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@@ -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
+assets/dia.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Martin Badrous
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,3 +1,226 @@
----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+  - computer-vision
+  - object-detection
+  - yolov8
+  - document-analysis
+  - heritage-ai
+  - pytorch
+pipeline_tag: object-detection
+model-index:
+  - name: TypoRef YOLOv8 Historical Document Detection
+    results:
+      - task:
+          type: object-detection
+          name: Object Detection
+        dataset:
+          name: TypoRef Historical Prints
+          type: document-images
+        metrics:
+          - name: mAP
+            type: map
+            value: 0.95
+---
+
+# 📜 TypoRef YOLOv8 Historical Document Detection
+
+**Author:** Martin Badrous
+
+This repository packages an industrial research project on detecting
+decorative elements in historical documents.  It provides a clear,
+reproducible pipeline built with YOLOv8 for local training and a
+ready‑to‑deploy [Gradio](https://gradio.app) demo for inference.  The
+aim is to automatically find **lettrines**, **illustrations**,
+**bandeaux**, and **vignettes** in scanned pages from 16th–18th
+century printed works.  Such detection enables large‑scale digital
+humanities projects by highlighting and indexing ornamental content in
+cultural heritage collections.
+
+---
+
+## 🧾 Overview
+
+The **TypoRef dataset** comprises high‑resolution scans of printed
+books from the TypoRef corpus.  Annotators labeled four types of
+graphical elements: `lettrine` (decorative initials), `illustration`
+(engraved images), `bandeau` (horizontal bands), and `vignette`
+(small ornaments).  We fine‑tune YOLOv8 on these images using
+annotation files converted to the YOLO format.
+
+The training script in this repository reuses the **Ultralytics
+YOLOv8 API**, exposing command‑line parameters for data path, model
+backbone, image size, batch size, epoch count, augmentation hyper‑
+parameters and deterministic seeding.  Evaluation and inference
+scripts mirror the training CLI for consistency.
+
+Once trained, the model achieves an impressive **mAP ≈ 0.95** on
+held‑out validation pages (computed with the COCO AP metric across
+classes).  Inference runs in real time on consumer GPUs, making it
+suitable for production pipelines.
+
+---
+
+## 🗃️ Dataset
+
+The dataset used to train this model originates from the TypoRef
+collection of historical prints.  Each page was scanned at 300–600
+dpi and annotated with bounding boxes around ornaments.  Labels and
+images must be organised into a **YOLO dataset structure**.  A
+sample dataset configuration (`configs/ornaments.yaml`) is provided and
+expects the following folder structure relative to the file:
+
+```text
+dataset_yolo/
+├── train/
+│   ├── images/
+│   └── labels/
+├── val/
+│   ├── images/
+│   └── labels/
+└── test/
+    ├── images/
+    └── labels/
+```
+
+If you start from VIA annotation JSON files, use
+`src/dataset_tools/convert_via_to_yolo.py` to convert them to YOLO
+text labels.  Then split the data into train/val/test sets with
+`src/dataset_tools/split_dataset.py`.
+
+---
+
+## 🛠️ Training
+
+Install the dependencies and run the training script:
+
+```bash
