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