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First, ensure the uniformity of your dataset. Dataset "food" likely contains many different images, different kinds of food, etc. If that were the case, I would expect results like this. DCgan figured out background separation, a plate, and a subject.
I'm training the model on over 15000 images but it's still outputting final test images like the one below:
I'm running the following command:
python main.py --data_dir dataset --dataset food --input_height 128 --output_height 128 --train
What can I do to get better results??
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