Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-nd-4.0
+license: apache-2.0
 language:
 - en
 base_model:
@@ -24,6 +24,49 @@ This model is fine-tuned for **NSFW image classification**. It classifies conten
 
 ---
 
+## 🚀 Usage Example
+
+```python
+import torch
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch.nn.functional as F
+
+model_path = "Ateeqq/nsfw-image-detection"
+processor = AutoImageProcessor.from_pretrained(model_path)
+model = SiglipForImageClassification.from_pretrained(model_path)
+
+image_path = r"/content/download.jpg"
+image = Image.open(image_path).convert("RGB")
+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:.6f}")
+```
+
+## Output
+```
+Predicted class ID: 2
+Predicted class label: safe_normal
+
+Confidence for 'gore_bloodshed_violent': 0.000002
+Confidence for 'nudity_pornography': 0.000005
+Confidence for 'safe_normal': 0.999993
+```
+
+---
+
 ## 🧠 Model Details
 
 * **Base model**: `google/siglip2-base-patch16-224`
@@ -58,35 +101,6 @@ id2label = {0: 'gore_bloodshed_violent', 1: 'nudity_pornography', 2: 'safe_norma
 ```
 ---
 
-## 🚀 Usage Example
-
-```python
-import torch
-from transformers import AutoImageProcessor, SiglipForImageClassification
-from PIL import Image
-import torch.nn.functional as F
-
-model_path = "Ateeqq/nsfw-image-detection"
-processor = AutoImageProcessor.from_pretrained(model_path)
-model = SiglipForImageClassification.from_pretrained(model_path)
-
-image = Image.open("your_image_path.jpg").convert("RGB")
-inputs = processor(images=image, return_tensors="pt")
-
-with torch.no_grad():
-    logits = model(**inputs).logits
-    probs = F.softmax(logits, dim=1)
-
-predicted_class_id = logits.argmax().item()
-predicted_class = model.config.id2label[predicted_class_id]
-
-print(f"Predicted class: {predicted_class}")
-for i, score in enumerate(probs[0]):
-    print(f"{model.config.id2label[i]}: {score:.8f}")
-```
-
----
-
 ## 📊 Training Metrics (Epoch 5 Selected ✅)
 
 | Epoch | Training Loss | Validation Loss | Accuracy   |