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docs(mkdocs): 分别使用location dataset和voc 07数据集进行测试
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docs/index.md

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4. 训练模型
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5. 计算`mAP`
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`50`轮训练完成后能够实现`97.31%``mAP`
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对于`Location DataSet`, `50`轮训练完成后能够实现`97.31% mAP`
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对于`VOC 07``50`轮训练完成后能够实现`xxx mAP`以及`xxx FPS`
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## 相关链接
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docs/log.md renamed to docs/location-log.md

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# 日志
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# 定位数据集训练日志
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## 训练参数
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docs/voc-07-log.md

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# 07 VOC训练日志
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## 训练参数
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* `S=7, B=2, C=3`
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* 缩放至`(448, 448)`,进行数据标准化处理
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* 优化器:`SGD`,学习率`1e-3`,动量大小`0.9`
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* 衰减器:每隔`4`轮衰减`4%`,学习因子`0.96`
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## 检测结果
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```
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。。。
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```
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## 训练日志
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```
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$ python train.py
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Epoch 0/49
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----------
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train Loss: 9.3544
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save model
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Epoch 1/49
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----------
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train Loss: 7.4459
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save model
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Epoch 2/49
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----------
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train Loss: 7.1511
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save model
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Epoch 3/49
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----------
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train Loss: 6.9450
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save model
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Epoch 4/49
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----------
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train Loss: 6.7656
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save model
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Epoch 5/49
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train Loss: 6.6077
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save model
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Epoch 6/49
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train Loss: 6.4264
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save model
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Epoch 7/49
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train Loss: 6.2750
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save model
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Epoch 8/49
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train Loss: 6.0318
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save model
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Epoch 9/49
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train Loss: 5.7777
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save model
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Epoch 10/49
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train Loss: 5.4760
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save model
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Epoch 11/49
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train Loss: 5.1784
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save model
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Epoch 12/49
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train Loss: 4.8067
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save model
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Epoch 13/49
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train Loss: 4.4603
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save model
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Epoch 14/49
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train Loss: 4.1291
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save model
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Epoch 15/49
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train Loss: 3.7810
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save model
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Epoch 16/49
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train Loss: 3.4259
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save model
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Epoch 17/49
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train Loss: 3.1166
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save model
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Epoch 18/49
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train Loss: 2.8041
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save model
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Epoch 19/49
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train Loss: 2.4853
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save model
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Epoch 20/49
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train Loss: 2.2107
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save model
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Epoch 21/49
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train Loss: 1.9424
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save model
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Epoch 22/49
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train Loss: 1.7307
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save model
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Epoch 23/49
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train Loss: 1.5518
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save model
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Epoch 24/49
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train Loss: 1.3712
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save model
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Epoch 25/49
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train Loss: 1.2174
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save model
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Epoch 26/49
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train Loss: 1.1142
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save model
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Epoch 27/49
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train Loss: 1.0305
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save model
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Epoch 28/49
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train Loss: 0.9270
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save model
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Epoch 29/49
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train Loss: 0.8465
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save model
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Epoch 30/49
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train Loss: 0.7739
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save model
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Epoch 31/49
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train Loss: 0.7228
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save model
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Epoch 32/49
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train Loss: 0.6765
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Epoch 33/49
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train Loss: 0.6264
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Epoch 34/49
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train Loss: 0.5932
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save model
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Epoch 35/49
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train Loss: 0.5646
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Epoch 36/49
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train Loss: 0.5359
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Epoch 37/49
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train Loss: 0.4941
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Epoch 38/49
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train Loss: 0.4682
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save model
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Epoch 39/49
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train Loss: 0.4482
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save model
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Epoch 40/49
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train Loss: 0.4276
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Epoch 41/49
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train Loss: 0.4048
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Epoch 42/49
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train Loss: 0.3874
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Epoch 43/49
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train Loss: 0.3759
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Epoch 44/49
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train Loss: 0.3607
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Epoch 45/49
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train Loss: 0.3415
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save model
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Epoch 46/49
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train Loss: 0.3327
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save model
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Epoch 47/49
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train Loss: 0.3222
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save model
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Epoch 48/49
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train Loss: 0.3141
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save model
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Epoch 49/49
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train Loss: 0.2978
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save model
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Training complete in 214m 30s
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```

docs/数据集.md

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# 数据集
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当前使用`3`类定位数据集,参考[[数据集]Image Localization Dataset](https://blog.zhujian.life/posts/a2d65e1.html)
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当前使用了两种数据集进行测试:
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* 定位数据集(`3`类)
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* `VOC 07`数据集(`20`类)
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## 定位数据集
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参考[[数据集]Image Localization Dataset](https://blog.zhujian.life/posts/a2d65e1.html)
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```
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{'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
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```
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## 解析
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### 解析
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下载数据集后,解压到`py/data`目录,得到`training_images`,其格式如下:
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├── cucumber_11.jpg
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。。。
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。。。
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```
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```
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## VOC 07
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。。。

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