Instructions to use JoshuaKelleyDs/quickdraw-MobileVITV2-2.0-Finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoshuaKelleyDs/quickdraw-MobileVITV2-2.0-Finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JoshuaKelleyDs/quickdraw-MobileVITV2-2.0-Finetune") 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("JoshuaKelleyDs/quickdraw-MobileVITV2-2.0-Finetune") model = AutoModelForImageClassification.from_pretrained("JoshuaKelleyDs/quickdraw-MobileVITV2-2.0-Finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 632 Bytes
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"_valid_processor_keys": [
"images",
"segmentation_maps",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_center_crop",
"crop_size",
"do_flip_channel_order",
"return_tensors",
"data_format",
"input_data_format"
],
"crop_size": {
"height": 28,
"width": 28
},
"do_center_crop": true,
"do_convert_rgb": false,
"do_flip_channel_order": false,
"do_rescale": true,
"do_resize": true,
"image_processor_type": "MobileViTImageProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 28
}
}
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