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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()
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