Instructions to use hafizurUMaine/cifar10_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hafizurUMaine/cifar10_m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hafizurUMaine/cifar10_m") 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("hafizurUMaine/cifar10_m") model = AutoModelForImageClassification.from_pretrained("hafizurUMaine/cifar10_m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hafizurUMaine/cifar10_m: direct link, hf CLI and curl.
- Browser
- Download file 344 Bytes
-
https://huggingface.co/hafizurUMaine/cifar10_m/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://hafizurUMaine/cifar10_m/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hafizurUMaine/cifar10_m/resolve/main/preprocessor_config.json
344 Bytes
| { | |
| "crop_size": { | |
| "height": 256, | |
| "width": 256 | |
| }, | |
| "do_center_crop": true, | |
| "do_flip_channel_order": true, | |
| "do_flip_channels": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "MobileViTImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 288 | |
| } | |
| } | |