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WildTrain Configuration Reference

Documentation for all WildTrain configuration files used in model training.

Detailed Config Guides​

Config CategoryDetailed Reference
Classification TrainingClassification Training Config →
Detection Training & SweepsDetection Training Config →
YOLO Configuration (Plain English)YOLO Config Guide →
Model Registration & InferenceRegistration Config →

Configuration Structure​

WildTrain uses YAML config files with OmegaConf variable interpolation (e.g., ${dataset.input_size}).

configs/
├── classification/                           # Classification configs
│   ├── classification_train.yaml             # Training config
│   ├── classification_eval.yaml              # Evaluation config
│   ├── classification_sweep.yaml             # Hyperparameter sweep
│   ├── classification_pipeline_config.yaml   # Full pipeline (train + eval)
│   └── classification_visualization.yaml     # FiftyOne visualization
├── detection/                                # Detection configs
│   ├── detection_sweep.yaml                  # Hyperparameter sweep
│   ├── visualization.yaml                    # FiftyOne visualization
│   └── yolo_configs/                         # YOLO-specific configs
│       ├── yolo.yaml                         # Training config
│       ├── yolo_eval.yaml                    # Evaluation config
│       ├── yolo_pipeline_config.yaml         # Full pipeline
│       ├── calibration_example.yaml          # Calibration config
│       ├── data/                             # Data YAML files
│       └── models/                           # Architecture YAML files
├── registration/                             # Model registration
│   ├── classifier_registration_example.yaml
│   └── detector_registration_example.yaml
├── inference.yaml                            # Inference server config
└── label_studio_config.xml                   # Label Studio interface

Classification Configs​

classification_train.yaml​

Purpose: Configure classification model training

model:
  architecture: "resnet50"
  num_classes: 10
  pretrained: true
  learning_rate: 0.001
  weight_decay: 0.0001
  dropout: 0.5

data:
  root_data_directory: "D:/data/roi_dataset"
  split: "train"
  batch_size: 32
  num_workers: 4
  image_size: 224

training:
  max_epochs: 100
  accelerator: "gpu"
  devices: 1
  precision: 16
  gradient_clip_val: 1.0

callbacks:
  early_stopping:
    monitor: "val_loss"
    patience: 10
  model_checkpoint:
    monitor: "val_acc"
    mode: "max"
    save_top_k: 3

mlflow:
  experiment_name: "classification"
  tracking_uri: "http://localhost:5000"

classification_eval.yaml​

model:
  checkpoint_path: "checkpoints/best.ckpt"
  mlflow_model_name: "classifier"

data:
  root_data_directory: "D:/data/roi_dataset"
  split: "test"
  batch_size: 64

evaluation:
  save_predictions: true
  generate_confusion_matrix: true

Detection Configs​

YOLO Configuration​

File: configs/detection/yolo_configs/yolo.yaml

Plain English Guide: See YOLO Config Guide for a detailed, non-technical explanation of every setting.

model:
  framework: "yolo"
  size: "n"  # n, s, m, l, x
  pretrained: true

data:
  data_yaml: "D:/data/wildlife/data.yaml"

training:
  epochs: 100
  imgsz: 640
  batch: 16
  optimizer: "AdamW"
  lr0: 0.001
  device: 0

augmentation:
  hsv_h: 0.015
  hsv_s: 0.7
  flipud: 0.0
  fliplr: 0.5
  mosaic: 1.0

mlflow:
  experiment_name: "yolo_detection"

YOLO Data YAML:

# data/wildlife/data.yaml
train: ./images/train
val: ./images/val
nc: 3
names: ['elephant', 'giraffe', 'zebra']

MMDetection Configuration​

File: configs/detection/mmdet_configs/mmdet.yaml

model:
  framework: "mmdet"
  config_file: "configs/detection/mmdet_configs/faster_rcnn.py"

data:
  data_root: "D:/data/coco_format"
  ann_file_train: "train.json"
  ann_file_val: "val.json"

training:
  work_dir: "work_dirs/faster_rcnn"
  max_epochs: 12

Registration Configs​

Classifier Registration​

File: configs/registration/classifier_registration_example.yaml

model_path: "checkpoints/best.ckpt"
model_name: "wildlife_classifier"
model_type: "classifier"

description: "ResNet50 classifier for wildlife ROI"

tags:
  architecture: "resnet50"
  dataset: "wildlife_roi_v1"
  accuracy: "0.95"

aliases:
  - "production"

Detector Registration​

File: configs/registration/detector_registration_example.yaml

model_path: "runs/detect/train/weights/best.pt"
model_name: "wildlife_detector"
model_type: "detector"

description: "YOLO11n for aerial wildlife"

tags:
  framework: "yolo"
  map50: "0.89"

aliases:
  - "production"

Inference Configuration​

File: configs/inference.yaml

server:
  host: "0.0.0.0"
  port: 8000
  workers: 2

model:
  mlflow_model_name: "wildlife_detector"
  mlflow_model_alias: "production"
  device: "cuda"

inference:
  batch_size: 8
  confidence_threshold: 0.5
  nms_threshold: 0.45

Main Configuration​

File: configs/main.yaml

defaults:
  - model: yolo
  - data: detection
  - training: default

experiment_name: "wildlife_detection"
seed: 42

Hyperparameter Tuning​

classification_sweep.yaml​

method: bayes  # grid, random, bayes
metric:
  name: val_acc
  goal: maximize

parameters:
  learning_rate:
    min: 0.0001
    max: 0.01
  batch_size:
    values: [16, 32, 64]
  architecture:
    values: ["resnet18", "resnet50"]

Best Practices​

  1. Use MLflow for experiment tracking
  2. Save checkpoints frequently
  3. Enable early stopping to prevent overfitting
  4. Use mixed precision (precision: 16) for faster training
  5. Version your configs with git

Next Steps​