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
Add config.json
Browse files- config.json +50 -0
config.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"task": "multi-label acne type recognition",
|
| 3 |
+
"classes": [
|
| 4 |
+
"Whitehead",
|
| 5 |
+
"Blackhead",
|
| 6 |
+
"Papule",
|
| 7 |
+
"Pustule",
|
| 8 |
+
"Nodule"
|
| 9 |
+
],
|
| 10 |
+
"backbone": "efficientnet_b0",
|
| 11 |
+
"img_size": 320,
|
| 12 |
+
"normalization": {
|
| 13 |
+
"mean": [
|
| 14 |
+
0.485,
|
| 15 |
+
0.456,
|
| 16 |
+
0.406
|
| 17 |
+
],
|
| 18 |
+
"std": [
|
| 19 |
+
0.229,
|
| 20 |
+
0.224,
|
| 21 |
+
0.225
|
| 22 |
+
]
|
| 23 |
+
},
|
| 24 |
+
"thresholds": [
|
| 25 |
+
0.9300000000000002,
|
| 26 |
+
0.7200000000000002,
|
| 27 |
+
0.7500000000000002,
|
| 28 |
+
0.5000000000000001,
|
| 29 |
+
0.8200000000000002
|
| 30 |
+
],
|
| 31 |
+
"best_epoch": 20,
|
| 32 |
+
"test_metrics": {
|
| 33 |
+
"subset_accuracy": 0.9797979797979798,
|
| 34 |
+
"label_accuracy": 0.9950937950937951,
|
| 35 |
+
"hamming_loss": 0.004906204906204906,
|
| 36 |
+
"macro_precision": 0.9920701754385967,
|
| 37 |
+
"macro_recall": 0.9859189631845048,
|
| 38 |
+
"macro_f1": 0.9889388150515815,
|
| 39 |
+
"micro_precision": 0.9912790697674418,
|
| 40 |
+
"micro_recall": 0.9841269841269841,
|
| 41 |
+
"micro_f1": 0.9876900796524257,
|
| 42 |
+
"macro_auc": 0.9992512177176863,
|
| 43 |
+
"macro_ap": 0.9978351524589545
|
| 44 |
+
},
|
| 45 |
+
"n_train": 3231,
|
| 46 |
+
"n_val": 693,
|
| 47 |
+
"n_test": 693,
|
| 48 |
+
"torch_version": "2.10.0+cu128",
|
| 49 |
+
"timm_version": "1.0.26"
|
| 50 |
+
}
|