Image Classification
Transformers
Safetensors
PyTorch
food-recognition
dinov3
vision-transformer
tsotsa-img
Instructions to use anonymous-eval/food-recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonymous-eval/food-recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="anonymous-eval/food-recognition") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anonymous-eval/food-recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 623 Bytes
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"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"disable_grouping": null,
"do_center_crop": null,
"do_convert_rgb": null,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "DINOv3ViTImageProcessorFast",
"image_std": [
0.229,
0.224,
0.225
],
"input_data_format": null,
"pad_size": null,
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"height": 224,
"width": 224
}
}
|