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
swinv2-base-patch4-window8-256-dmae-humeda-DAV15 / runs /Jan21_00-03-14_feec14bb91f4 /events.out.tfevents.1737417814.feec14bb91f4.14041.0
- Xet hash:
- 61092544d86f77bc63c0f55feded33e63d1256e46e7a5e7cedafd2208a5a41b8
- Size of remote file:
- 23.9 kB
- SHA256:
- e6bf34b3a11ff8f4b292d3f7465d04eefe0e3b42fadf6bb2c05fa36170df231b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.