Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Augusto777/vit-base-patch16-224-RX1-24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Augusto777/vit-base-patch16-224-RX1-24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Augusto777/vit-base-patch16-224-RX1-24") 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("Augusto777/vit-base-patch16-224-RX1-24") model = AutoModelForImageClassification.from_pretrained("Augusto777/vit-base-patch16-224-RX1-24", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from Augusto777/vit-base-patch16-224-RX1-24: direct link, hf CLI and curl.
- Browser
- Download file 209 Bytes
-
https://huggingface.co/Augusto777/vit-base-patch16-224-RX1-24/resolve/main/eval_results.json
- Command line
-
hf download hf://Augusto777/vit-base-patch16-224-RX1-24/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/Augusto777/vit-base-patch16-224-RX1-24/resolve/main/eval_results.json
209 Bytes
| { | |
| "epoch": 22.4, | |
| "eval_accuracy": 0.8431372549019608, | |
| "eval_loss": 0.5686964988708496, | |
| "eval_runtime": 1.0601, | |
| "eval_samples_per_second": 48.108, | |
| "eval_steps_per_second": 1.887 | |
| } |