The Soccer Object Detection Model is a computer vision solution specifically designed for comprehensive soccer video analysis. Built upon the YOLOv11n architecture and trained on a meticulously curated multi-source dataset, this model provides real-time detection of players, balls, and referees in soccer videos across diverse conditions and environments.

This model serves as the foundation for the complete Soccer Analysis Pipeline, enabling advanced capabilities such as player tracking, team assignment, tactical analysis, and performance metrics extraction.

Key Features

Multi-class Detection: Simultaneous detection of players, balls, and referees
Real-time Performance: Optimized for live video analysis (30+ FPS)
Scale-Invariant: Effective detection across different camera distances and angles
Robust Performance: Trained on diverse datasets with varying lighting, weather, and field conditions
EdgeCase-Optimized: Specifically fine-tuned for soccer scenarios and edge cases

Demo

SAMPLE LINK : DRIVE

Note : This sample uses Kmeans, UMAP and SIGLIP for team assignment. This does not have Re-identification applied, hence the large player numbers.

Model Capabilities

Detection Classes

The model is trained to detect three primary object classes with high accuracy:

Class ID	Object Type	Description	Use Case
0	Player	Soccer players from both teams including goalkeepers	Primary tracking target, team assignment, tactical analysis
1	Ball	Soccer ball in various states (rolling, airborne, stationary)	Possession tracking, game flow analysis, event detection
2	Referee	Match officials including referees and linesmen	Contextual differentiation, avoiding tracking confusion

Multi-Scale Detection

Close-up Shots: High-precision detection in detailed player views
Medium Shots: Balanced detection for tactical analysis
Wide-angle Views: Full-field coverage with consistent detection quality
Aerial Views: Drone and elevated camera perspectives

Environmental Robustness

Lighting Conditions: Day games, evening matches, indoor venues, stadium lighting
Weather Conditions: Clear weather, rain, snow, fog conditions
Field Surfaces: Natural grass, artificial turf, different field conditions
Camera Angles: Sideline, goal-line, elevated, broadcast standard angles

Real-world Scenarios

Crowded Scenes: Penalty area situations with multiple overlapping players
Occlusion Handling: Partially visible players and objects
Motion Blur: Fast-moving players and ball tracking
Scale Variation: Players at different distances from camera

Performance Characteristics

Detection Accuracy: High precision with minimal false positives
Processing Speed: Real-time capable (30+ FPS on modern GPUs)
Memory Efficiency: Optimized for continuous video processing

Architecture & Technical Specifications

Base Architecture: YOLOv11n

YOLOv11n (You Only Look Once version 11, nano variant) serves as the foundation architecture, providing an optimal balance between accuracy and computational efficiency.

Dataset

Soccana_player_ball_detection_v1

The dataset covers various Edge cases like:

Occlusions
Close up shots
Behind the goalpost scenes
Camera overlay scenes
Low and High angle shots
Various Resolution shots (160, 320, 540, 1280)

Training Configuration

The model training follows an optimized configuration designed for soccer-specific detection tasks:

python

# Core Training Parameters
epochs = 200                    # Extended training for convergence
img_size = 1280                 # High-resolution input (1280x1280)
batch_size = 32                 # Optimal batch size for 1280 resolution
workers = 8                     # Multi-threaded data loading

# Learning Rate Schedule
lr0 = 0.01                      # Initial learning rate
lrf = 0.01                      # Final learning rate (no decay)
momentum = 0.937                # SGD momentum
weight_decay = 0.0005           # L2 regularization

# Regularization & Augmentation
dropout = 0.3                   # Dropout rate for overfitting prevention
augmentation_probability = 0.5  # Data augmentation frequency

Advanced Training Settings

Augmentation Strategy

python

# Photometric Augmentations
hsv_h = 0.015                   # Hue augmentation range
hsv_s = 0.7                     # Saturation augmentation range  
hsv_v = 0.4                     # Value augmentation range

# Geometric Augmentations
degrees = 0.0                   # Rotation range (disabled for sports)
translate = 0.1                 # Translation augmentation
scale = 0.5                     # Scale augmentation range
shear = 0.0                     # Shear transformation (disabled)

# Advanced Augmentations
mosaic = 1.0                    # Mosaic augmentation probability
mixup = 0.0                     # Mixup augmentation (disabled)
copy_paste = 0.0                # Copy-paste augmentation (disabled)