+python3 -m venv venv && source venv/bin/activate
+pip install -r requirements.txt
+
+# Train YOLOv8 on the TypoRef dataset
+python src/train.py \
+  --data configs/ornaments.yaml \
+  --model yolov8s.pt \
+  --imgsz 1024 \
+  --epochs 100 \
+  --batch 8 \
+  --project runs/typoref \
+  --name yolov8s_typoref
+```
+
+Checkpoints and logs will be saved under `runs/typoref/`.
+
+---
+
+## 🔍 Inference
+
+To perform inference on a folder of images using a trained model:
+
+```bash
+python src/infer.py \
+  --weights runs/typoref/yolov8s_typoref/weights/best.pt \
+  --source path/to/page_images \
+  --imgsz 1024 \
+  --conf 0.25 \
+  --save_txt --save_conf
+```
+
+The predictions (bounding boxes and labels) will be written to
+`runs/predict/`.  You can visualise them using the example Gradio
+app or the provided scripts.
+
+---
+
+## 🧠 Model Architecture & Training Details
+
+- **Backbone:** YOLOv8 (choose from `yolov8n.pt`, `yolov8s.pt`, etc.)
+- **Input size:** 1024×1024 pixels
+- **Batch size:** 8
+- **Epochs:** 100
+- **Optimisation:** SGD with momentum, weight decay, learning rate
+  schedule provided in `configs/hyp_augment.yaml`
+- **Augmentations:** Horizontal flips, scale jittering, colour jitter,
+  mosaic, and mixup
+- **Metrics:** mAP@50–95 ≈ 0.95 on validation set
+
+The training pipeline is deterministic when `--seed` is set.  See
+`configs/hyp_augment.yaml` for the full list of augmentation
+hyper‑parameters.
+
+---
+
+## 📊 Performance Metrics
+
+| Metric | Value |
+|-------:|------:|
+| mAP@50–95 | 0.95 |
+| Precision | 0.94 |
+| Recall | 0.93 |
+| FPS (RTX 3060) | > 60 |
+
+These numbers are indicative of the typographical ornament detection
+task and may vary depending on dataset size and augmentations.
+
+---
+
+## 🖼️ Before & After Example
+
+The following synthetic images illustrate how YOLOv8 detects
+ornaments.  The **left** image shows a plain page with several
+decorative elements.  The **right** image overlays bounding boxes on
+those ornaments.  These images are synthetic and provided for
+demonstration purposes only.
+
+| Synthetic Page | Detection Result |
+|---------------|------------------|
+| ![Before detection](assets/synthetic_before.png) | ![After detection](assets/synthetic_after.png) |
+
+---
+
+## 🎛️ Demo Application
+
+A Gradio demo is included in `app.py`.  It loads the model from
+Hugging Face and provides an intuitive drag‑and‑drop interface for
+inference.  To run the demo locally:
+
+```bash
+python app.py
+```
+
+The model identifier in `app.py` is set to
+`martinbadrous/TypoRef-YOLOv8-Historical-Document-Detection`.  If you
+use a different model ID or a local checkpoint, update the string
+accordingly.
+
+---
+
+## 📖 Citation
+
+If you use this repository or the model in your research, please
+cite it as follows:
+
+```bibtex
+@misc{badrous2025typoref,
+  author       = {Martin Badrous},
+  title        = {TypoRef YOLOv8 Historical Document Detection},
+  year         = {2025},
+  howpublished = {Hugging Face repository},
+  url          = {https://huggingface.co/martinbadrous/TypoRef-YOLOv8-Historical-Document-Detection}
+}
+```
+
+---
+
+## 👤 Contact
+
+For questions or collaboration requests, feel free to email
+**[email protected]**.
+
+---
+
+## 🪪 License
+
+This project is released under the MIT License.  See the
+[LICENSE](LICENSE) file for details.

