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✅ : You have successfully created a 512-dimensional deep feature vector using a pre-trained ResNet18 backbone, which represents high-level semantic information from your image.

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

Deep Learning-Oriented Feature Extraction for Biological Sequences ✅ : You have successfully created a 512-dimensional