训练自己的数据，mAP很低

[xugc98]

作者您好，感谢你的工作。 我使用efficientdet-d3对其他数据集进行训练，效果比较差，可以请您帮忙分析一下吗？谢谢！ 数据集是铝型材表面缺陷检测数据。 前30个epoch冻结主干，之后解冻训练，训练到val_loss不再降低。 map结果如下： `Get map.

2.78% = 不导电 AP || score_threhold=0.5 : F1=0.00 ; Recall=0.00% ; Precision=0.00%

0.00% = 喷流 AP || score_threhold=0.5 : F1=0.00 ; Recall=0.00% ; Precision=0.00%

0.00% = 擦花 AP || score_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%

38.01% = 杂色 AP || score_threhold=0.5 : F1=0.11 ; Recall=5.56% ; Precision=100.00%

13.00% = 桔皮 AP || score_threhold=0.5 : F1=0.00 ; Recall=0.00% ; Precision=0.00%

0.00% = 漆泡 AP || score_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%

0.00% = 漏底 AP || score_threhold=0.5 : F1=0.00 ; Recall=0.00% ; Precision=0.00%

5.01% = 脏点 AP || score_threhold=0.5 : F1=0.14 ; Recall=10.84% ; Precision=21.43%

0.00% = 角位漏底 AP || score_threhold=0.5 : F1=0.00% ; Recall=0.00% ; Precision=0.00%

0.00% = 起坑 AP || score_threhold=0.5 : F1=0.00 ; Recall=0.00% ; Precision=0.00%

mAP = 5.88%

Get map done.`

每个epoch的val_loss如下： 692.5821533203125 18.020043546503242 6.0580932082551895 2.9250449375672773 1.7247823729659573 1.1773547078623916 0.8997215122887583 0.7432047751816836 0.6556733747323354 0.5967527682130987 0.5552684529261156 0.5247120744351185 0.5120401086680817 0.490136006564805 0.48692420892643207 0.4789598101016247 0.46626631509174 0.47130427938519104 0.46737760531179834 0.4521835113339352 0.4599695607568278 0.45360165202256403 0.44911260993191693 0.4493762557253693 0.4430947389566537 0.44133371464682347 0.44138025108611945 0.44033303834272153 0.4294479764772184 0.4288688725368543 0.43481780243898505 0.4259181917825742 0.4300796524691048 0.41619802549926205 0.41009346857222156 0.4145459183561268 0.4220570447618392 0.4012312131530758 0.3930108847735978 0.4246373758998825 0.38846801557758853 0.39046983073340424 0.5298791383462611 0.3835990623251271 0.424211601240199 0.40973395342702296 0.374117885035143 0.3753899414537113 0.3762250279924318 0.3619030268668239 0.4167037764147146 0.37196493557473614 0.3779459266798265 0.3777198547691996 0.3685086570235331 0.3662413968635139 0.3724715964062445 0.37364781386594276 0.3646606824068881 0.40380949271259026 0.36690836080085876 0.39359222296903384 0.35407807564001476 0.35932715515147395 0.35652801939355794 0.3581762860126015 0.3570717303777364 0.3549506452712995 0.3696301862208256 0.36184008566857273 0.35130936827566195 0.35368453527786836 0.36518700056667647 0.34808981838399794 0.3540204591326304 0.35394072493732864 0.3597586819651856 0.35117012387447394 0.35453211001829427 0.338129321863847 0.354188849065286 0.3480956403733189 0.3560672045421244 0.3494431595665528 0.3579848694489963 0.3562058041344828 0.3504434914620065 0.36181518032368437 0.3520742502730729 0.3408385811568196 0.3392267982239154 0.34833704064419463 0.3375129512330489 0.34490444361051514 0.3474811746168937 0.3642430662997623 0.3400071593029286 0.3533157893359216 0.34791290949084863 0.35537530932186256 0.3504680647000448 0.3470999870949717 0.3505480893011858 0.351605375561474 0.35297540341740224 0.3379963515743391 0.34117161613235725 0.3530546065364311 0.35188829584686615 0.35485441400322004 0.3438295838492575 0.3458844947058763 0.35485429858872247 0.3565744514674393 0.34367825498165033 0.34764408359109467 0.35074018681449676 0.3437748175225596 0.34253188953804437 0.3441715170976831 0.3461703913870142 0.34832563582084963 0.3480878883222146 0.34780260700899274 0.3481335105068648 0.3435267209172694 0.34988239888491024 0.35219536335277024 0.3490558216876503 0.34662854827162043 0.3428226285961582 0.3569837624985558 0.34416547353699134 0.34747738218796786 0.34722422989113116 0.34134776695673147 0.343578678819893 0.3511959823654659 0.3519623815012512 0.34963406944897635 0.3476591583118955 0.34318768255301374 0.3484093218819419 0.35494244101443395 0.3509057753010472 0.3456782892957997 0.3371015375118647 0.3482280739708178 0.3487955643447922 0.3454236375140165 0.35292010598663076 0.3519064793780224 0.3401252330461545 0.3439494139278558 0.34353317512171483 0.34736122029708394 0.3405051428491055 0.34928369027242734 0.34589640301332547 0.34704446495135327 0.348259352842596 0.34758604331803855 0.34305048622746964 0.3531607194428346 0.3395370377311066 0.3502034348950012 0.3446665857988062 0.3422466347605657 0.34934172028703475 0.35288102302088664 0.35932373247151056 0.3504922179255023 0.3531327823093578 0.3520925745868416 0.3523084268029501 0.34706903154503055 0.35040349913622015 0.3543376238432838 0.3538556429766007 0.34093494554842585 0.3473847135901451 0.3451451262764966 0.34536995963930195 0.3503570426533471 0.34914408012557385 0.3532811352399303 0.34506166659629167 0.3482327019489968 0.3509918149393886 0.3524799474806928 0.35067986360570386 0.3517061372134668 0.3485593108543709 0.3451726289827432 0.34738497355424647 0.34392267045801256 0.3420782624674377 0.33875321841506817 0.347360303236255 0.3521433267186382 0.3485356454473378 0.34775876684753754 0.3512924103094126 0.3482155179632689 0.3382312229264583 0.35628125399573524 0.34736081468525215 0.3492826893369653 0.3421760888964827 0.3456490655610366 0.34405294005105747 0.34908889931862924 0.34774825335549775 0.35118296286508216 0.3519998987886443 0.3402652802474018 0.34220543765087624 0.34587571233399766

[xugc98]

刚检查代码发现是数据bbox格式转换的bug o(╥﹏╥)o

[bubbliiiing]

啊这。加油