Instructions to use hf-tiny-model-private/tiny-random-EfficientNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-EfficientNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-EfficientNetModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-EfficientNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-EfficientNetModel") - Notebooks
- Google Colab
- Kaggle
File size: 469 Bytes
6dca878 | 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 | {
"crop_size": {
"height": 600,
"width": 600
},
"do_center_crop": false,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "EfficientNetImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"include_top": true,
"resample": 0,
"rescale_factor": 0.00392156862745098,
"rescale_offset": false,
"size": {
"height": 600,
"width": 600
}
}
|