Instructions to use CVPROJ25/FINETUNED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CVPROJ25/FINETUNED with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="CVPROJ25/FINETUNED") 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("CVPROJ25/FINETUNED") model = AutoModelForImageClassification.from_pretrained("CVPROJ25/FINETUNED", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from CVPROJ25/FINETUNED: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/CVPROJ25/FINETUNED/resolve/main/eval_results.json
- Command line
-
hf download hf://CVPROJ25/FINETUNED/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/CVPROJ25/FINETUNED/resolve/main/eval_results.json
190 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.773, | |
| "eval_loss": 0.8921805620193481, | |
| "eval_runtime": 100.2563, | |
| "eval_samples_per_second": 19.949, | |
| "eval_steps_per_second": 1.247 | |
| } |