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
File size: 221 Bytes
d97c895 | 1 2 3 4 5 6 7 8 | {
"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
} |