medical en dataset:marcelhuber/KermanyV3_original region:us
---
datasets:
- marcelhuber/KermanyV3_original
language:
- en
tags:
- medical
---
# KermanyV3 GAN Model

## Description
The KermanyV3 GAN is a Generative Adversarial Network trained on the KermanyV3_original dataset to generate synthetic medical images.

## Files Included
- `fakes001020.png`: Sample of synthetic images generated by the GAN at snapshot 001020.
- `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-001020.pkl`: Snapshot of the GAN's trained parameters at iteration 001020.
- `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_resized

## Model Performance
- FID Score: 8.745657893911403
- KID Score: 0.005549223578578

## Training Details
- Training Iterations: 001020
- Training Time: 11h 06m
- Architecture: StyleGAN2-ADA-PyTorch
- Batch Size: 16

## Usage Instructions
1. Load the pre-trained GAN parameters from `network-snapshot-001020.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`.

![Generated Image](fakes001020.png)