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RFDETR-Soccernet

Object Detection
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sports-analytics
soccer
football
rf-detr
computer-vision
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RFDETR-Soccernet
File size: 3,149 Bytes 
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{
  "model_name": "rf-detr-soccernet",
  "architecture": "RF-DETR-Large",
  "task": "object-detection",
  "domain": "sports-analytics",
  "dataset": "SoccerNet-Tracking-2023",
  
  "model_info": {
    "parameters": 128000000,
    "parameter_count": "128M",
    "architecture_details": "RF-DETR-Large with DINOv2 backbone",
    "input_size": [1280, 1280],
    "input_channels": 3,
    "output_format": "bounding_boxes_with_classes"
  },
  
  "classes": {
    "num_classes": 4,
    "class_names": ["ball", "player", "referee", "goalkeeper"],
    "class_mapping": {
      "0": "ball",
      "1": "player", 
      "2": "referee",
      "3": "goalkeeper"
    },
    "class_colors": {
      "ball": [255, 0, 0],
      "player": [0, 255, 0],
      "referee": [255, 255, 0],
      "goalkeeper": [0, 255, 255]
    }
  },
  
  "performance": {
    "mAP": 0.498,
    "mAP@50": 0.857,
    "mAP@75": 0.520,
    "target_achieved": true,
    "target_mAP@50": 0.8495,
    "evaluation_dataset": "SoccerNet-Tracking-2023-test",
    "evaluation_images": 9000
  },
  
  "training_info": {
    "epochs_completed": 4,
    "total_training_time_hours": 14,
    "dataset_size": 42750,
    "validation_size": 9000,
    "batch_size": 4,
    "learning_rate": 0.0001,
    "optimizer": "AdamW",
    "scheduler": "cosine_annealing",
    "hardware": "NVIDIA A100 40GB",
    "training_framework": "PyTorch",
    "mixed_precision": true
  },
  
  "preprocessing": {
    "normalization": "ImageNet",
    "augmentation": [
      "random_scaling",
      "random_rotation", 
      "color_jittering",
      "horizontal_flip"
    ],
    "input_resolution": 1280,
    "multi_scale_training": true,
    "scale_range": [896, 1280]
  },
  
  "inference": {
    "default_confidence_threshold": 0.5,
    "recommended_thresholds": {
      "ball": 0.4,
      "player": 0.5,
      "referee": 0.6,
      "goalkeeper": 0.5
    },
    "processing_speed_fps": {
      "RTX_4070": 15,
      "A100": 30,
      "CPU": 3
    },
    "memory_requirements": {
      "model_size_gb": 1.46,
      "gpu_memory_gb": 6,
      "ram_gb": 4
    }
  },
  
  "use_cases": [
    "sports_analytics",
    "player_tracking", 
    "ball_possession_analysis",
    "formation_analysis",
    "broadcast_enhancement",
    "tactical_research",
    "video_analytics",
    "automated_highlights"
  ],
  
  "limitations": [
    "optimized_for_broadcast_footage",
    "standard_camera_angles_preferred",
    "lighting_sensitivity",
    "small_ball_occlusion_challenges"
  ],
  
  "file_info": {
    "checkpoint_filename": "checkpoint_best_regular.pth",
    "checkpoint_size_mb": 1495,
    "created_date": "2025-07-29",
    "version": "1.0.0",
    "huggingface_compatible": true,
    "git_lfs_required": true
  },
  
  "citation": {
    "title": "RF-DETR SoccerNet: High-Performance Soccer Object Detection",
    "authors": ["Computer Vision Research Team"],
    "year": 2025,
    "publisher": "Hugging Face",
    "license": "Apache-2.0"
  },
  
  "contact": {
    "repository": "huggingface.co/YOUR-USERNAME/rf-detr-soccernet",
    "issues": "github.com/YOUR-USERNAME/rf-detr-soccernet/issues",
    "documentation": "README.md"
  }
}