Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jackie68/foodImageToText with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackie68/foodImageToText with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jackie68/foodImageToText") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jackie68/foodImageToText") model = AutoModelForImageClassification.from_pretrained("jackie68/foodImageToText", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jackie68/foodImageToText: direct link, hf CLI and curl.
- Browser
- Download file 344 MB
-
https://huggingface.co/jackie68/foodImageToText/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jackie68/foodImageToText/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jackie68/foodImageToText/resolve/main/pytorch_model.bin
344 MB
- Xet hash:
- 2294b0003be57ebeb5b85c0455530c5539af568445cb83372da2fafbb02eeb0e
- Size of remote file:
- 344 MB
- SHA256:
- d73946ac8fd777c1562c833c1b301a0106e45a5a0b60b9be7e6b88057062075e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.