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
| { | |
| "epoch": 41.869565217391305, | |
| "total_flos": 2.8899664857724355e+18, | |
| "train_loss": 2.0296274548485167, | |
| "train_runtime": 2115.3253, | |
| "train_samples_per_second": 14.474, | |
| "train_steps_per_second": 0.099 | |
| } |