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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- nswf |
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- exnrt.com |
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--- |
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# NSFW Image Detection – A Top Performer |
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This model is fine-tuned for **NSFW image classification**. It classifies content into three safety-critical categories, making it useful for moderation, safety filtering, and compliant content handling systems. |
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<p> |
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<a href="https://exnrt.com/blog/ai/fine-tuning-siglip2/" target="_blank"> |
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<img src="https://img.shields.io/badge/View%20Training%20Code-blue?style=for-the-badge&logo=readthedocs"/> |
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</a> |
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<a href="https://exnrt.com/blog/ai/fine-tuning-siglip2/" target="_blank">https://exnrt.com/blog/ai/fine-tuning-siglip2/</a> |
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</p> |
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--- |
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## 🚀 Usage Example |
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```python |
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import torch |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch.nn.functional as F |
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model_path = "Ateeqq/nsfw-image-detection" |
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processor = AutoImageProcessor.from_pretrained(model_path) |
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model = SiglipForImageClassification.from_pretrained(model_path) |
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image_path = r"/content/download.jpg" |
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image = Image.open(image_path).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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probabilities = F.softmax(logits, dim=1) |
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predicted_class_id = logits.argmax().item() |
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predicted_class_label = model.config.id2label[predicted_class_id] |
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confidence_scores = probabilities[0].tolist() |
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print(f"Predicted class ID: {predicted_class_id}") |
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print(f"Predicted class label: {predicted_class_label}\n") |
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for i, score in enumerate(confidence_scores): |
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label = model.config.id2label[i] |
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print(f"Confidence for '{label}': {score:.6f}") |
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``` |
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## Output |
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``` |
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Predicted class ID: 2 |
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Predicted class label: safe_normal |
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Confidence for 'gore_bloodshed_violent': 0.000002 |
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Confidence for 'nudity_pornography': 0.000005 |
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Confidence for 'safe_normal': 0.999993 |
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``` |
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--- |
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## 🧠 Model Details |
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* **Base model**: `google/siglip2-base-patch16-224` |
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* **Task**: Image Classification (NSFW/Safe detection) |
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* **Framework**: PyTorch / Hugging Face Transformers |
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* **Fine-tuned on**: Custom dataset with 3 content categories |
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* **Selected checkpoint**: Epoch 5 |
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* **Batch size**: 64 |
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* **Epochs trained**: 5 |
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--- |
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### 📌 Confusion Matrix |
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--- |
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### 🏷️ Categories |
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| ID | Label |Excluded| |
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| -- | ---------------------------|---------------| |
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| 0 | ✅`gore_bloodshed_violent` |❌ Fight, Accident, Angry| |
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| 1 | ✅`nudity_pornography` |❌ Normal Romance, Normal Kissing| |
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| 2 | ✅`safe_normal` |❌ | |
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### 🧾 Label Mapping |
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```python |
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label2id = {'gore_bloodshed_violent': 0, 'nudity_pornography': 1, 'safe_normal': 2} |
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id2label = {0: 'gore_bloodshed_violent', 1: 'nudity_pornography', 2: 'safe_normal'} |
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``` |
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--- |
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## 📊 Training Metrics (Epoch 5 Selected ✅) |
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| Epoch | Training Loss | Validation Loss | Accuracy | |
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| ----- | ------------- | --------------- | ---------- | |
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| 1 | 0.0765 | 0.1166 | 95.70% | |
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| 2 | 0.0719 | 0.0477 | 98.34% | |
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| 3 | 0.0089 | 0.0634 | 98.05% | |
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| 4 | 0.0109 | 0.0437 | 98.61% | |
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| 5 ✅ | 0.0001 | 0.0389 | **99.02%** | |
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### 📌 Epoch Training Results |
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- **Training runtime**: 1h 21m 40s |
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- **Final Training Loss**: 0.0727 |
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- **Steps/sec**: 0.11 | **Samples/sec**: 6.99 |
