Chvp02.rar -

Pass the image through the network to obtain the feature vector.

with torch.no_grad(): deep_feature = feature_extractor(input_batch) # Flatten the output to a 1D vector (e.g., size 512 for ResNet18) deep_feature_vector = torch.flatten(deep_feature, 1) print(f"Deep Feature Vector Shape: {deep_feature_vector.shape}") Use code with caution. Copied to clipboard These vectors can now be used for downstream tasks like: CHVP02.rar

: Using cosine similarity to find similar images in the dataset. Pass the image through the network to obtain

Extract your images from the .rar file and apply the transformations required by the pre-trained model (usually resizing to 224x224 and normalizing with ImageNet stats). CHVP02.rar

: Feeding them into a simpler classifier like an SVM or KNN.

: Visualizing the features using t-SNE or PCA.