Instructions to use AndrewOgn/resnet_onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndrewOgn/resnet_onnx with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AndrewOgn/resnet_onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from AndrewOgn/resnet_onnx: direct link, hf CLI and curl.
- Browser
- Download file 596 Bytes
-
https://huggingface.co/AndrewOgn/resnet_onnx/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://AndrewOgn/resnet_onnx/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AndrewOgn/resnet_onnx/resolve/main/preprocessor_config.json
596 Bytes
| {"_processor_class": null, "do_resize": true, "size": {"shortest_edge": 224}, "crop_pct": 0.875, "resample": 3, "do_rescale": true, "rescale_factor": 0.00392156862745098, "do_normalize": true, "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225], "_valid_processor_keys": ["images", "do_resize", "size", "crop_pct", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "return_tensors", "data_format", "input_data_format"], "image_processor_type": "ConvNextFeatureExtractor", "do_center_crop": true, "crop_size": {"height": 224, "width": 224}} |