Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Augusto777/vit-base-patch16-224-R1-40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Augusto777/vit-base-patch16-224-R1-40 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Augusto777/vit-base-patch16-224-R1-40") 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-R1-40") model = AutoModelForImageClassification.from_pretrained("Augusto777/vit-base-patch16-224-R1-40", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from Augusto777/vit-base-patch16-224-R1-40: direct link, hf CLI and curl.
- Browser
- Download file 174 Bytes
-
https://huggingface.co/Augusto777/vit-base-patch16-224-R1-40/resolve/main/train_results.json
- Command line
-
hf download hf://Augusto777/vit-base-patch16-224-R1-40/train_results.json
-
curl -L -o train_results.json https://huggingface.co/Augusto777/vit-base-patch16-224-R1-40/resolve/main/train_results.json
174 Bytes
| { | |
| "epoch": 39.74, | |
| "train_loss": 0.11905086408124159, | |
| "train_runtime": 3040.3234, | |
| "train_samples_per_second": 64.204, | |
| "train_steps_per_second": 0.5 | |
| } |