Create README.md

README.md

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+---
+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)