Instructions to use Qdrant/resnet50-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/resnet50-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Qdrant/resnet50-onnx") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qdrant/resnet50-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Qdrant/resnet50-onnx: direct link, hf CLI and curl.
- Browser
- Download file 734 Bytes
-
https://huggingface.co/Qdrant/resnet50-onnx/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://Qdrant/resnet50-onnx@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Qdrant/resnet50-onnx/resolve/refs%2Fpr%2F1/preprocessor_config.json
734 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 | |
| } | |
| } |