Instructions to use charuka0/acne-multilabel-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use charuka0/acne-multilabel-classifier with timm:
import timm model = timm.create_model("hf_hub:charuka0/acne-multilabel-classifier", pretrained=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,078 Bytes
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"task": "multi-label acne type recognition",
"classes": [
"Whitehead",
"Blackhead",
"Papule",
"Pustule",
"Nodule"
],
"backbone": "efficientnet_b0",
"img_size": 320,
"normalization": {
"mean": [
0.485,
0.456,
0.406
],
"std": [
0.229,
0.224,
0.225
]
},
"thresholds": [
0.9300000000000002,
0.7200000000000002,
0.7500000000000002,
0.5000000000000001,
0.8200000000000002
],
"best_epoch": 20,
"test_metrics": {
"subset_accuracy": 0.9797979797979798,
"label_accuracy": 0.9950937950937951,
"hamming_loss": 0.004906204906204906,
"macro_precision": 0.9920701754385967,
"macro_recall": 0.9859189631845048,
"macro_f1": 0.9889388150515815,
"micro_precision": 0.9912790697674418,
"micro_recall": 0.9841269841269841,
"micro_f1": 0.9876900796524257,
"macro_auc": 0.9992512177176863,
"macro_ap": 0.9978351524589545
},
"n_train": 3231,
"n_val": 693,
"n_test": 693,
"torch_version": "2.10.0+cu128",
"timm_version": "1.0.26"
} |