Instructions to use kd13/Modern-SqueezeNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kd13/Modern-SqueezeNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kd13/Modern-SqueezeNet", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("kd13/Modern-SqueezeNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kd13/Modern-SqueezeNet: direct link, hf CLI and curl.
- Browser
- Download file 381 Bytes
-
https://huggingface.co/kd13/Modern-SqueezeNet/resolve/47f4ddbcaaf8a8fcd1cbd86909f4e6f97d45de6d/preprocessor_config.json
- Command line
-
hf download hf://kd13/Modern-SqueezeNet@47f4ddbcaaf8a8fcd1cbd86909f4e6f97d45de6d/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kd13/Modern-SqueezeNet/resolve/47f4ddbcaaf8a8fcd1cbd86909f4e6f97d45de6d/preprocessor_config.json
381 Bytes
| { | |
| "crop_pct": 1.0, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.4802, | |
| 0.4481, | |
| 0.3975 | |
| ], | |
| "image_processor_type": "ConvNextImageProcessor", | |
| "image_std": [ | |
| 0.2302, | |
| 0.2265, | |
| 0.2262 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 64 | |
| } | |
| } | |