Create README.md

README.md

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+---
+license: mit
+tags:
+- vision
+- image-classification
+- pytorch
+- efficientnet
+datasets:
+- Shad0wKillar/pizza_steak_sushi
+metrics:
+- accuracy
+---
+# EfficientNet-B7 Pizza/Steak/Sushi Classifier
+
+I fine-tuned a pre-trained EfficientNet-B7 model to classify images into three categories: pizza, steak, and sushi. 
+
+## Model Details
+* **Architecture:** `torchvision.models.efficientnet_b7`
+* **Weights:** `EfficientNet_B7_Weights.DEFAULT`
+* **Modifications:** I froze all the base feature layers.
+
+## Training Procedure
+I trained the model for 10 epochs using the Adam optimizer.
+
+* **Batch Size:** 32
+* **Learning Rate:** 0.001
+* **Loss Function:** CrossEntropyLoss
+* **Transforms:** I used the automatic transforms provided by the default EfficientNet-B7 weights. 
+* **Hardware:** Trained using `cuda` (if available) with a set manual seed of 37 for reproducibility.
+
+## Dataset
+I used a 20% subset of a pizza, steak, and sushi dataset. The data was split into `train` and `test` directories. 
+
+## Evaluation Results
+
+### Accuracy and Loss Curves
+Over the 10 epochs, the training and testing loss steadily decreased, with the testing loss ending near a highly impressive 0.15. The testing accuracy consistently outperformed the training accuracy throughout, finishing near a perfect 99%.
+
+![Loss and Accuracy Curves](EfficientNet_B7_20percent.pth_curves.png)
+
+### Confusion Matrix
+This model achieved the best performance across all three classes on the test set, making almost no errors:
+* **Pizza:** 46 correct, 0 misclassified.
+* **Steak:** 57 correct, 0 misclassified as pizza, 1 misclassified as sushi.
+* **Sushi:** 46 correct, 0 misclassified.
+
+![Confusion Matrix](EfficientNet_B7_20percent.pth_confusion_matrix.png)
+
+### Most Confident Wrong Predictions
+I plotted the single instance where the model was incorrect. The model only made one mistake in the entire test set, predicting a steak dish as sushi with a relatively low confidence of 0.56.
+
+![Wrong Predictions](EfficientNet_B7_20percent.pth_wrong_pred.png)
+
+## How to use
+```python
+import torch
+import torchvision
+
+# I loaded the model architecture
+weights = torchvision.models.EfficientNet_B7_Weights.DEFAULT
+model = torchvision.models.efficientnet_b7(weights=weights)
+
+# I modified the classifier
+model.classifier = torch.nn.Sequential(
+    torch.nn.Dropout(p=0.2, inplace=True),
+    torch.nn.Linear(in_features=2560, out_features=3, bias=True),
+)
+
+# I loaded the saved weights
+model.load_state_dict(torch.load("EfficientNet_B7_20percent.pth", map_location="cpu"))
+model.eval()