Instructions to use vananhle/swinv2-base-patch4-window8-256-isic217 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vananhle/swinv2-base-patch4-window8-256-isic217 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vananhle/swinv2-base-patch4-window8-256-isic217") 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("vananhle/swinv2-base-patch4-window8-256-isic217") model = AutoModelForImageClassification.from_pretrained("vananhle/swinv2-base-patch4-window8-256-isic217") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 9.887640449438202, | |
| "eval_accuracy": 0.65, | |
| "eval_loss": 1.158837914466858, | |
| "eval_runtime": 3.034, | |
| "eval_samples_per_second": 6.592, | |
| "eval_steps_per_second": 3.296 | |
| } |