Image Classification
Transformers
TensorBoard
Safetensors
deit
Generated from Trainer
Eval Results (legacy)
Instructions to use BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold1") 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("BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold1") model = AutoModelForImageClassification.from_pretrained("BilalMuftuoglu/deit-base-distilled-patch16-224-55-fold1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 714 Bytes
f362d84 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"return_tensors",
"data_format",
"input_data_format"
],
"crop_size": {
"height": 224,
"width": 224
},
"do_center_crop": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "DeiTImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
}
}
|