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--- |
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datasets: |
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- marcelhuber/KermanyV3_flattened_postprocessed |
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language: |
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- en |
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tags: |
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- medical |
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--- |
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# KermanyV3 GAN Model |
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## Description |
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The KermanyV3 GAN is a Generative Adversarial Network trained on the KermanyV3_flattened_posptrocessed dataset to generate synthetic medical images. |
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## Files Included |
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- `fakes000840.png`: Sample of synthetic images generated by the GAN at snapshot 000840. |
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- `log.txt`: Log file containing training progress and details. |
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- `metric-fid50k_full.jsonl`: JSONL file with FID (Fréchet Inception Distance) metric scores for evaluating image quality. |
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- `metric-kid50k_full.jsonl`: JSONL file with KID (Kernel Inception Distance) metric scores for evaluating image quality. |
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- `network-snapshot-000840.pkl`: Snapshot of the GAN's trained parameters at iteration 000840. |
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- `stats.jsonl`: JSONL file containing statistics and metrics related to the training process. |
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- `training_options.json`: JSON file detailing the training hyperparameters and options. |
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## Dataset Used |
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KermanyV3_flattened_posptrocessed |
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## Model Performance |
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- FID Score: 4.954847615273339 |
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- KID Score: 0.003050379662162 |
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## Training Details |
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- Training Iterations: 840 |
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- Training Time: 8h 40m |
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- Architecture: StyleGAN2-ADA-PyTorch |
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- Batch Size: 16 |
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## Usage Instructions |
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1. Load the pre-trained GAN parameters from `network-snapshot-000840.pkl`. |
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2. Use the generator to generate synthetic images. |
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3. To evaluate image quality, refer to `metric-fid50k_full.jsonl` for FID scores and `metric-kid50k_full.jsonl` for KID scores. |
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4. For additional training information, refer to `log.txt`, `stats.jsonl`, and `training_options.json`. |
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