soccernetpro 0.0.1.dev4

SoccerNetPro is the professional extension of the popular SoccerNet library, designed for advanced video understanding in soccer. It provides state-of-the-art tools for action recognition, spotting, retrieval, and captioning, making it ideal for researchers, analysts, and developers working with soccer video data.

SoccerNetPro

SoccerNetPro is the professional extension of the popular SoccerNet library, designed for advanced video understanding in soccer. It provides state-of-the-art tools for action recognition, spotting, retrieval, and captioning, making it ideal for researchers, analysts, and developers working with soccer video data.

Installation

conda create -n SoccerNet python=3.12 pip
conda activate SoccerNet
pip install --pre soccernetpro

Configuration Sample (.yaml) file

TASK: classification

DATA:
  dataset_name: mvfouls
  data_dir: /path/to/mvfouls
  view_type: single
  annotations:
    train: /path/to/train_annotations.json
    valid: /path/to/test_annotations.json
    test: /path/to/valid_annotations.json
  num_frames: 16               # 8 before + 8 after the foul
  input_fps: 25                # Original FPS of video
  target_fps: 16               # Temporal downsampling to 1s clip (approx)
  frame_size: [224, 224]       # Spatial resolution (HxW)
  augmentations:
    random_crop: true
    random_horizontal_flip: true
    flip_prob: 0.5
    color_jitter: false
    jitter_params: [0.4, 0.4, 0.4, 0.1]   # brightness, contrast, saturation, hue
    random_erasing: false
  num_workers: 1
  train_batch_size: 8
  valid_batch_size: 4

MODEL:
  type: huggingface
  backbone: video_mae
  pretrained_model: MCG-NJU/videomae-base
  num_classes: 7
  freeze_backbone: true
  

TRAIN:
  enabled: true
  use_weighted_sampler: true
  epochs: 10
  learning_rate: 0.005
  save_dir: ./checkpoints
  log_interval: 10
  save_every: 5

SYSTEM:
  log_dir: ./logs
  seed: 42

Annotations (train/valid/test) (.json) format

{
  "version": "1.0",
  "date": "2025-11-11",
  "task": "action_classification",
  "dataset_name": "mvfouls",
  "metadata": {
    "source": "Professional Soccer Dataset",
    "license": "CC-BY-NC-4.0",
    "created_by": "AI Sports Lab",
    "notes": "Converted automatically from SoccerNet-like foul annotation structure."
  },
  "labels": {
    "foul_type": {
      "type": "single_label",
      "labels": [
        "Challenge",
        "Dive",
        "Elbowing",
        "High Leg",
        "Holding",
        "Pushing",
        "Standing Tackling",
        "Tackling"
      ]
    },
    "severity": {
      "type": "single_label",
      "labels": [
        "No Offence",
        "Offence + No Card",
        "Offence + Yellow Card",
        "Offence + Red Card"
      ]
    },
    "attributes": {
      "type": "multi_label",
      "labels": [
        "Intentional",
        "Reckless",
        "Dangerous Play",
        "VAR Checked",
        "InBox",
        "CounterAttack"
      ]
    }
  },
  "data": [
    {
      "id": "action_0",
      "inputs": [
        {
          "type": "video",
          "path": "Dataset/Train/action_0/clip_0",
          "metadata": {
            "camera_type": "Main camera center",
            "timestamp": 1730826,
            "replay_speed": 1.0
          }
        },
        {
          "type": "video",
          "path": "Dataset/Train/action_0/clip_1",
          "metadata": {
            "camera_type": "Close-up player or field referee",
            "timestamp": 1744173,
            "replay_speed": 1.8
          }
        }
      ],
      "labels": {
        "foul_type": {
          "label": "Challenge"
        },
        "severity": {
          "label": "Offence + No Card"
        },
        "attributes": {
          "labels": [
            "Reckless"
          ]
        }
      },
      "metadata": {
        "UrlLocal": "england_epl\\2014-2015\\2015-02-21 - 18-00 Chelsea 1 - 1 Burnley",
        "Contact": "With contact",
        "Bodypart": "Upper body",
        "Upper body part": "Use of shoulder",
        "Handball": "No handball"
      }
    }
    ]
}

Train

from soccernetpro import model
import wandb

# Initialize model with config
myModel = model.classification(
    config="/path/to/classification.yaml"
)

# Optionally adjust config
myModel.config.TRAIN.learning_rate = 1e-4
myModel.config.MODEL.freeze_backbone = True

# Train on your dataset
myModel.train(
    train_set="/path/to/train_annotations.json",
    valid_set="/path/to/valid_annotations.json",
    pretrained=/path/to/  # or path to pretrained checkpoint
)

Test / Inference

from soccernetpro import model

# Load trained model
myModel = model.classification(
    config="/path/to/classification.yaml"
)

# Run inference on test set
preds, metrics = myModel.infer(
    test_set="/path/to/test_annotations.json",
    pretrained="/path/to/checkpoints/final_model",
)