Football-Vision/config.yaml at main · Smasko7/Football-Vision · GitHub

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# ──────────────────────────────────────────────
# FootballVision configuration
# Override roboflow_api_key by setting the
# environment variable ROBOFLOW_API_KEY
# ──────────────────────────────────────────────

# Video I/O
# Place your source video in the videos/ folder and update the path below
source_video_path: "videos/Demo_Video.mp4"
# target_video_path is auto-generated as outputs/<source_stem>_<mode>.mp4
# Set this only if you want to override the auto-generated name
target_video_path: ""

# Roboflow
roboflow_api_key: ""  # Set via ROBOFLOW_API_KEY environment variable
player_detection_model_id: "football-players-detection-3zvbc/15"
field_detection_model_id: "football-field-detection-f07vi/14"

# ──────────────────────────────────────────────
# Training
# ──────────────────────────────────────────────

# Path to the data.yaml of the dataset you want to train on
# Place your dataset in datasets/ following YOLO format (see README)
dataset_path: "datasets/football-players-detection-dataset/versions/1/data.yaml"

# Pretrained weights to fine-tune from
base_model: "models/yolo11n.pt"

# Path to a locally trained model to use for inference instead of the Roboflow API
# Leave empty to use the Roboflow API model (default)
# e.g. "models/football-players-detection-dataset_yolo11n.pt"
trained_model_path: ""

training_epochs: 2
training_batch: 2
training_imgsz: 640
training_device: "cpu"     # "cpu" or "0" for first GPU
export_format: "pt"        # "pt" (PyTorch) or "onnx"

# Detection thresholds
detection_confidence: 0.3
nms_threshold: 0.5
ball_pad_px: 10

# Class IDs (as defined by the Roboflow model)
ball_id: 0
goalkeeper_id: 1
player_id: 2
referee_id: 3

# Team classifier
siglip_model_path: "google/siglip-base-patch16-224"
team_classifier_batch_size: 32
umap_components: 3
n_teams: 2
embedding_stride: 30   # sample every N frames when collecting crops

# Embeddings cache file (used by team_split mode)
embeddings_path: "embeddings/siglip_embeddings.npy"

# Per-mode frame limits
annotation_frame_limit: 750    # ~10 s at 75 fps
tracking_frame_limit: 250
radar_frame_limit: 250
team_video_frame_limit: 10

# Annotator colours (hex, no #)
color_team_0: "00BFFF"
color_team_1: "FF1493"
color_ball: "FFD700"
color_referee: "FFD700"
color_box_palette:
  - "FF8C00"
  - "00BFFF"
  - "FF1493"
  - "FFD700"

# Annotator geometry
ellipse_thickness: 2
triangle_base: 25
triangle_height: 21
triangle_outline_thickness: 1
keypoint_radius: 8
keypoint_confidence_threshold: 0.5