Instructions to use Francesco/resnet152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet152 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet152") 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("Francesco/resnet152") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet152", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Francesco/resnet152: direct link, hf CLI and curl.
- Browser
- Download file 266 Bytes
-
https://huggingface.co/Francesco/resnet152/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://Francesco/resnet152@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Francesco/resnet152/resolve/refs%2Fpr%2F1/preprocessor_config.json
266 Bytes
| { | |
| "crop_pct": 0.875, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ConvNextFeatureExtractor", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 3, | |
| "size": 224 | |
| } | |