Update README.md

README.md

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----
-license: cc-by-nd-4.0
----
+---
+license: cc-by-nd-4.0
+language:
+- en
+base_model:
+- google/siglip2-base-patch16-224
+pipeline_tag: image-classification
+library_name: transformers
+tags:
+- nswf
+- exnrt.com
+---
+
+# Nsfw Image Detection
+
+This model is a fine-tuned for **nsfw image classification**. It has been trained to classify images into three safety-related categories, making it suitable for content moderation, filtering, or safety-aware applications.
+
+<p>
+  <a href="https://exnrt.com/blog/ai/fine-tuning-siglip2/" target="_blank">
+    <img src="https://img.shields.io/badge/View%20Training%20Code-blue?style=for-the-badge&logo=readthedocs"/>
+  </a>
+</p>
+
+## 🧠 Model Details
+
+* **Base model**: `google/siglip2-base-patch16-224`
+* **Task**: Image Classification (Safety Filtering)
+* **Framework**: PyTorch
+* **Fine-tuned on**: Custom dataset with 3 safety-related categories
+* **Selected checkpoint**: Epoch 3
+* **Batch size**: 64
+* **Epochs**: 7
+
+### 🏷️ Categories
+
+The model classifies images into the following categories:
+
+| ID | Label                 |
+| -- | --------------------- |
+| 0  | `graphically_violent` |
+| 1  | `nudity_pornography`  |
+| 2  | `safe_normal`         |
+
+### 🧾 Label Mapping
+
+```python
+label2id = {'graphically_violent': 0, 'nudity_pornography': 1, 'safe_normal': 2}
+id2label = {0: 'graphically_violent', 1: 'nudity_pornography', 2: 'safe_normal'}
+```
+
+
+## 📈 Visual Results
+
+### 📌 Epoch Training Results
+
+![Epoch Results](https://huggingface.co/Ateeqq/nsfw-image-detection/resolve/main/nsfw-epochs-results.png)
+
+### 📌 Final Metrics & Confusion Matrix
+
+![Metrics](https://huggingface.co/Ateeqq/nsfw-image-detection/resolve/main/nsfw-training-results.png)
+
+---
+
+## 🚀 Usage
+
+```python
+import torch
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch.nn.functional as F
+
+model_path = "Ateeqq/siglip2-safety-classifier-gpu"
+processor = AutoImageProcessor.from_pretrained(model_path)
+model = SiglipForImageClassification.from_pretrained(model_path)
+
+image = Image.open("your_image_path.jpg")
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+probabilities = F.softmax(logits, dim=1)
+
+predicted_class_id = logits.argmax().item()
+predicted_class_label = model.config.id2label[predicted_class_id]
+
+confidence_scores = probabilities[0].tolist()
+
+print(f"Predicted class ID: {predicted_class_id}")
+print(f"Predicted class label: {predicted_class_label}\n")
+
+for i, score in enumerate(confidence_scores):
+    label = model.config.id2label[i]
+    print(f"Confidence for '{label}': {score:.4f}")
+```
+
+### Output
+
+```
+Predicted class ID: 0
+Predicted class label: graphically_violent
+
+Confidence for 'graphically_violent': 0.9941
+Confidence for 'nudity_pornography': 0.0040
+Confidence for 'safe_normal': 0.0019
+```
+
+---
+
+## 📊 Training Metrics (Epoch 3 Selected ✅)
+
+| Epoch | Training Loss | Validation Loss | Accuracy   |
+| ----- | ------------- | --------------- | ---------- |
+| 1     | 0.1086        | 0.0817          | 97.05%     |
+| 2     | 0.0415        | 0.1233          | 95.50%     |
+| 3 ✅   | 0.0302        | 0.0516          | **98.45%** |
+| 4     | 0.0271        | 0.0799          | 97.89%     |
+| 5     | 0.0222        | 0.1015          | 98.03%     |
+| 6     | 0.0026        | 0.0707          | 98.45%     |
+| 7     | 0.0178        | 0.0665          | 98.59%     |