deep-learningobject-detectionyolobayesianoptuna

YOLOv8: Optimising for map with confidence and iou in prediction


I'm trying to figure out what the best conf and iou is for the model.pred.

from ultralytics import YOLO
import pandas as pd
import numpy as np


df= pd.DataFrame()

# Load a model
for i in range(1,105):
  print('epoch: ',i)
  try:
    
    model = YOLO(f'/content/weights/epoch{i}.pt')


    for confidence in np.arange(0.1,0.4,0.02):
      for inter in np.arange(0.1,0.8,0.05):  
        # Customize validation settings
        validation_results = model.val(data='/content/myproject/data.yaml',
                                      imgsz=640,
                                      batch=16,
                                      conf=confidence,
                                      iou=inter,
                                      device='cpu')

        t = pd.DataFrame([{"epoch":i,
                          "conf":confidence,
                          "iou":inter,
                          "map50":validation_results.box.map50}])
        df = df.append(t, ignore_index = True)

    print(df.sort_values(by=['map50'],ascending=False).head(3))
  except:
    pass

above is my attempt at trying every combination and sorting this by the highest mean average precision (MAP).

This is slow because it's trying every combination. Maybe a package like Optuna or other Bayesian packages can be used? What would you do to optimise this scenario?


Solution

  • An answer using Optuna.

    The following will setup an objective trialling various iou and conf until it finds the maximum MAP50 score. This will lead to a better understanding of the iou and conf values and how they affect the map score.

    1. Install Optuna
    !pip install optuna
    
    1. Setup Objective
    from ultralytics import YOLO
    import pandas as pd
    import numpy as np
    
    class Objective:
    
        def __init__(self):
            self.best_map = 0
    
        def __call__(self, trial):
    
            i = trial.suggest_int("epoch", 1, 104)
            confidence = trial.suggest_float("confidence", 0.05, 0.5)
            inter = trial.suggest_float("iou", 0.1, 0.8)
            model = YOLO(f'/content/weights/epoch{i}.pt')
            validation_results = model.val(data='/content/myproject/data.yaml',
                                  imgsz=640,
                                  batch=16,
                                  conf=confidence,
                                  iou=inter,
                                  device='cpu')
    
            print(validation_results.box.map50)
            self._map = float(validation_results.box.map50)
            map = float(validation_results.box.map50)
            return map
    
        def callback(self, study, trial):
            if study.best_trial == trial:
                self.best_map = self._map
                print('NEW BEST MAP: ', self._map)
    
    1. start trail
    import warnings
    warnings.filterwarnings("ignore", category=RuntimeWarning) # for log error
    
    import optuna
    objective = Objective()
    
    # Setting SEED 
    from optuna.samplers import TPESampler
    sampler = TPESampler(seed=10)
    
    study = optuna.create_study(
        pruner=optuna.pruners.MedianPruner(n_warmup_steps=10), direction="maximize",
        sampler=sampler
    )
    study.optimize(objective, n_trials=1000, callbacks=[objective.callback])
    
    print("Best trial:")
    trial = study.best_trial
    
    print("  Params: ")
    for key, value in trial.params.items():
        print("    {}: {}".format(key, value))