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docs(yolo_v1): 新增文档
1. 训练日志 2. 架构解析 3. 工程介绍
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docs/index.md

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# YOLO_v1
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# YOLO_v1
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实现`YOLO_v1`算法
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## 数据集
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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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```
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{'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
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```
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## 实现流程
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1. 创建训练数据集
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2. 定义`YoLo_v1`模型
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3. 定义损失函数`Multi-part Loss`
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4. 训练模型
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5. 计算`mAP`

docs/log.md

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# 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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## 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: 4.3550
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save model
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Epoch 1/49
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----------
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train Loss: 3.4803
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save model
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Epoch 2/49
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----------
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train Loss: 3.3921
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save model
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Epoch 3/49
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----------
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train Loss: 3.0650
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save model
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Epoch 4/49
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----------
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train Loss: 2.9081
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save model
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Epoch 5/49
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----------
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train Loss: 2.5893
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save model
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Epoch 6/49
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----------
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train Loss: 2.5640
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save model
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Epoch 7/49
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----------
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train Loss: 2.4905
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save model
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Epoch 8/49
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----------
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train Loss: 2.1913
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save model
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Epoch 9/49
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----------
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train Loss: 2.1152
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save model
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Epoch 10/49
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----------
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train Loss: 1.9428
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save model
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Epoch 11/49
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----------
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train Loss: 1.7271
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save model
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Epoch 12/49
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----------
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train Loss: 1.4372
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save model
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Epoch 13/49
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----------
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train Loss: 1.8185
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Epoch 14/49
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----------
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train Loss: 1.5460
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Epoch 15/49
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----------
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train Loss: 1.1692
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save model
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Epoch 16/49
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----------
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train Loss: 1.0540
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save model
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Epoch 17/49
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----------
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train Loss: 0.9028
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save model
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Epoch 18/49
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----------
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train Loss: 0.7702
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save model
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Epoch 19/49
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----------
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train Loss: 0.7176
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save model
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Epoch 20/49
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----------
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train Loss: 0.7485
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Epoch 21/49
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----------
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train Loss: 0.6307
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save model
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Epoch 22/49
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----------
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train Loss: 0.5581
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save model
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Epoch 23/49
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----------
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train Loss: 0.5320
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save model
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Epoch 24/49
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----------
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train Loss: 0.5893
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Epoch 25/49
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----------
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train Loss: 0.5185
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save model
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Epoch 26/49
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----------
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train Loss: 0.6156
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Epoch 27/49
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----------
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train Loss: 0.5096
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save model
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Epoch 28/49
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----------
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train Loss: 0.5403
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Epoch 29/49
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----------
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train Loss: 0.4653
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save model
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Epoch 30/49
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----------
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train Loss: 0.3850
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save model
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Epoch 31/49
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----------
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train Loss: 0.3609
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save model
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Epoch 32/49
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----------
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train Loss: 0.4063
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Epoch 33/49
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----------
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train Loss: 0.3349
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save model
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Epoch 34/49
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----------
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train Loss: 0.2629
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save model
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Epoch 35/49
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----------
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train Loss: 0.3319
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Epoch 36/49
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train Loss: 0.2790
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Epoch 37/49
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train Loss: 0.2487
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save model
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Epoch 38/49
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train Loss: 0.2325
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save model
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Epoch 39/49
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----------
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train Loss: 0.2146
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save model
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Epoch 40/49
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----------
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train Loss: 0.2087
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save model
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Epoch 41/49
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----------
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train Loss: 0.1626
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save model
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Epoch 42/49
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----------
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train Loss: 0.1446
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save model
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Epoch 43/49
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----------
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train Loss: 0.1372
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save model
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Epoch 44/49
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----------
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train Loss: 0.1260
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save model
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Epoch 45/49
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----------
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train Loss: 0.1231
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save model
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Epoch 46/49
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----------
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train Loss: 0.1232
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Epoch 47/49
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----------
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train Loss: 0.1475
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Epoch 48/49
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----------
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train Loss: 0.1226
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save model
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Epoch 49/49
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----------
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train Loss: 0.1000
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save model
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Training complete in 6m 28s
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```
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## 检测结果
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```
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compute mAP
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{'cucumber': 63, 'mushroom': 61, 'eggplant': 62}
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99.43% = cucumber AP
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98.39% = eggplant AP
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99.22% = mushroom AP
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mAP = 99.01%
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```

docs/架构解析.md

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# 架构解析
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使用`Python`实现,相关源码位于`py`目录下
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```
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├── batch_detect.py
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├── data
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├── detector.py
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├── lib
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├── data
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│   ├── __init__.py
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│   └── parse_location.py
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├── __init__.py
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├── models
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│   ├── basic_conv2d.py
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│   ├── __init__.py
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│   ├── location_dataset.py
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│   ├── multi_part_loss.py
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│   ├── __pycache__
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│   └── yolo_v1.py
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├── __pycache__
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├── train.py
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└── utils
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├── draw.py
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├── file.py
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├── __init__.py
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├── __pycache__
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├── util.py
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└── voc_map.py
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└── models
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```
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* 数据集操作:`lib/data/parse_location.py`
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* 模型定义:`lib/models`
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* 训练文件:`lib/train.py`
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* 批量测试:`batch_detect.py`

mkdocs.yml

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- 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
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# 导航
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nav:
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- Home: index.md
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- Home: index.md
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- 架构解析: '架构解析.md'
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- log: log.md

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