Instructions to use VinayHajare/quickdraw-mobilevit-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VinayHajare/quickdraw-mobilevit-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="VinayHajare/quickdraw-mobilevit-small") 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("VinayHajare/quickdraw-mobilevit-small") model = AutoModelForImageClassification.from_pretrained("VinayHajare/quickdraw-mobilevit-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from VinayHajare/quickdraw-mobilevit-small: direct link, hf CLI and curl.
- Browser
- Download file 370 Bytes
-
https://huggingface.co/VinayHajare/quickdraw-mobilevit-small/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://VinayHajare/quickdraw-mobilevit-small/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/VinayHajare/quickdraw-mobilevit-small/resolve/main/preprocessor_config.json
370 Bytes
| { | |
| "crop_size": { | |
| "height": 28, | |
| "width": 28 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": false, | |
| "do_flip_channel_order": false, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "MobileViTImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 28 | |
| } | |
| } | |