Shad0wKillar
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efficientnet-b1

Image Classification

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Shad0wKillar commited on 28 days ago

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@@ -36,7 +36,7 @@ I used a 20% subset of a pizza, steak, and sushi dataset. The data was split int
 ### Accuracy and Loss Curves
 Over the 10 epochs, both the training and testing loss steadily decreased, with the testing loss ending below 0.40. The testing accuracy outperformed the training accuracy early on and finished highly stable above 90%.
-![Loss and Accuracy Curves](plots/EfficientNet_B1_20percent.pth_curves.png)
 ### Confusion Matrix
 The model performs exceptionally well across all three classes on the test set:
@@ -44,12 +44,12 @@ The model performs exceptionally well across all three classes on the test set:
 * **Steak:** 56 correct, 0 misclassified as pizza, 2 misclassified as sushi.
 * **Sushi:** 42 correct, 3 misclassified as pizza, 1 misclassified as steak.
-![Confusion Matrix](plots/EfficientNet_B1_20percent.pth_confusion_matrix.png)
 ### Most Confident Wrong Predictions
 I plotted the instances where the model was highly confident but incorrect. The model occasionally struggled with distinguishing close-up textures, such as predicting a steak dish as sushi with 0.82 confidence, or a sushi dish as pizza with 0.61 confidence.
-![Wrong Predictions](plots/EfficientNet_B1_20percent.pth_wrong_pred.png)
 ## How to use
 ```python

 ### Accuracy and Loss Curves
 Over the 10 epochs, both the training and testing loss steadily decreased, with the testing loss ending below 0.40. The testing accuracy outperformed the training accuracy early on and finished highly stable above 90%.
+![Loss and Accuracy Curves](EfficientNet_B1_20percent.pth_curves.png)
 ### Confusion Matrix
 The model performs exceptionally well across all three classes on the test set:
 * **Steak:** 56 correct, 0 misclassified as pizza, 2 misclassified as sushi.
 * **Sushi:** 42 correct, 3 misclassified as pizza, 1 misclassified as steak.
+![Confusion Matrix](EfficientNet_B1_20percent.pth_confusion_matrix.png)
 ### Most Confident Wrong Predictions
 I plotted the instances where the model was highly confident but incorrect. The model occasionally struggled with distinguishing close-up textures, such as predicting a steak dish as sushi with 0.82 confidence, or a sushi dish as pizza with 0.61 confidence.
+![Wrong Predictions](EfficientNet_B1_20percent.pth_wrong_pred.png)
 ## How to use
 ```python