VOC2007trainval+VOC2012trainval用於訓練

[1451595897]

应该是测试2000张图片是80%. 4952张图片如下，现场跑的：

model restore from : /home/yanghe/YangHe_MyCode/FPN_Tensorflow-master/output/trained_weights/FPN_Res101_v1/voc_80000model.ckpt
2018-12-11 22:16:40.830512: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:897] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-12-11 22:16:40.830848: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1392] Found device 0 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
totalMemory: 10.92GiB freeMemory: 10.36GiB
2018-12-11 22:16:40.830859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1471] Adding visible gpu devices: 0
2018-12-11 22:16:41.007018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-12-11 22:16:41.007044: I tensorflow/core/common_runtime/gpu/gpu_device.cc:958]      0 
2018-12-11 22:16:41.007049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   N 
2018-12-11 22:16:41.007183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10017 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
restore model
003607.jpg image cost 0.10984158515930176s:[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>]100%	4952/4952Writing aeroplane VOC resutls file
Writing bicycle VOC resutls file
Writing bird VOC resutls file
Writing boat VOC resutls file
Writing bottle VOC resutls file
Writing bus VOC resutls file
Writing car VOC resutls file
Writing cat VOC resutls file
Writing chair VOC resutls file
Writing cow VOC resutls file
Writing diningtable VOC resutls file
Writing dog VOC resutls file
Writing horse VOC resutls file
Writing motorbike VOC resutls file
Writing person VOC resutls file
Writing pottedplant VOC resutls file
Writing sheep VOC resutls file
Writing sofa VOC resutls file
Writing train VOC resutls file
Writing tvmonitor VOC resutls file
cls : aeroplane|| Recall: 0.9368421052631579 || Precison: 0.0006888900356055524|| AP: 0.8316195907441402
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cls : bicycle|| Recall: 0.9317507418397626 || Precison: 0.0008097061591820421|| AP: 0.8428989611072188
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cls : bird|| Recall: 0.9564270152505446 || Precison: 0.0011153908695475427|| AP: 0.8564636847169369
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cls : boat|| Recall: 0.9239543726235742 || Precison: 0.0006345525621038943|| AP: 0.6681021871004866
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cls : bottle|| Recall: 0.9040511727078892 || Precison: 0.0010303391127905422|| AP: 0.6836585228090871
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cls : bus|| Recall: 0.971830985915493 || Precison: 0.000545292179140335|| AP: 0.893992976333262
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cls : car|| Recall: 0.9592006661115737 || Precison: 0.002975037575344376|| AP: 0.8971617581902198
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cls : cat|| Recall: 0.9776536312849162 || Precison: 0.0008884534056957478|| AP: 0.9199375930964409
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cls : chair|| Recall: 0.8783068783068783 || Precison: 0.001715261435291504|| AP: 0.6108782073953818
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cls : cow|| Recall: 0.9754098360655737 || Precison: 0.000614017662037455|| AP: 0.7791710473975842
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cls : diningtable|| Recall: 0.9320388349514563 || Precison: 0.00048727625264258543|| AP: 0.6986196556015922
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cls : dog|| Recall: 0.983640081799591 || Precison: 0.001226562081636504|| AP: 0.9059106898861442
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cls : horse|| Recall: 0.9683908045977011 || Precison: 0.0008725303120137326|| AP: 0.8889594199400792
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cls : motorbike|| Recall: 0.9384615384615385 || Precison: 0.0007925062685946655|| AP: 0.8489529517343617
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cls : person|| Recall: 0.9293286219081273 || Precison: 0.010406388256212797|| AP: 0.8462024604502763
____________________
cls : pottedplant|| Recall: 0.8541666666666666 || Precison: 0.00103803512609595|| AP: 0.5071455263152422
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cls : sheep|| Recall: 0.9504132231404959 || Precison: 0.0005689267073985208|| AP: 0.8354602699815161
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cls : sofa|| Recall: 0.9623430962343096 || Precison: 0.0006337869043091998|| AP: 0.786707445700414
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cls : train|| Recall: 0.9432624113475178 || Precison: 0.000724355500608622|| AP: 0.865695670375713
____________________
cls : tvmonitor|| Recall: 0.961038961038961 || Precison: 0.0007615832698680609|| AP: 0.7962795325532337
____________________
mAP is : 0.7981909075714665

我使用2007和2012的训练集和验证集进行训练，使用了预训练模型。

提交结点这里

Originally posted by @yanghedada in #46 (comment)

你好,我嘗試用VOC2007trainval+VOC2012trainval來生成tfrecord訓練的時候,會出現rpn_loc_loss:nan的情況,這是數據集上的問題呢還是通過修改代碼就可以解決了?想請教一下你的!

[yanghedada]

@1451595897, 你好，

1. 你可以查看FPN_TensorFlow-master_old\data\io\convert_data_to_tfrecord.py 的# line 74

gtbox_label = np.transpose(np.stack([ymin, xmin, ymax, xmax, label], axis=0))  # [ymin, xmin, ymax, xmax, label]

的[ymin, xmin, ymax, xmax, label]顺序。
2. 是否使用预训练的参数？没有使用预训练参数，可能会出现loss==nan。

[1451595897]

你好,我是有用預訓練參數去訓練的.我用的是新的convert_data_to_tfrecord.py轉成tfrecord文件的,你用原來的convert_data_to_tfrecord_raw.py文件去轉就不會出現這個問題對吧?因爲我看了一下,兩個文件這四個坐標的順序的確是不一樣的! @yanghedada @yangxue0827

[yangxue0827]

这个代码是xmin ymin xmax ymax 的顺序 @1451595897