Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use arpanl/Fine-Tuned_Model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Fine-Tuned_Model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Fine-Tuned_Model3") 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("arpanl/Fine-Tuned_Model3") model = AutoModelForImageClassification.from_pretrained("arpanl/Fine-Tuned_Model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from arpanl/Fine-Tuned_Model3: direct link, hf CLI and curl.
- Browser
- Download file 222 Bytes
-
https://huggingface.co/arpanl/Fine-Tuned_Model3/resolve/main/eval_results.json
- Command line
-
hf download hf://arpanl/Fine-Tuned_Model3/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/arpanl/Fine-Tuned_Model3/resolve/main/eval_results.json
222 Bytes
| { | |
| "epoch": 30.0, | |
| "eval_accuracy": 0.608, | |
| "eval_f1": 0.5096170704866357, | |
| "eval_loss": 0.736162006855011, | |
| "eval_runtime": 44.8107, | |
| "eval_samples_per_second": 2.79, | |
| "eval_steps_per_second": 0.357 | |
| } |