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alexnet_places365.pth_mlx.npz

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+size 117002764

resnet50_places365.pth_mlx.npz

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+size 48691562

train_dream.py

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+# TODO: Implement Fine-Tuning Logic
+
+"""
+DeepDream Training / Fine-Tuning Script (Placeholder)
+
+Goal:
+Allow users to fine-tune these base models (VGG, GoogLeNet, etc.) on their own datasets
+to create custom Dream styles.
+
+Steps to Implement:
+1.  Load Dataset: Use `torchvision.datasets.ImageFolder` or custom loader for user images.
+2.  Load Model: Use our MLX models (need to add `train()` mode with dropout/grad support if missing,
+    or simpler: use PyTorch for training -> export to MLX).
+    *Easier path:* Train in PyTorch using standard scripts, then use `export_*.py` to bring it here.
+3.  Training Loop: Standard classification training or style transfer fine-tuning.
+4.  Export: Save the fine-tuned weights to `.pth`, then run export script.
+
+Usage:
+    python train_dream.py --data /path/to/images --epochs 10 --model vgg16
+"""
+
+import argparse
+
+def main():
+    print("--- DeepDream-MLX Training Stub ---")
+    print("Feature coming soon.")
+    print("Current Workflow: Train in PyTorch -> Use export_*.py -> Dream in MLX")
+
+if __name__ == "__main__":
+    main()