PaddleX/tutorials/train/object_detection/picodet.py

import paddlex as pdx
from paddlex import transforms as T

# 下载和解压昆虫检测数据集
dataset = 'https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz'
pdx.utils.download_and_decompress(dataset, path='./')

# 定义训练和验证时的transforms
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/transforms/transforms.md
train_transforms = T.Compose([
    T.RandomCrop(), T.RandomHorizontalFlip(), T.RandomDistort(),
    T.BatchRandomResize(
        target_sizes=[576, 608, 640, 672, 704], interp='RANDOM'), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

eval_transforms = T.Compose([
    T.Resize(
        target_size=640, interp='CUBIC'), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 定义训练和验证所用的数据集
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/datasets.md
train_dataset = pdx.datasets.VOCDetection(
    data_dir='insect_det',
    file_list='insect_det/train_list.txt',
    label_list='insect_det/labels.txt',
    transforms=train_transforms,
    shuffle=True)

eval_dataset = pdx.datasets.VOCDetection(
    data_dir='insect_det',
    file_list='insect_det/val_list.txt',
    label_list='insect_det/labels.txt',
    transforms=eval_transforms,
    shuffle=False)

# 初始化模型，并进行训练
# 可使用VisualDL查看训练指标，参考https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/visualdl.md
num_classes = len(train_dataset.labels)
model = pdx.det.PicoDet(num_classes=num_classes, backbone='ESNet_l')

# API说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/apis/models/detection.md
# 各参数介绍与调整说明：https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/parameters.md
model.train(
    num_epochs=20,
    train_dataset=train_dataset,
    train_batch_size=14,
    eval_dataset=eval_dataset,
    pretrain_weights='COCO',
    learning_rate=.05,
    warmup_steps=24,
    warmup_start_lr=0.005,
    save_interval_epochs=1,
    lr_decay_epochs=[6, 8, 11],
    use_ema=True,
    save_dir='output/picodet_esnet_l',
    use_vdl=True)