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🐛 Pytorch实现PNASNet的训练与测试

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基于ImageNet预训练模型的微调的迁移学习的实现

arxiv

Xu Jing

实现了:

  • 数据增强

    • 随机水平翻转
    • 随机竖直翻转
    • 随机亮度值(brightness)
    • 随机色调(hue)
    • 随机饱和度(saturation)
    • 随机对比度(contrast)
  • Pytorch加载训练集的pipeline

  • 基于ImageNet预训练的模型微调的PNASNet及训练

  • 单张图像和视频的推断

训练的参数设置:

  • batch size:16
  • epochs:300
  • Loss: CrossEntropyLoss
  • optim: Adam
  • lr: feature_param:0.0001, linear_param: 0.001
  • 硬件:ubuntu 16.04 64G, Tesla V100(32G)

模型训练:

python3 model.py

推断模型:

python3 inference.py
python3 inference_video.py

测试结果:

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