| --- |
| license: mit |
| tags: |
| - image-classification |
| - multi-label-image-classification |
| - pytorch |
| - timm |
| - dermatology |
| - acne |
| library_name: timm |
| pipeline_tag: image-classification |
| --- |
| |
| # Multi-Label Acne Type Classifier (efficientnet_b0) |
| |
| Predicts which of five acne lesion types are **present** in a full-face photograph. This is a |
| multi-label presence task: it does not count lesions and does not localise them. |
| |
| **Classes:** Whitehead, Blackhead, Papule, Pustule, Nodule |
| |
| ## Intended use and limitations |
| |
| Research and educational use only. **This model is not a medical device and must not be used for |
| diagnosis or treatment decisions.** It was trained on a limited academic dataset and has not been |
| clinically validated. Performance across skin tones, ages, lighting conditions and camera hardware |
| has not been audited and is very likely uneven; treat any output on under-represented skin tones as |
| unreliable. |
| |
| ## Architecture |
| |
| - Backbone: `efficientnet_b0` (timm, ImageNet-pretrained) |
| - Head: single linear layer, 5 logits, sigmoid activation at inference |
| - Input: 320 x 320 RGB, ImageNet normalisation |
| - Loss: BCEWithLogitsLoss with per-class `pos_weight` |
| - Optimiser: AdamW, warmup + cosine decay, mixed precision |
| - Per-class decision thresholds tuned on validation (see `thresholds.json`) |
|
|
| ## Training data |
|
|
| 3,231 train / 693 validation / 693 test images, multi-label |
| stratified. Sources include the public Kaggle dataset `tiswan14/acne-dataset-image` plus |
| project-specific annotations. |
|
|
| ## Test-set results |
|
|
| | Metric | Value | |
| |---|---| |
| | Subset (exact-match) accuracy | 0.9798 | |
| | Label-wise accuracy | 0.9951 | |
| | Macro F1 | 0.9889 | |
| | Micro F1 | 0.9877 | |
| | Macro ROC-AUC | 0.9993 | |
| | Macro Average Precision | 0.9978 | |
|
|
| | Class | Support | Precision | Recall | F1 | ROC-AUC | |
| |---|---|---|---|---|---| |
| | Whitehead | 45 | 1.000 | 1.000 | 1.000 | 1.000 | |
| | Blackhead | 186 | 1.000 | 0.995 | 0.997 | 1.000 | |
| | Papule | 155 | 0.987 | 0.955 | 0.971 | 0.998 | |
| | Pustule | 151 | 0.974 | 0.980 | 0.977 | 0.998 | |
| | Nodule | 156 | 1.000 | 1.000 | 1.000 | 1.000 | |
|
|
| ## Usage |
|
|
| ```python |
| import torch, timm, numpy as np, cv2 |
| from huggingface_hub import hf_hub_download |
| |
| ckpt_path = hf_hub_download("charuka0/acne-multilabel-classifier", "best_model.pth") |
| ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) |
| |
| model = timm.create_model(ckpt["backbone"], pretrained=False, num_classes=5) |
| model.load_state_dict(ckpt["model_state_dict"]); model.eval() |
| |
| img = cv2.cvtColor(cv2.imread("face.jpg"), cv2.COLOR_BGR2RGB) |
| img = cv2.resize(img, (ckpt["img_size"], ckpt["img_size"])) / 255.0 |
| img = (img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225] |
| x = torch.tensor(img).permute(2, 0, 1).unsqueeze(0).float() |
| |
| probs = torch.sigmoid(model(x))[0].detach().numpy() |
| present = probs >= np.array(ckpt["thresholds"]) |
| print(dict(zip(ckpt["classes"], zip(probs.round(3), present)))) |
| ``` |
|
|
| ## Citation |
|
|
| Final Year Research Project, 2026. Trained on Kaggle free-tier GPU. |
|
|
