| --- |
| license: mit |
| tags: |
| - segmentation |
| - semantic-segmentation |
| - segformer |
| - sports |
| - advertising-boards |
| - mmsegmentation |
| --- |
| |
| # Advertising Panel Segmentation — SegFormer |
|
|
| Semantic segmentation models for detecting advertising LED boards in sports broadcasting footage. |
|
|
| ## Dataset |
| - **967 images** at 1920x1080 from sports highlights |
| - **Manually annotated** via CVAT.ai with polygon annotations |
| - **Binary segmentation**: background (0) vs advertising board (1) |
| - **Split**: Train 672 / Val 149 / Test 146 |
|
|
| ## Models |
|
|
| | Experiment | Model | Augmentation | mIoU | Board IoU | Dice | Precision | Recall | |
| |---|---|---|---|---|---|---|---| |
| | Exp0 | SegFormer-B0 | Standard | 87.15% | 76.28% | 86.55% | 84.13% | 89.10% | |
| | Exp1 | SegFormer-B1 | Standard | 84.29% | 71.12% | 83.12% | 79.26% | 87.39% | |
| | **Exp2** | **SegFormer-B1** | **Sport-specific** | **87.26%** | **76.45%** | **86.66%** | **85.76%** | **87.57%** | |
| | Exp3-opt1 | SegFormer-B1 | Sport-specific reduced + LR 3e-5 + channels=512 | 84.58% | 71.68% | 83.51% | 78.66% | 88.99% | |
| | Exp3-opt2 | SegFormer-B1 | Sport-specific reduced + LR 3e-5 + channels=256 | 84.92% | 72.26% | 83.90% | 80.40% | 87.71% | |
| | Exp3-opt3 | SegFormer-B1 | Sport-specific + drop_path=0.15 | 86.42% | 74.99% | 85.71% | 82.25% | 89.47% | |
| |
| Best model: **Exp2 - SegFormer-B1 Augmented** (`models/exp2_segformer_b1_augmented/best_mIoU_iter_14000.pth`) |
| |
| ## Detailed Results |
| |
| ### Exp0 - SegFormer-B0 Baseline |
| - Best checkpoint: best_mIoU_iter_18000.pth |
| - mIoU: 87.15% | Board IoU: 76.28% | Dice: 86.55% | Precision: 84.13% | Recall: 89.10% |
|
|
| ### Exp1 - SegFormer-B1 Standard |
| - Best checkpoint: best_mIoU_iter_10000.pth |
| - mIoU: 84.29% | Board IoU: 71.12% | Dice: 83.12% | Precision: 79.26% | Recall: 87.39% |
| |
| ### Exp2 - SegFormer-B1 Augmented ⭐ Best |
| - Best checkpoint: best_mIoU_iter_14000.pth |
| - mIoU: 87.26% | Board IoU: 76.45% | Dice: 86.66% | Precision: 85.76% | Recall: 87.57% |
|
|
| ### Exp3 - SegFormer-B1 Optimized1 |
| - Best checkpoint: best_mIoU_iter_14000.pth |
| - mIoU: 84.58% | Board IoU: 71.68% | Dice: 83.51% | Precision: 78.66% | Recall: 88.99% |
| |
| ### Exp3 - SegFormer-B1 Optimized2 |
| - Best checkpoint: best_mIoU_iter_12000.pth |
| - mIoU: 84.92% | Board IoU: 72.26% | Dice: 83.90% | Precision: 80.40% | Recall: 87.71% |
|
|
| ### Exp3 - SegFormer-B1 Optimized3 |
| - Best checkpoint: best_mIoU_iter_18000.pth |
| - mIoU: 86.42% | Board IoU: 74.99% | Dice: 85.71% | Precision: 82.25% | Recall: 89.47% |
| |
| ## Framework |
| - PyTorch 2.4.1 |
| - MMSegmentation 1.2.2 |
| - mmcv 2.2.0 |
| |
| ## Repository Structure |
| ``` |
| models/ |
| exp0_segformer_b0_baseline/best_mIoU_iter_18000.pth |
| exp1_segformer_b1_standard/best_mIoU_iter_10000.pth |
| exp2_segformer_b1_augmented/best_mIoU_iter_14000.pth |
| exp3_segformer_b1_optimized/best_mIoU_iter_14000.pth |
| exp3_segformer_b1_optimized2/best_mIoU_iter_12000.pth |
| exp3_segformer_b1_optimized3/best_mIoU_iter_18000.pth |
| dataset/ |
| processed.zip (967 images + masks, train/val/test split) |
| ``` |
| |
| ## GitHub |
| https://github.com/ilMassy/advertising-panel-segmentation |
| |
