Add DCGAN model with Git LFS

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,85 @@
 ---
 license: mit
+tags:
+- gan
+- dcgan
+- image-generation
+- chess
+- generative-ai
+datasets:
+- niteshfre/chessman-image-dataset
+metrics:
+- fid
+library_name: tensorflow
 ---
+
+# Chessman DCGAN - Chess Piece Generator
+
+This is a Deep Convolutional Generative Adversarial Network (DCGAN) trained to generate images of chess pieces.
+
+## Model Description
+
+- **Architecture**: DCGAN (Deep Convolutional GAN)
+- **Framework**: TensorFlow/Keras
+- **Input**: 100-dimensional random noise vector
+- **Output**: 64x64 RGB images of chess pieces
+- **Training Data**: 552 images from the Chessman Image Dataset (6 chess piece types)
+
+## Training Details
+
+- **Epochs**: 50
+- **Batch Size**: 128
+- **Optimizer**: Adam (lr=1e-4, beta_1=0.5)
+- **Loss**: Binary Cross-Entropy with label smoothing
+- **Data Augmentation**: Random flips, rotations, and zoom
+
+## Model Architecture
+
+### Generator
+- Input: 100-dim latent vector
+- Dense layer → 8×8×256
+- Conv2DTranspose layers: 256→128→64→3
+- Output: 64×64×3 RGB image
+
+### Discriminator
+- Input: 64×64×3 RGB image
+- Conv2D layers: 64→128→256
+- Output: Binary classification (real/fake)
+
+## Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the model
+generator = tf.keras.models.load_model('dcgan_generator.keras')
+
+# Generate random noise
+noise = tf.random.normal([1, 100])
+
+# Generate image
+generated_image = generator(noise, training=False)
+
+# Display
+img = ((generated_image[0, :, :, :] * 127.5) + 127.5).numpy().astype("uint8")
+plt.imshow(img)
+plt.axis('off')
+plt.show()
+```
+
+## Limitations
+
+- Images are 64×64 resolution (relatively low)
+- Model trained on only 552 images (small dataset)
+- Generated pieces may not always be perfectly recognizable
+- No control over which piece type is generated
+
+## Citation
+
+Dataset: [Chessman Image Dataset on Kaggle](https://www.kaggle.com/datasets/niteshfre/chessman-image-dataset)
+
+## License
+
+MIT License

dcgan_generator.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23505b4648f0b240e18ca82fa65652bd1bf7b65185511b3911d8a7caf696c2b6
+size 10977320

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow>=2.10.0
+numpy>=1.23.0
+matplotlib>=3.5.0