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license: mit
metrics:
- accuracy
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# 🖼️ Image Multi-Label Safety Classifier
**Repo:** `abhi099k/image-multi-detect`
**Framework:** PyTorch + ONNX
**Task:** Multi-label image content classification
**Author:** Abhinav
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## 🚀 Overview
This model is a **professional multi-label image classifier** trained to detect multiple safety-related categories simultaneously.
It is optimized for:
- NSFW / adult content detection
- Violence
- Weapons
- Substance categories (smoking, alcohol, drugs)
- Sensitive content
- Hate content
The model supports **8 independent labels**, using **sigmoid (multi-label)** rather than softmax.
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## 🧠 Labels
| Index | Label | Meaning |
|-------|------------|---------|
| 0 | `nsfw` | Nude/sexual content |
| 1 | `violence` | Physical harm, fighting, blood |
| 2 | `weapon` | Guns, knives, explosives |
| 3 | `smoking` | Cigarettes, vaping, smoking activity |
| 4 | `alcohol` | Alcoholic drinks or consumption |
| 5 | `drugs` | Illegal drugs, pills, paraphernalia |
| 6 | `sensitive` | Sensitive contexts (medical, blood, etc.) |
| 7 | `hate` | Hateful symbols, extremist logos |
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## 📦 Files in Repository
| File | Description |
|------|-------------|
| `best.pth` | PyTorch model weights |
| `model.onnx` | ONNX-exported model (recommended for inference) |
| `metrics_test.json` | Evaluation results |
| `history.json` | Training logs |
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## 🔧 Technical Details
### Architecture
- **Backbone:** ResNet-50
- **Head:** Fully connected layer → 8 logits
- **Loss:** `BCEWithLogitsLoss`
- **Optimizer:** AdamW
- **Mixed precision:** Yes
- **Balanced sampling:** WeightedRandomSampler
### Image Size
`224 × 224`
### Training Transformations
- Resize
- Random crop
- Horizontal flip
- Color jitter
- Normalization
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## 📈 Performance
Macro-averaged metrics on test set: |