Instructions to use gary2002/output_dir-full_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gary2002/output_dir-full_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="gary2002/output_dir-full_dataset") 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("gary2002/output_dir-full_dataset") model = AutoModelForImageClassification.from_pretrained("gary2002/output_dir-full_dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from gary2002/output_dir-full_dataset: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/gary2002/output_dir-full_dataset/resolve/main/eval_results.json
- Command line
-
hf download hf://gary2002/output_dir-full_dataset/eval_results.json
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curl -L -o eval_results.json https://huggingface.co/gary2002/output_dir-full_dataset/resolve/main/eval_results.json
204 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9169753086419753, | |
| "eval_loss": 0.20968997478485107, | |
| "eval_runtime": 111.5277, | |
| "eval_samples_per_second": 29.051, | |
| "eval_steps_per_second": 7.263 | |
| } |