Instructions to use RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") 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("RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") model = AutoModelForImageClassification.from_pretrained("RobertoSonic/swinv2-base-patch4-window8-256-dmae-humeda-DAV15") - Notebooks
- Google Colab
- Kaggle
File size: 200 Bytes
5d8c4c8 | 1 2 3 4 5 6 7 8 | {
"epoch": 41.869565217391305,
"eval_accuracy": 0.75,
"eval_loss": 0.842289388179779,
"eval_runtime": 1.5112,
"eval_samples_per_second": 34.41,
"eval_steps_per_second": 1.323
} |