Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use sasha/swin-tiny-finetuned-dogfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasha/swin-tiny-finetuned-dogfood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sasha/swin-tiny-finetuned-dogfood") 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("sasha/swin-tiny-finetuned-dogfood") model = AutoModelForImageClassification.from_pretrained("sasha/swin-tiny-finetuned-dogfood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d1ef3b98dd748439429bffcc6a24a4bd1b0c4fa738b1cef51dfa8aae8284249
- Size of remote file:
- 110 MB
- SHA256:
- c8e4174888a60a1c2509871ee3cd5718c91b6ffcf59c8053864f2b39004001ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.