YOLOv8：在预测中以置信度和 IOU 优化地图

Question

我正在尝试找出最适合 model.pred 的

conf

和

iou

。

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

上面是我尝试每种组合并按最高平均精度 (MAP) 进行排序的尝试。

这很慢，因为它正在尝试每种组合。也许可以使用像 Optuna 这样的包或其他贝叶斯包？您会采取什么措施来优化这个场景？

Answer 1

使用 Optuna 的答案。

下面将设置一个目标，尝试各种

iou

和

conf

，直到找到最大 MAP50 分数。这将有助于更好地理解

iou

和

conf

值以及它们如何影响地图得分。

1. 安装 Optuna

!pip install optuna

2. 设定目标

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("combos", 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)

3. 开始足迹

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))