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