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.gitattributes

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README.md

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+---
+license: apache-2.0
+library_name: ultralytics
+tags:
+- object-detection
+- yolo
+- yolov12
+- comic-books
+- comic
+- computer-vision
+- ultralytics
+- pytorch
+widget:
+- modelId: mosesb/best-comic-panel-detection
+  title: YOLOv12 Comic Panel Detection
+  url: https://huggingface.co/mosesb/best-comic-panel-detection/blob/main/prediction.jpg
+datasets:
+- Custom-Object-Detection
+metrics:
+- mAP50
+- mAP50-95
+---
+
+# YOLOv12 for Comic Panel Detection
+
+This repository contains a **YOLOv12x** object detection model fine-tuned to detect individual panels in comic book pages. The model identifies the bounding boxes for each panel, making it a valuable tool for digitizing comics, extracting content, or building datasets for downstream analysis.
+
+This model was trained in PyTorch using the powerful `ultralytics` library and demonstrates high performance on a custom-annotated dataset of comic pages.
+
+## Model Details
+*   **Architecture:** `YOLOv12x` (the extra-large variant)
+*   **Fine-tuned on:** A custom Roboflow dataset named "Custom-Workflow-3-Object-Detection-1".
+*   **Classes:** `Comic Panel`
+*   **Frameworks:** PyTorch, Ultralytics
+
+## How to Get Started
+
+You can easily use this model with the `ultralytics` library. The model file `best.pt` from this repository is required.
+
+```python
+# 1. Install Ultralytics
+!pip install ultralytics
+
+from ultralytics import YOLO
+from PIL import Image
+
+# 2. Load the fine-tuned model
+# Make sure 'best.pt' is in your current directory
+model = YOLO('best.pt')
+
+# 3. Run inference on an image
+image_path = 'path/to/your/comic_page.jpg'
+results = model.predict(source=image_path)
+
+# 4. Process and visualize results
+# The 'results' object contains bounding boxes, classes, and confidence scores
+for result in results:
+    # Plotting will draw the bounding boxes on the image
+    im_array = result.plot()
+    im = Image.fromarray(im_array[..., ::-1]) # Convert BGR to RGB
+    im.show() # Display the image
+    # or
+    # im.save('prediction_result.jpg')
+
+# You can also access bounding box data directly
+for box in results[0].boxes:
+    print("Class:", model.names[int(box.cls)])
+    print("Confidence:", box.conf.item())
+    print("Coordinates (xyxy):", box.xyxy[0].tolist())
+    print("-" * 20)
+```
+
+## Training Procedure
+
+The model was fine-tuned using transfer learning from a YOLOv12x checkpoint pre-trained on the COCO dataset.
+
+### Training Hyperparameters
+*   **Image Size:** 640x640
+*   **Batch Size:** 16
+*   **Optimizer:** AdamW (lr=0.002)
+*   **Epochs:** 200
+*   **Patience:** 100 epochs for early stopping
+
+![Training and Validation Metrics](results.png)
+
+## Evaluation
+
+The model's performance was evaluated on the validation set during training. The final metrics are based on the checkpoint that achieved the highest **mAP50-95**.
+
+### Key Performance Metrics
+| Metric      | Value | Description                                          |
+| :---------- | :---- | :--------------------------------------------------- |
+| **mAP50**   | 0.991 | Mean Average Precision at IoU threshold 0.50.        |
+| **mAP50-95**| 0.985 | Mean Average Precision averaged over IoU thresholds from 0.50 to 0.95. |
+
+The model achieves near-perfect precision and recall on the validation data, indicating a strong ability to correctly identify comic panels within the styles present in the dataset.
+
+![Confusion Matrix](confusion_matrix.png)
+
+### Qualitative Results
+
+The model correctly identifies panels of various sizes and layouts in the validation set.
+
+![Validation Predictions](val_batch0_pred.jpg)
+
+## Intended Use and Limitations
+This model is intended for applications requiring the segmentation of comic book pages into their constituent panels. This can be a pre-processing step for:
+-   Creating structured digital reading experiences.
+-   Extracting text or characters from individual panels.
+-   Analyzing comic book layouts and artistic styles.
+
+**The model has been tested in real world applications and has shown promising results.**
+
+### Limitations
+*   **Non-Rectangular Panels:** The model is trained to detect rectangular bounding boxes and may struggle with highly irregular or overlapping panel shapes.
+
+## Acknowledgements
+
+*   **Ultralytics** for the amazing [YOLOv12 model](https://github.com/ultralytics/ultralytics) and library.
+*   **Roboflow:** for their dataset hosting platform and **custom-workflow-3-object-detection-g24r5-fmfkb** for compiling and annotating this incredible dataset.
+
+
+*This model card is based on the training notebook [`YOLOV12-Comic-Panel-Detection`](https://github.com/mosesab/YOLOV12-Comic-Panel-Detection).*

args.yaml

@@ -0,0 +1,108 @@
+task: detect
+mode: train
+model: yolo12x.pt
+data: /workspace/datasets/Custom-Workflow-3-Object-Detection-1/data.yaml
+epochs: 200
+time: null
+patience: 100
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: true
+device:
+- 0
+- 1
+workers: 8
+project: my_yolo_train
+name: finetuning
+exist_ok: true
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+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
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+crop_fraction: 1.0
+cfg: null
+tracker: botsort.yaml
+save_dir: my_yolo_train/finetuning

best.pt

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+size 119112954

last.pt

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results.csv

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