Loss Function Configuration

python

# Detection Loss Components
box_loss_gain = 0.05            # Bounding box loss weight
cls_loss_gain = 0.5             # Classification loss weight  
dfl_loss_gain = 1.5             # Distribution focal loss weight

# Focal Loss Parameters
fl_gamma = 0.0                  # Focal loss gamma (disabled)
label_smoothing = 0.0           # Label smoothing factor

Optimizer Settings

python

optimizer = "SGD"               # Stochastic Gradient Descent
nbs = 64                        # Nominal batch size for scaling
warmup_epochs = 3.0             # Learning rate warmup period
warmup_momentum = 0.8           # Warmup momentum
warmup_bias_lr = 0.1            # Warmup bias learning rate

Model Architecture Parameters

Backbone Configuration

python

depth_multiple = 0.33           # Model depth scaling factor (nano)
width_multiple = 0.25           # Model width scaling factor (nano)  
max_channels = 1024             # Maximum channel count

Detection Head Settings

python

anchors = None                  # Anchor-free detection
nc = 3                          # Number of classes (Player, Ball, Referee)
conf_threshold = 0.25           # Confidence threshold for detection
iou_threshold = 0.45            # IoU threshold for NMS
max_det = 300                   # Maximum detections per image

Hardware Optimization

GPU Configuration

python

device = "cuda"                 # GPU acceleration  
multi_gpu = True                # Multi-GPU training support
amp = True                      # Automatic Mixed Precision
half = False                    # FP16 inference (disabled during training)

Memory Management

python

cache = "ram"                   # Dataset caching strategy
save_memory = False             # Memory optimization mode
rect = False                    # Rectangular training (disabled)

Implementation & Usage

Model Integration Points

The Soccer Object Detection Model is seamlessly integrated throughout the Soccer Analysis Pipeline:

Core Detection Module (`player_detection/`)

python

from player_detection import load_detection_model, get_detections
import supervision as sv

# Load the trained model
model = load_detection_model("Models/Trained/yolov11_sahi_1280/Model/weights/best.pt")

# Perform detection on a frame
player_detections, ball_detections, referee_detections = get_detections(model, frame)

# Results are returned as supervision.Detections objects with:
# - Bounding boxes in [x1, y1, x2, y2] format  
# - Confidence scores for each detection
# - Class IDs (0=Player, 1=Ball, 2=Referee)

Pipeline Integration (`pipelines/detection_pipeline.py`)

python

from pipelines import DetectionPipeline

# Initialize detection pipeline
pipeline = DetectionPipeline(model_path)

# Video-based detection
pipeline.detect_in_video("input.mp4", "output_detected.mp4", frame_count=300)

# Real-time detection  
pipeline.detect_realtime("input.mp4")  # or webcam index: 0

# Frame-level detection
player_det, ball_det, ref_det = pipeline.detect_frame_objects(frame)
annotated_frame = pipeline.annotate_detections(frame, player_det, ball_det, ref_det)

A detailed guide and code can be found at github

Quick Links

🔗 Repository: https://github.com/Adit-jain/Soccer_Analysis
📊 Dataset: https://huggingface.co/datasets/Adit-jain/Soccana_player_ball_detection_v1
🤖 Model: https://huggingface.co/Adit-jain/soccana

Ssssad542/soccana

⚽ SoccerNet Object Detection Model (YOLOv11)

Introduction

Key Features

Demo

Model Capabilities

Detection Classes

Multi-Scale Detection

Environmental Robustness

Real-world Scenarios

Performance Characteristics

Architecture & Technical Specifications

Base Architecture: YOLOv11n

Dataset

Training Configuration

Advanced Training Settings

Augmentation Strategy

Loss Function Configuration

Optimizer Settings

Model Architecture Parameters

Backbone Configuration

Detection Head Settings

Hardware Optimization

GPU Configuration

Memory Management

Implementation & Usage

Model Integration Points

Core Detection Module (player_detection/)

Pipeline Integration (pipelines/detection_pipeline.py)

A detailed guide and code can be found at github

Ssssad542/
soccana

Core Detection Module (`player_detection/`)

Pipeline Integration (`pipelines/detection_pipeline.py`)