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徐勇
f4562c5e58
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3 years ago | |
|---|---|---|
| .META-INF | 3 years ago | |
| __pycache__ | 3 years ago | |
| models | 3 years ago | |
| AnomalyDtectionTest.py | 3 years ago | |
| AnomalyDtectionTrain.py | 3 years ago | |
| Dataset.py | 3 years ago | |
| config.py | 3 years ago | |
| evaluate.py | 3 years ago | |
| losses.py | 3 years ago | |
| readme.md | 3 years ago | |
| train.py | 3 years ago | |
| utils.py | 3 years ago | |
readme.md
项目中文名
基于生成对抗网络的异常行为检测
作者
黄超
项目简介
1. 功能
实现复杂场景中异常行为的实时检测
2. 性能
| 数据集 | AUC |
|---|---|
| UCSD Ped2 | 95.4% |
| 平均检测速度为25fps. |
3. 评估指标
AUC
4. 使用数据集
UCSD Ped2
运行环境与依赖
代码运行的环境与依赖。如下所示：
| 类别 | 名称 | 版本 |
|---|---|---|
| os | ubuntu | 16.04 |
| 深度学习框架 | pytorch | >= 1.1 |
| Python | >= 3.6 | |
| tensorboardX | ||
| cupy | ||
| sklearn | ||
| Other common packages. |
输入与输出
代码的输入与输出。如下所示：
| 名称 | 说明 |
|---|---|
| 输入 | RGB图像。大小为256X256（宽x高） |
| 输出 | 异常分数。范围0到1，分数越大异常概率越大 |
运行方式
下载光流预训练模型链接：https://pan.baidu.com/s/1zP8jb3ew1iPSXsTczAdtIQ
提取码：25ro
将其放到/models/liteFlownet文件夹里
下载异常检测预训练模型链接：https://pan.baidu.com/s/19ClStpgDS26KcBccRDiKYg
提取码：5rud
将其放到/weights文件夹里
下载数据集链接：https://pan.baidu.com/s/1j0TEt-2Dw3kcfdX-LCF0YQ
提取码：i9b3
在terminal下运行以下命令。
Train
# Train by default with specified dataset.
python train.py --dataset=avenue
# Train with different batch_size, you might need to tune the learning rate by yourself.
python train.py --dataset=avenue --batch_size=16
# Set the max training iterations.
python train.py --dataset=avenue --iters=80000
# Set the save interval and the validation interval.
python train.py --dataset=avenue --save_interval=2000 --val_interval=2000
# Resume training with the latest trained model or a specified model.
python train.py --dataset=avenue --resume latest [or avenue_10000.pth]
# Train with Flownet2SD instead of lite-flownet.
python train.py --dataset=avenue --flownet=2sd
# Visualize the optic flow during training.
python train.py --dataset=avenue --show_flow
Use tensorboard
tensorboard --logdir=tensorboard_log/ped2_bs4
Evalution
# Validate with a trained model.
python evaluate.py --dataset=ped2 --trained_model=ped2_26000.pth
# Show and save the psnr curve and the difference heatmap between the gt frame and the
# generated frame during evaluating. This drops fps.
python evaluate.py --dataset=ped2 --trained_model=ped2_26000.pth --show_curve --show_heatmap
基于生成对抗网络的异常行为检测
Python