feat: upload new model version

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+augmentation_example.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+library_name: mlx
+tags:
+  - computer-vision
+  - image-classification
+  - chess
+  - cnn
+  - lightweight
+datasets:
+  - synthetic-chess-squares
+model-index:
+  - name: chess-cv
+    results:
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: Chess CV Test Dataset
+          type: chess-cv-test
+        metrics:
+          - type: accuracy
+            value: 0.9985
+            name: Accuracy
+            verified: false
+          - type: f1
+            value: 0.9989
+            name: F1 Score (Macro)
+            verified: false
+      - task:
+          type: image-classification
+          name: Image Classification
+        dataset:
+          name: Chess CV OpenBoard Dataset
+          type: chess-cv-openboard
+        metrics:
+          - type: accuracy
+            value: 0.9757
+            name: Accuracy
+            verified: false
+          - type: f1
+            value: 0.9578
+            name: F1 Score (Macro)
+            verified: false
+pipeline_tag: image-classification
+---
+
+# Chess CV
+
+<div align="center">
+  <img src="https://raw.githubusercontent.com/S1M0N38/chess-cv/main/docs/assets/model.svg" alt="Model Architecture" width="600">
+</div>
+
+Lightweight CNN (156k parameters) that classifies chess pieces from 32×32 pixel square images into 13 classes (6 white pieces, 6 black pieces, empty square). Trained on synthetic data from chess.com/lichess boards and piece sets.
+
+## Quick Start
+
+```bash
+pip install chess-cv
+```
+
+```python
+import mlx.core as mx
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from chess_cv.model import SimpleCNN
+
+# Load model
+model_path = hf_hub_download(repo_id="S1M0N38/chess-cv", filename="best_model.safetensors")
+model = SimpleCNN(num_classes=13)
+model.load_weights(model_path)
+model.eval()
+
+# Predict
+img = Image.open("square.png").convert('RGB').resize((32, 32))
+img_array = mx.array(np.array(img, dtype=np.float32)[None, ...] / 255.0)
+pred_idx = mx.argmax(model(img_array), axis=-1).item()
+
+classes = ['bB', 'bK', 'bN', 'bP', 'bQ', 'bR', 'wB', 'wK', 'wN', 'wP', 'wQ', 'wR', 'xx']
+print(f"Predicted: {classes[pred_idx]}")
+```
+
+## Training Your Own Model
+
+To train or evaluate the model yourself:
+
+```bash
+git clone https://github.com/S1M0N38/chess-cv.git
+cd chess-cv
+uv sync --all-extras
+
+# Generate training data
+python -m chess_cv.preprocessing
+
+# Train model
+python -m chess_cv.train
+
+# Evaluate model
+python -m chess_cv.test
+```
+
+See the [Setup Guide](https://s1m0n38.github.io/chess-cv/setup/) and [Usage Guide](https://s1m0n38.github.io/chess-cv/usage/) for detailed instructions on data generation, training configuration, and evaluation.
+
+## Limitations
+
+- Requires precisely cropped 32×32 pixel square images (no board detection)
+- Trained on synthetic data; may not generalize to real-world photos
+- Not suitable for non-standard piece designs or chess game logic
+- Optimized for Apple Silicon (slower on CPU)
+
+For detailed documentation, architecture details, and advanced usage, see the [full documentation](https://s1m0n38.github.io/chess-cv/).
+
+## Citation
+
+```bibtex
+@software{bertolotto2024chesscv,
+  author = {Bertolotto, Simone},
+  title = {Chess CV: Lightweight CNN for Chess Piece Recognition},
+  year = {2025},
+  url = {https://github.com/S1M0N38/chess-cv}
+}
+```
+
+**Repository:** [github.com/S1M0N38/chess-cv](https://github.com/S1M0N38/chess-cv) • **PyPI:** [pypi.org/project/chess-cv](https://pypi.org/project/chess-cv/)

best_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62dfba5caeeee332a9999e409174edef2c1cd2c78c1a0c05276fa6e57d68c0af
+size 626617

config.json

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+{
+  "architecture": "SimpleCNN",
+  "num_classes": 13,
+  "input_size": [32, 32, 3],
+  "num_parameters": 156000,
+  "framework": "mlx",
+  "task": "image-classification",
+  "classes": [
+    "bB", "bK", "bN", "bP", "bQ", "bR",
+    "wB", "wK", "wN", "wP", "wQ", "wR",
+    "xx"
+  ]
+}