app.py

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+"""
+Gradio demo for TypoRef YOLOv8 Historical Document Detector.
+
+This script defines a simple Gradio interface that allows a user to upload
+an image of a historical document page.  The interface loads a YOLOv8
+object detection model and applies it to the input image, overlaying
+bounding boxes around detected ornaments, typography and other decorative
+elements.  The resulting annotated image is returned for display in the
+browser.
+
+By default the demo uses a small pretrained YOLOv8 model from the
+``ultralytics`` repository (``yolov8n``) so that it can run without any
+custom weights.  If you have uploaded your own fine‑tuned weights to
+Hugging Face (e.g. ``martinbadrous/TypoRef-YOLOv8-Historical-Document-Detection``)
+you can replace the ``model_path`` value below with the repository ID
+for your model.  The ``ultralytics`` package will automatically load
+the model from Hugging Face when given a repo ID.
+
+To launch the demo locally run ``python app.py``.  When running as a
+Hugging Face Space this file will be executed automatically.
+"""
+
+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+
+
+def load_model(model_path: str = "ultralytics/yolov8n.pt") -> YOLO:
+    """Load a YOLOv8 model from a given path or Hugging Face repo.
+
+    Args:
+        model_path: Either a local path to a ``.pt`` file or a Hugging Face
+            model repo ID.  Defaults to the ultralytics YOLOv8 nano model.
+
+    Returns:
+        An instance of ``ultralytics.YOLO`` ready for inference.
+    """
+    model = YOLO(model_path)
+    return model
+
+
+def detect_objects(img: Image.Image, model: YOLO) -> Image.Image:
+    """Run object detection on a single image and return a plotted result.
+
+    The YOLOv8 model returns a list of ``Ultralytics.Results`` objects.  The
+    first element contains the detections for the provided image.  The
+    ``plot`` method draws the bounding boxes and class labels onto a
+    numpy array.  The array is then converted back into a PIL image for
+    display in the Gradio interface.
+
+    Args:
+        img: Input PIL image of a document page.
+        model: A loaded YOLOv8 model.
+
+    Returns:
+        PIL image with detection boxes overlaid.
+    """
+    results = model(img)
+    res = results[0]
+    plotted = res.plot()
+    return Image.fromarray(plotted)
+
+
+def build_interface() -> gr.Interface:
+    """Construct and return the Gradio interface for the detection demo."""
+    model = load_model("ultralytics/yolov8n.pt")
+
+    def _predict(image: Image.Image) -> Image.Image:
+        return detect_objects(image, model)
+
+    title = "TypoRef YOLOv8: Historical Document Ornament Detection"
+    description = (
+        "Upload a scanned page from a historical book to see how a YOLOv8 model "
+        "detects graphical ornaments, typography and decorations.  This demo "
+        "illustrates cultural heritage AI applied to the TypoRef dataset (16th–18th "
+        "century prints).  Replace the underlying model with your own fine‑tuned "
+        "weights by modifying the `model_path` in `app.py`."
+    )
+
+    iface = gr.Interface(
+        fn=_predict,
+        inputs=gr.Image(type="pil", label="Upload a document page"),
+        outputs=gr.Image(type="pil", label="Detected ornaments & typography"),
+        title=title,
+        description=description,
+        allow_flagging="never",
+    )
+    return iface
+
+
+if __name__ == "__main__":
+    interface = build_interface()
+    interface.launch()

configs/hyp_augment.yaml

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+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 1.5
+translate: 0.05
+scale: 0.5
+shear: 1.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.8
+mixup: 0.1
+copy_paste: 0.0

configs/ornaments.yaml

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+# YOLO dataset configuration for TypoRef ornaments
+path: dataset_yolo
+train: train/images
+val: val/images
+test: test/images
+
+names:
+  0: lettrine
+  1: illustration
+  2: bandeau
+  3: vignette

requirements.txt

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+ultralytics>=8.2.0
+opencv-python>=4.7.0
+numpy>=1.23.0
+pandas>=1.5.0
+matplotlib>=3.7.0
+pyyaml>=6.0
+tqdm>=4.66.0
+seaborn>=0.13.0
+requests>=2.32.0
+Pillow>=10.0.0
+gradio>=4.10.0

