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advertising-panel-segmentation
ilMassy/advertising-panel-segmentation
advertising-panel-segmentation is a machine learning model from ilMassy. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Semantic segmentation models for detecting advertising LED boards in sports broadcasting footage.
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Updated Apr 9, 2026
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.zip405 MB · 57%
At a glance
- License
- mit
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Apr 9, 2026
- SHA
- bc42ecfa
Model card
From the Hugging Face model README
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
dataset/
processed.zip (967 images + masks, train/val/test split)
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
GitHub
- License
- mit
- Created
- Mar 28, 2026
- Updated
- Apr 9, 2026