Image Classification
Transformers
TensorBoard
Safetensors
English
swinv2
pytoroch
Swinv2ForImageClassification
food-classification
Generated from Trainer
Instructions to use arnabdhar/Swin-V2-base-Food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arnabdhar/Swin-V2-base-Food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arnabdhar/Swin-V2-base-Food") 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("arnabdhar/Swin-V2-base-Food") model = AutoModelForImageClassification.from_pretrained("arnabdhar/Swin-V2-base-Food", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a02c1ce4e552c9bb33e64334db1a5eceed53a65234b37d4a30bbb535747c1153
- Size of remote file:
- 4.6 kB
- SHA256:
- 5f0fe3194b0092e09185386c26999b6dc3c722fc08df0cb77df9817528db6078
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.