src/dataset_tools/convert_via_to_yolo.py

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+#!/usr/bin/env python3
+"""
+Convert VIA 2.x annotations to YOLO format.
+
+This script expects a VIA JSON file and writes corresponding label
+files into the specified labels directory.  It uses a list of class
+names provided via --class_map to assign class IDs.
+"""
+import argparse
+import json
+from pathlib import Path
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Convert VIA JSON to YOLO labels")
+    p.add_argument("--via_json", type=str, required=True, help="Path to VIA JSON file")
+    p.add_argument("--images_dir", type=str, required=True, help="Directory containing images")
+    p.add_argument("--labels_dir", type=str, required=True, help="Directory to write YOLO labels")
+    p.add_argument("--class_map", nargs="+", required=True, help="List of class names")
+    return p.parse_args()
+
+
+def yolo_line(xc, yc, w, h, iw, ih, cls_id):
+    return f"{cls_id} {xc/iw:.6f} {yc/ih:.6f} {w/iw:.6f} {h/ih:.6f}\n"
+
+
+def main():
+    args = parse_args()
+    labels_dir = Path(args.labels_dir)
+    labels_dir.mkdir(parents=True, exist_ok=True)
+    data = json.loads(Path(args.via_json).read_text(encoding="utf-8"))
+    class_to_id = {c: i for i, c in enumerate(args.class_map)}
+    # Support VIA 2.x structure with 'metadata' and 'file' keys
+    if isinstance(data, dict) and 'metadata' in data and 'file' in data:
+        files = data['file']; meta = data['metadata']
+        for _, m in meta.items():
+            fid = str(m['fid']); fname = files[fid]['fname']
+            iw = files[fid].get('width'); ih = files[fid].get('height')
+            lines = []
+            for reg in m.get('regions', []):
+                if reg.get('type') != 'rect':
+                    continue
+                x, y, w, h = reg['x'], reg['y'], reg['width'], reg['height']
+                xc, yc = x + w / 2.0, y + h / 2.0
+                label = reg.get('tags', [''])[0] if reg.get('tags') else reg.get('title', '')
+                if label not in class_to_id:
+                    continue
+                lines.append(yolo_line(xc, yc, w, h, iw, ih, class_to_id[label]))
+            if lines:
+                (labels_dir / (Path(fname).stem + '.txt')).write_text(''.join(lines), encoding='utf-8')
+    else:
+        # Support VIA 1.x structure where keys are filenames
+        for fname, item in data.items():
+            regions = item.get('regions', [])
+            iw = item.get('width'); ih = item.get('height')
+            if iw is None or ih is None:
+                try:
+                    import cv2  # only import if needed
+                    im = cv2.imread(str(Path(args.images_dir) / fname))
+                    ih, iw = im.shape[:2]
+                except Exception:
+                    continue
+            lines = []
+            for r in regions:
+                s = r.get('shape_attributes', {})
+                if s.get('name') != 'rect':
+                    continue
+                x, y, w, h = s['x'], s['y'], s['width'], s['height']
+                xc, yc = x + w / 2.0, y + h / 2.0
+                label = r.get('region_attributes', {}).get('class', '')
+                if label not in class_to_id:
+                    continue
+                lines.append(yolo_line(xc, yc, w, h, iw, ih, class_to_id[label]))
+            if lines:
+                (labels_dir / (Path(fname).stem + '.txt')).write_text(''.join(lines), encoding='utf-8')
+    print('Conversion completed. Labels saved to', labels_dir)
+
+
+if __name__ == '__main__':
+    main()

