Create README.md

README.md

@@ -0,0 +1,79 @@
+# Custom ResNet-18 for 7-Class Classification
+
+This is a fine-tuned **`ResNet-18`** model designed for a 7-class classification task. The model replaces all **ReLU** activation functions with **PReLU**, introduces **Dropout2D** layers for better generalization, and was trained on a custom dataset with various augmentations.
+
+---
+
+## 📜 Model Details
+
+- **Base Model:** ResNet-18 (pre-trained on ImageNet).
+- **Activations:** ReLU layers replaced with PReLU.
+- **Dropout:** Dropout2D applied to enhance generalization.
+- **Classes:** 7 output classes.
+- **Input Size:** Images with customizable dimensions (default: `[100, 100]`).
+- **Normalization:** Input images are normalized using the following statistics:
+  - Mean: `[0.485, 0.456, 0.406]`
+  - Std: `[0.229, 0.224, 0.225]`
+
+---
+
+## 📈 Evaluation Metrics on Test Data 
+![Confusion Matrix](Test Accuracy 80.31%.png)
+
+
+## 🧑‍💻 How to Use
+
+You can load the model weights and architecture for inference or fine-tuning with the provided files:
+
+### **Using PyTorch**
+```
+
+def get_out_channels(module):
+    """تابعی برای یافتن تعداد کانال‌های خروجی از لایه‌های کانولوشن و BatchNorm"""
+    if isinstance(module, nn.Conv2d):
+        return module.out_channels
+    elif isinstance(module, nn.BatchNorm2d):
+        return module.num_features
+    elif isinstance(module, nn.Linear):
+        return module.out_features
+    return None
+
+def replace_relu_with_prelu_and_dropout(module, inplace=True):
+    for name, child in module.named_children():
+        replace_relu_with_prelu_and_dropout(child, inplace)
+        
+        if isinstance(child, nn.ReLU): 
+            out_channels = None
+            for prev_name, prev_child in module.named_children():
+                if prev_name == name:
+                    break
+                out_channels = get_out_channels(prev_child) or out_channels
+            
+            if out_channels is None:
+                raise ValueError(f"Cannot determine `out_channels` for {child}. Please check the model structure.")
+            
+            prelu = PReLU(device=device, num_parameters=out_channels) 
+            dropout = nn.Dropout2d(p=0.2) 
+            setattr(module, name, nn.Sequential(prelu, dropout).to(device))
+model = models.resnet18(weights = models.ResNet18_Weights.IMAGENET1K_V1).train(True).to(device)
+replace_relu_with_prelu_and_dropout(model)
+# print(model.fc.in_features)
+
+
+number = model.fc.in_features
+module  = []
+
+module.append(LazyLinear(7))
+model.fc = Sequential(*module).to(device)
+
+state_dict = load_file("model.safetensors")
+model.load_state_dict(state_dict)
+model.eval()
+```
+
+## ⚠️ Limitations and Considerations
+Input Dimensions: Make sure your input images are resized to the expected dimensions (100x100) before inference.
+Number of Classes: The trained model supports exactly 7 classes as defined in the training dataset.
+
+
+