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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.
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