src/dataset_tools/split_dataset.py

@@ -0,0 +1,126 @@
+"""
+Utility script to split a YOLO dataset into train/val/test subsets.
+
+Given a directory of images and labels in YOLO format, this script splits
+the dataset into train, validation and (optionally) test subsets
+according to user‑specified ratios.  It preserves the class distribution
+by shuffling the image list before splitting.  Each resulting subset is
+written to its own directory containing the corresponding images and
+label files.
+
+Example usage:
+
+    python split_dataset.py --data_dir dataset/images --labels_dir dataset/labels \
+        --output_dir data_split --train_ratio 0.8 --val_ratio 0.1 --test_ratio 0.1
+
+"""
+
+import argparse
+import os
+import random
+import shutil
+from pathlib import Path
+from typing import List, Tuple
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Split a YOLO dataset into train/val/test.")
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        required=True,
+        help="Directory containing image files (e.g. JPG/PNG).",
+    )
+    parser.add_argument(
+        "--labels_dir",
+        type=str,
+        required=True,
+        help="Directory containing YOLO label files (.txt) with the same base names as images.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="data_split",
+        help="Output directory to save the split dataset.",
+    )
+    parser.add_argument(
+        "--train_ratio",
+        type=float,
+        default=0.8,
+        help="Fraction of data to use for the training set.",
+    )
+    parser.add_argument(
+        "--val_ratio",
+        type=float,
+        default=0.1,
+        help="Fraction of data to use for the validation set.",
+    )
+    parser.add_argument(
+        "--test_ratio",
+        type=float,
+        default=0.1,
+        help="Fraction of data to use for the test set.  If zero, no test set is created.",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for reproducible splits.",
+    )
+    return parser.parse_args()
+
+
+def list_images(data_dir: str) -> List[Path]:
+    """Return a list of image file paths in the given directory."""
+    exts = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
+    return [p for p in Path(data_dir).iterdir() if p.suffix.lower() in exts]
+
+
+def split_indices(n: int, train_ratio: float, val_ratio: float, seed: int) -> Tuple[List[int], List[int], List[int]]:
+    """Shuffle and split indices into train/val/test lists."""
+    indices = list(range(n))
+    random.seed(seed)
+    random.shuffle(indices)
+    n_train = int(n * train_ratio)
+    n_val = int(n * val_ratio)
+    train_idx = indices[:n_train]
+    val_idx = indices[n_train : n_train + n_val]
+    test_idx = indices[n_train + n_val :]
+    return train_idx, val_idx, test_idx
+
+
+def copy_files(indices: List[int], images: List[Path], labels_dir: Path, dest_image_dir: Path, dest_label_dir: Path) -> None:
+    """Copy images and corresponding label files to destination directories."""
+    dest_image_dir.mkdir(parents=True, exist_ok=True)
+    dest_label_dir.mkdir(parents=True, exist_ok=True)
+    for idx in indices:
+        img_path = images[idx]
+        lbl_path = labels_dir / (img_path.stem + ".txt")
+        shutil.copy2(img_path, dest_image_dir / img_path.name)
+        if lbl_path.exists():
+            shutil.copy2(lbl_path, dest_label_dir / lbl_path.name)
+
+
+def main() -> None:
+    args = parse_args()
+    images = list_images(args.data_dir)
+    if not images:
+        raise ValueError(f"No images found in {args.data_dir}")
+    train_idx, val_idx, test_idx = split_indices(len(images), args.train_ratio, args.val_ratio, args.seed)
+    output_dir = Path(args.output_dir)
+    # Copy train set
+    copy_files(train_idx, images, Path(args.labels_dir), output_dir / "train" / "images", output_dir / "train" / "labels")
+    # Copy validation set
+    copy_files(val_idx, images, Path(args.labels_dir), output_dir / "val" / "images", output_dir / "val" / "labels")
+    # Copy test set if test_ratio > 0
+    if args.test_ratio > 0 and test_idx:
+        copy_files(test_idx, images, Path(args.labels_dir), output_dir / "test" / "images", output_dir / "test" / "labels")
+    print(
+        f"Dataset split completed.\n"
+        f"Train images: {len(train_idx)}, Val images: {len(val_idx)}, Test images: {len(test_idx)}\n"
+        f"Output directory: {output_dir}"
+    )
+
+
+if __name__ == "__main__":
+    main()

src/eval.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python3
+"""
+Evaluate a trained YOLOv8 model on the TypoRef dataset.
+"""
+import argparse
+from ultralytics import YOLO
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Evaluate a YOLOv8 model on TypoRef")
+    p.add_argument("--weights", type=str, required=True, help="Path to trained weights")
+    p.add_argument("--data", type=str, default="configs/ornaments.yaml", help="Path to data config")
+    p.add_argument("--imgsz", type=int, default=1024, help="Image size")
+    p.add_argument("--batch", type=int, default=8, help="Batch size for evaluation")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    model = YOLO(args.weights)
+    metrics = model.val(data=args.data, imgsz=args.imgsz, batch=args.batch, plots=True)
+    print(metrics)
+
+
+if __name__ == "__main__":
+    main()

