Instructions to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-MobileViTV2ForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 313 Bytes
25752b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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
}
}
|