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, | |
| "total_flos": 1.8042639737683968e+17, | |
| "train_loss": 1.1781822247938676, | |
| "train_runtime": 376.1356, | |
| "train_samples_per_second": 4.732, | |
| "train_steps_per_second": 0.585 | |
| } |