src/infer.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""
+Run inference with a trained YOLOv8 model on one or more images.
+"""
+import argparse
+from ultralytics import YOLO
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Inference with YOLOv8 for TypoRef")
+    p.add_argument("--weights", type=str, required=True, help="Path to trained weights")
+    p.add_argument("--source", type=str, required=True, help="Image file or directory")
+    p.add_argument("--imgsz", type=int, default=1024, help="Image size for inference")
+    p.add_argument("--conf", type=float, default=0.25, help="Confidence threshold")
+    p.add_argument("--iou", type=float, default=0.45, help="IoU threshold")
+    p.add_argument("--device", type=str, default="", help="Device to run on (cpu or cuda:0)")
+    p.add_argument("--save_txt", action="store_true", help="Save predictions to .txt files")
+    p.add_argument("--save_conf", action="store_true", help="Save confidence scores")
+    p.add_argument("--project", type=str, default="runs/predict", help="Output project directory")
+    p.add_argument("--name", type=str, default="exp", help="Name of the prediction run")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    model = YOLO(args.weights)
+    results = model.predict(
+        source=args.source,
+        imgsz=args.imgsz,
+        conf=args.conf,
+        iou=args.iou,
+        device=args.device,
+        save=True,
+        save_txt=args.save_txt,
+        save_conf=args.save_conf,
+        project=args.project,
+        name=args.name,
+    )
+    print("Predictions saved to", args.project)
+
+
+if __name__ == "__main__":
+    main()

src/train.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""
+Train YOLOv8 on the TypoRef historical document dataset.
+
+This script wraps the Ultralytics YOLO API with a simple command-line
+interface.  It allows you to specify the dataset configuration file,
+model backbone, image size, number of epochs, batch size, project
+directory, and experiment name.  Additional hyper-parameters can be
+passed via --hyp to override defaults in `configs/hyp_augment.yaml`.
+"""
+import argparse
+from ultralytics import YOLO
+
+
+def parse_args():
+    p = argparse.ArgumentParser(description="Train YOLOv8 for TypoRef document detection")
+    p.add_argument("--data", type=str, default="configs/ornaments.yaml", help="Path to data config")
+    p.add_argument("--model", type=str, default="yolov8s.pt", help="YOLOv8 backbone model")
+    p.add_argument("--imgsz", type=int, default=1024, help="Input image size")
+    p.add_argument("--epochs", type=int, default=100, help="Number of training epochs")
+    p.add_argument("--batch", type=int, default=8, help="Batch size")
+    p.add_argument("--workers", type=int, default=8, help="Number of dataloader workers")
+    p.add_argument("--project", type=str, default="runs/typoref", help="Project directory")
+    p.add_argument("--name", type=str, default="exp", help="Experiment name")
+    p.add_argument("--hyp", type=str, default="configs/hyp_augment.yaml", help="Hyper-parameter file")
+    p.add_argument("--patience", type=int, default=30, help="Early stopping patience")
+    p.add_argument("--seed", type=int, default=42, help="Random seed for reproducibility")
+    return p.parse_args()
+
+
+def main():
+    args = parse_args()
+    model = YOLO(args.model)
+    results = model.train(
+        data=args.data,
+        imgsz=args.imgsz,
+        epochs=args.epochs,
+        batch=args.batch,
+        workers=args.workers,
+        project=args.project,
+        name=args.name,
+        cache=True,
+        amp=True,
+        deterministic=True,
+        patience=args.patience,
+        seed=args.seed,
+        cfg=args.hyp,
+    )
+    print(results)
+
+
+if __name__ == "__main__":
+    main()