Instructions to use aaa12963337/msi-swinv2-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaa12963337/msi-swinv2-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aaa12963337/msi-swinv2-tiny") 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("aaa12963337/msi-swinv2-tiny") model = AutoModelForImageClassification.from_pretrained("aaa12963337/msi-swinv2-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from aaa12963337/msi-swinv2-tiny: direct link, hf CLI and curl.
- Browser
- Download file 366 Bytes
-
https://huggingface.co/aaa12963337/msi-swinv2-tiny/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://aaa12963337/msi-swinv2-tiny/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/aaa12963337/msi-swinv2-tiny/resolve/main/preprocessor_config.json
366 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.69261899, | |
| 0.51298305, | |
| 0.7263637 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.13099993, | |
| 0.18332116, | |
| 0.14441279 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 256, | |
| "width": 256 | |
| } | |
| } | |