vit_model/resolve/main/eval_results.json
- Command line
-
hf download hf://jeraldflowers/vit_model/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/jeraldflowers/vit_model/resolve/main/eval_results.json
187 Bytes
| { | |
| "epoch": 4.0, | |
| "eval_accuracy": 1.0, | |
| "eval_loss": 0.00948187243193388, | |
| "eval_runtime": 2.9079, | |
| "eval_samples_per_second": 45.738, | |
| "eval_steps_per_second": 5.846 | |
| } |