Instructions to use dacanizalesconvers/material-surface-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dacanizalesconvers/material-surface-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dacanizalesconvers/material-surface-classifier") 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("dacanizalesconvers/material-surface-classifier") model = AutoModelForImageClassification.from_pretrained("dacanizalesconvers/material-surface-classifier") - timm
How to use dacanizalesconvers/material-surface-classifier with timm:
import timm model = timm.create_model("hf_hub:dacanizalesconvers/material-surface-classifier", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 899 Bytes
9021a01 | 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 45 46 47 48 49 50 51 52 | {
"architecture": "mobilenetv3_large_100",
"architectures": [
"TimmWrapperForImageClassification"
],
"do_pooling": true,
"dtype": "float32",
"initializer_range": 0.02,
"label_names": [
"asphalt",
"concrete",
"metal",
"other",
"wood"
],
"model_args": null,
"model_type": "timm_wrapper",
"num_classes": 5,
"num_features": 1280,
"pretrained_cfg": {
"classifier": "classifier",
"crop_mode": "center",
"crop_pct": 0.875,
"custom_load": false,
"first_conv": "conv_stem",
"fixed_input_size": false,
"input_size": [
3,
224,
224
],
"interpolation": "bicubic",
"mean": [
0.485,
0.456,
0.406
],
"pool_size": [
7,
7
],
"std": [
0.229,
0.224,
0.225
],
"tag": "ra_in1k"
},
"transformers_version": "5.7.0",
"use_cache": false
}
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