: If one model is insufficient, you can concatenate feature vectors from multiple architectures (e.g., ResNet + EfficientNet) into a single array for more discriminatory power. 4. Saving and Validation
: Apply mean and standard deviation normalization based on the ImageNet dataset (if using pre-trained weights) to ensure consistent feature scaling.
is the feature vector size (e.g., 1792 for EfficientNet-B4). Ekipa Sara grebenom.zip
: To improve robustness, apply random rotations, flips, or cropping during the training phase. 3. Feature Extraction Workflow
: Better for capturing complex, fine-grained details in visually similar images. : If one model is insufficient, you can
: Load the model in evaluation mode and pass the images through. Extract the flattened vector from the global average pooling layer (the layer just before the final classification head).
: If the dataset is specialized, fine-tune only the last few convolutional blocks while keeping the initial layers frozen. is the feature vector size (e
: Remove any corrupted files or outliers that do not belong to the "Ekipa Sara grebenom" topic. 2. Pre-processing