Image Classification
Transformers
Safetensors
custom_resnet
feature-extraction
resnet
svhn
custom_code
Instructions to use Dawntasy/SVHN-V1-ResNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dawntasy/SVHN-V1-ResNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dawntasy/SVHN-V1-ResNet", 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 AutoModel model = AutoModel.from_pretrained("Dawntasy/SVHN-V1-ResNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Dawntasy/SVHN-V1-ResNet: direct link, hf CLI and curl.
- Browser
- Download file 269 Bytes
-
https://huggingface.co/Dawntasy/SVHN-V1-ResNet/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Dawntasy/SVHN-V1-ResNet/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Dawntasy/SVHN-V1-ResNet/resolve/main/preprocessor_config.json
269 Bytes
| { | |
| "image_processor_type": "BitImageProcessor", | |
| "do_resize": true, | |
| "resample": 2, | |
| "size": { | |
| "height": 64, | |
| "width": 64 | |
| }, | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "image_mean": [0.4377, 0.4438, 0.4728], | |
| "image_std": [0.198, 0.201, 0.197] | |
| } |