futbol players Computer Vision Project

Background Information

This dataset was curated and annotated by Ilyes Talbi, Head of La revue IA, a French publication focused on stories of machine learning applications.

Main objetive is to identify if soccer (futbol) players, the referree and the soccer ball (futbol).

The original custom dataset (v1) is composed of 163 images.

• Class 0 = players
• Class 1 = referree
• Class 2 = soccer ball (or futbol)

The dataset is available under the Public License.

Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Dataset Versions

Version 7 (v7) - 163 images (raw images)

• Preprocessing: Auto-Orient, Modify Classes: 3 remapped, 0 dropped
  • Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 2 (v2) - 163 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416)
• Augmentations: No augmentations applied
• Training Metrics: This version of the dataset was not trained

Version 3 (v3) - 391 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 0 dropped
  • Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
• Augmentations:
  • Outputs per training example: 3
  • Rotation: Between -25° and +25°
  • Shear: ±15° Horizontal, ±15° Vertical
  • Brightness: Between -25% and +25%
  • Blur: Up to 0.75px
  • Noise: Up to 1% of pixels
  • Bounding Box: Blur: Up to 0.5px
• Training Metrics: 86.4%mAP, 51.8% precision, 90.4% recall

Version 4 (v4) - 391 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 0 dropped
  • Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
• Augmentations:
  • Outputs per training example: 3
  • Rotation: Between -25° and +25°
  • Shear: ±15° Horizontal, ±15° Vertical
  • Brightness: Between -25% and +25%
  • Blur: Up to 0.75px
  • Noise: Up to 1% of pixels
  • Bounding Box: Blur: Up to 0.5px
• Training Metrics: 84.6% mAP, 52.3% precision, 85.3% recall

Version 5 (v5) - 391 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 2 dropped
  • Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
    • Only Class 0, which was remapped to players was included in this version
• Augmentations:
  • Outputs per training example: 3
  • Rotation: Between -25° and +25°
  • Shear: ±15° Horizontal, ±15° Vertical
  • Brightness: Between -25% and +25%
  • Blur: Up to 0.75px
  • Noise: Up to 1% of pixels
  • Bounding Box: Blur: Up to 0.5px
• Training Metrics: Trained from the COCO Checkpoint in Public Models ("transfer learning") on Roboflow
  • 98.8%mAP, 76.3% precision, 99.2% recall

Version 6 (v6) - 391 images

• Preprocessing: Auto-Orient and Resize (Stretch to 416x416), Modify Classes: 3 remapped, 2 dropped
  • Modified Classes: Class 0 = players, Class 1 = referree, Class 2 = futbol
    • Only Class 0, which was remapped to players was included in this version
• Augmentations:
  • Outputs per training example: 3
  • Rotation: Between -25° and +25°
  • Shear: ±15° Horizontal, ±15° Vertical
  • Brightness: Between -25% and +25%
  • Blur: Up to 0.75px
  • Noise: Up to 1% of pixels
  • Bounding Box: Blur: Up to 0.5px
• Training Metrics: Trained from Scratch (no transfer learning employed)
  • 95.5%mAP, 67.8% precision, 95.5% recall

Ilyes Talbi - LinkedIn | La revue IA