Enhancing Model Accuracy through Advanced Transfer Learning Technique #12
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-The current implementation utilizes EfficientNet for terrain classification. - Data Agumentation includes resizing, horizontal flipping, color jitter, and random rotations. - I have loaded a pre-trained EfficientNet B0 model and modified the classifier layer to match the number of output classes (5 for different terrain types). - I noticed at high learning rates the accuracy was highly volatile so it was lowered - I defined the loss function (CrossEntropyLoss) and optimizer (Adam) with a lower learning rate for fine-tuning. - Learning rate scheduling : ReduceLROnPlateau - Included code to load model and test it using a example image - The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu
-The current implementation utilizes EfficientNet for terrain classification. Data Agumentation includes resizing, horizontal flipping, color jitter, and random rotations. I have loaded a pre-trained EfficientNet B0 model and modified the classifier layer to match the number of output classes (5 for different terrain types). I noticed at high learning rates the accuracy was highly volatile so it was lowered I defined the loss function (CrossEntropyLoss) and optimizer (Adam) with a lower learning rate for fine-tuning. Learning rate scheduling : ReduceLROnPlateau Included code to load model and test it using a example image The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu - added readme.md - added improvement.md
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Fixed Issue : #8
Darsh Agrawal , GSSoC 2024 extd contributor