Image Classification
Transformers
Safetensors
PyTorch
efficientnet_b3
feature-extraction
vision
efficientnet
mushroom
musheff
custom_code
Instructions to use blasisd/musheff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blasisd/musheff with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="blasisd/musheff", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("blasisd/musheff", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 495 Bytes
6061356 15f1d76 6061356 15f1d76 6061356 15f1d76 6061356 | 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": 289,
"width": 289
},
"do_center_crop": false,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "EfficientNetImageProcessor",
"image_std": [
0.47853944,
0.4732864,
0.47434163
],
"include_top": true,
"resample": 0,
"rescale_factor": 0.00392156862745098,
"rescale_offset": false,
"size": {
"height": 300,
"width": 300
}
}
|