Instructions to use mohitsha/resnet-tiny-beans-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/resnet-tiny-beans-quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mohitsha/resnet-tiny-beans-quantized") 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("mohitsha/resnet-tiny-beans-quantized") model = AutoModelForImageClassification.from_pretrained("mohitsha/resnet-tiny-beans-quantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from mohitsha/resnet-tiny-beans-quantized: direct link, hf CLI and curl.
- Browser
- Download file 404 Bytes
-
https://huggingface.co/mohitsha/resnet-tiny-beans-quantized/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://mohitsha/resnet-tiny-beans-quantized/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/mohitsha/resnet-tiny-beans-quantized/resolve/main/preprocessor_config.json
404 Bytes
| { | |
| "crop_pct": 0.875, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ConvNextFeatureExtractor", | |
| "image_mean": [ | |
| 0.45, | |
| 0.45, | |
| 0.45 | |
| ], | |
| "image_processor_type": "ConvNextFeatureExtractor", | |
| "image_std": [ | |
| 0.22, | |
| 0.22, | |
| 0.22 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 224 | |
| } | |
| } | |