Instructions to use hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, MobileViTV2ForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation") model = MobileViTV2ForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation: direct link, hf CLI and curl.
- Browser
- Download file 313 Bytes
-
https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation/resolve/refs%2Fpr%2F2/preprocessor_config.json
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation@refs/pr/2/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForSemanticSegmentation/resolve/refs%2Fpr%2F2/preprocessor_config.json
313 Bytes
| { | |
| "crop_size": { | |
| "height": 64, | |
| "width": 64 | |
| }, | |
| "do_center_crop": true, | |
| "do_flip_channel_order": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_processor_type": "MobileViTImageProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 64 | |
| } | |
| } | |