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Model Training Tutorial

Learn how to train detection and classification models using WildTrain.

Prerequisites​

Step 0: Start MLflow (Optional)​

Before you begin training, it is recommended to launch the MLflow tracking server to monitor your experiments in real-time.

cd wildetect
scripts\launch_mlflow.bat

Access at: http://localhost:5000

Training a YOLO Detector​

Step 1: Prepare Data​

Ensure your dataset is in YOLO format:

data/
├── images/
│   ├── train/
│   └── val/
├── labels/
│   ├── train/
│   └── val/
└── data.yaml

data.yaml:

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

Step 2: Configure Training​

Create or edit a YOLO training config (e.g., configs/detection/yolo_configs/yolo.yaml). The config follows the Ultralytics format:

model: yolo12n.pt        # Pretrained model
data: configs/detection/yolo_configs/data/wildlife.yaml
epochs: 100
imgsz: 800
batch: 16
device: 0                # GPU index
lr0: 0.001
lrf: 0.01
optimizer: AdamW
weight_decay: 0.0005

Step 3: Train​

wildtrain train detector -c configs/detection/yolo_configs/yolo.yaml

Or use the provided script:

wildtrain\scripts\train_detector.bat

Step 4: Evaluate​

wildtrain evaluate detector -c configs/detection/yolo_configs/yolo_eval.yaml

You can also run direct Ultralytics evaluation:

wildtrain evaluate yolo-model -c configs/detection/yolo_configs/eval_config.yaml

Step 5: Register Model to MLflow​

wildtrain register detector configs/registration/detector_registration_example.yaml

See Registration Config Reference for config details.

Training a Classifier​

Step 1: Prepare ROI Dataset​

Use WilData to create an ROI dataset from detection annotations:

wildata create-roi-dataset -c configs/roi-create-config.yaml

This generates a classification-ready directory structure:

data/<dataset_name>/roi/<split>/<class_name>/<images>

Step 2: Compute Dataset Statistics​

wildtrain dataset stats D:/data/roi_dataset --split train -o stats.json

Use the computed mean and std in your training config.

Step 3: Configure Training​

Create or edit configs/classification/classification_train.yaml:

dataset:
  root_data_directory: D:/data
  dataset_type: "roi"
  input_size: 384
  batch_size: 64
  rebalance: true
  stats:
    mean: [0.554, 0.469, 0.348]   # From step 2
    std: [0.203, 0.173, 0.144]
  transforms:
    train:
      - name: Resize
        params: { size: ${dataset.input_size} }
      - name: RandomHorizontalFlip
        params: { p: 0.5 }
    val:
      - name: Resize
        params: { size: ${dataset.input_size} }
  single_class:
    enable: true
    background_class_name: "background"
    single_class_name: "wildlife"
    discard_classes: ["rocks", "vegetation", "other"]

model:
  backbone: timm/vit_base_patch14_reg4_dinov2.lvd142m
  pretrained: true
  backbone_source: timm
  dropout: 0.2
  freeze_backbone: true
  hidden_dim: 128
  num_layers: 2

mlflow:
  experiment_name: wildtrain_classification
  run_name: "baseline_dinov2"
  log_model: true

train:
  epochs: 20
  lr: 1e-3
  weight_decay: 1e-3
  precision: bf16-mixed
  accelerator: auto
  num_workers: 4

checkpoint:
  monitor: val_f1score
  save_top_k: 1
  mode: max
  patience: 10
  dirpath: checkpoints/classification
  filename: "best"

See Classification Training Config Reference for all fields.

Step 4: Train​

wildtrain train classifier -c configs/classification/classification_train.yaml

Step 5: Evaluate​

wildtrain evaluate classifier -c configs/classification/classification_eval.yaml

Step 6: Register Model to MLflow​

wildtrain register classifier -c configs/registration/classifier_registration_example.yaml

Check the Results in MLflow​

Once your training is complete, access the MLflow UI at http://localhost:5000 to view:

  • Training metrics (loss, accuracy, F1)
  • Hyperparameters
  • Model artifacts and checkpoints
  • Model registry

Hyperparameter Sweep​

Run an Optuna-based hyperparameter sweep:

# Classification sweep
wildtrain pipeline classification -c configs/classification/classification_pipeline_config.yaml

# Detection sweep
wildtrain pipeline detection -c configs/detection/detection_sweep.yaml

Validate Configs Before Training​

Check your config for errors before starting a long training run:

wildtrain config validate configs/classification/classification_train.yaml --type classification

Next Steps: