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--- |
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language: en |
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tags: |
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- image-classification |
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- resnet |
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- custom-model |
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datasets: |
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- RAF-dataset |
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license: mit |
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pipeline_tag: image-classification |
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--- |
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# Custom ResNet-18 for 7-Class Classification |
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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 **RAF-DB DATASET** with various augmentations. |
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--- |
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## 📜 Model Details |
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- **Base Model:** ResNet-18. |
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- **Activations:** ReLU layers replaced with PReLU. |
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- **Dropout:** Dropout2D applied to enhance generalization. |
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- **Classes:** 7 output classes. |
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- **Input Size:** Images with customizable dimensions (default: `[100, 100]`). |
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- **Normalization:** Input images are normalized using the following statistics: |
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- Mean: `[0.485, 0.456, 0.406]` |
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- Std: `[0.229, 0.224, 0.225]` |
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--- |
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## 📈 Evaluation Metrics on Test Data |
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Accuracy: 79.92% |
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Precision: 79.80% |
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Recall: 79.92% |
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F1-Score: 79.80% |
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Classification Report: |
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precision recall f1-score support |
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1 0.79 0.81 0.80 329 |
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2 0.58 0.47 0.52 74 |
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3 0.51 0.42 0.46 160 |
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4 0.92 0.90 0.91 1185 |
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5 0.74 0.78 0.76 478 |
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6 0.68 0.72 0.70 162 |
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7 0.75 0.78 0.77 680 |
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accuracy 0.80 3068 |
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macro avg 0.71 0.70 0.70 3068 |
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weighted avg 0.80 0.80 0.80 3068 |
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## 🧑💻 How to Use |
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You can load the model weights and architecture for inference or fine-tuning with the provided files: |
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### **Using PyTorch** |
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``` |
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def get_out_channels(module): |
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if isinstance(module, nn.Conv2d): |
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return module.out_channels |
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elif isinstance(module, nn.BatchNorm2d): |
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return module.num_features |
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elif isinstance(module, nn.Linear): |
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return module.out_features |
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return None |
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def replace_relu_with_prelu_and_dropout(module, inplace=True): |
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for name, child in module.named_children(): |
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replace_relu_with_prelu_and_dropout(child, inplace) |
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if isinstance(child, nn.ReLU): |
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out_channels = None |
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for prev_name, prev_child in module.named_children(): |
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if prev_name == name: |
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break |
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out_channels = get_out_channels(prev_child) or out_channels |
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if out_channels is None: |
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raise ValueError(f"Cannot determine `out_channels` for {child}. Please check the model structure.") |
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prelu = PReLU(device=device, num_parameters=out_channels) |
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dropout = nn.Dropout2d(p=0.2) |
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setattr(module, name, nn.Sequential(prelu, dropout).to(device)) |
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model = models.resnet18(weights = models.ResNet18_Weights.IMAGENET1K_V1).train(True).to(device) |
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replace_relu_with_prelu_and_dropout(model) |
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# print(model.fc.in_features) |
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number = model.fc.in_features |
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module = [] |
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module.append(LazyLinear(7)) |
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model.fc = Sequential(*module).to(device) |
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state_dict = load_file("model.safetensors") |
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model.load_state_dict(state_dict) |
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model.eval() |
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``` |
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## ⚠️ Limitations and Considerations |
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Input Dimensions: Make sure your input images are resized to the expected dimensions (100x100) before inference. |
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Number of Classes: The trained model supports exactly 7 classes as defined in the training dataset. |
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Output: The model output should be a probability of each of the 7 face type labels. Don't forget to use the softmax function to make predictions. Note that softmax is not used in the last layer of this model's architecture. |
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