Instructions to use sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW") model = AutoModelForImageClassification.from_pretrained("sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download all_results.json from sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW: direct link, hf CLI and curl.
- Browser
- Download file 575 Bytes
-
https://huggingface.co/sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW/resolve/main/all_results.json
- Command line
-
hf download hf://sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW/all_results.json
-
curl -L -o all_results.json https://huggingface.co/sidushdid/ViT-base-patch16-BUSI-Mendeley-AdamW/resolve/main/all_results.json
575 Bytes
| { | |
| "epoch": 143.48, | |
| "eval_accuracy": 0.9545454545454546, | |
| "eval_loss": 0.26813745498657227, | |
| "eval_runtime": 2.1151, | |
| "eval_samples_per_second": 72.809, | |
| "eval_steps_per_second": 4.728, | |
| "test_accuracy": 0.9483870967741935, | |
| "test_loss": 0.23885636031627655, | |
| "test_runtime": 1.9904, | |
| "test_samples_per_second": 77.872, | |
| "test_steps_per_second": 5.024, | |
| "total_flos": 8.017005638819359e+18, | |
| "train_loss": 0.1887252431566065, | |
| "train_runtime": 3245.2786, | |
| "train_samples_per_second": 33.325, | |
| "train_steps_per_second": 0.508 | |
| } |