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  • validation accuracy

    2020-12-27 13:19:07
    I have trained and tested the program successfully, and the validation is very good (always 99.5%, even if with only 100 steps), but when I run the label_image.py, the result is very bad: almost all ...
  • validation accuracy vs train accuracy

    千次阅读 2018-11-18 23:00:08
    训练时validation accuracy和train accuracy之间没有差距,本义为这是一个还不错的曲线,但是今天讨论时有人评论说这种情况说明网络参数不足,因为在参数充足的情况下多多少少会有过拟和,所以正常情况下train ...

    训练时validation accuracy和train accuracy之间没有差距,本义为这是一个还不错的曲线,但是今天讨论时有人评论说这种情况说明网络参数不足,因为在参数充足的情况下多多少少会有过拟和,所以正常情况下train accuracy会高于validiation accuracy,似乎很有道理!

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  • <div><p>In running the sample code, I found that the training accuracy is much lower than the validation accuracy, which is different from training GraphSAGE on reddit in their repo. Is this normal. ...
  • Lower validation accuracy

    2020-12-01 14:53:33
    When I use your uploaded <code>VOC12_scenes_20000.pth</code> model to evaluate the single scale model on 1449 validation data, I get 71.8% which is much lower than reported 74.0%. Moreover, when I try...
  • Validation of accuracy

    2020-12-09 07:20:42
    <div><p>In the following section: <code>Open Standards > Benefits > Interoperability > Cloud computing</code></p> <p>I don't feel that the following statement is accurate. ...
  • Too bad validation accuracy

    2021-01-10 13:03:48
    t freeze any layer of it, after 40 epochs iteration, it still get too bad validation accuracy in my validation datasets, just as the following picture: so, I just want to know whether is's nornal...
  • 关于facenet 的accuracyvalidation rate 理解

    千次阅读 热门讨论 2018-05-09 20:10:58
    使用davidsandberg大侠的facenet 代码时, https://github.com/davidsandberg/facenet , 对于训练结果评价,提示类似如下:Accuracy: 0.99650+-0.00252Validation rate: 0.98367+-0.00948 @ FAR=0.00100 可以看到有...

     

     


    使用davidsandberg大侠的facenet  代码时, https://github.com/davidsandberg/facenet , 对于训练结果评价,提示类似如下:
    Accuracy: 0.99650+-0.00252
    Validation rate: 0.98367+-0.00948 @ FAR=0.00100


    可以看到有两个评价方法,accracy 和 validation rate。 我对 accracy 和 val rate的计算方式是明白的,但中间的逻辑(为什么要这样算) 有疑问,很长时间都没弄明白..
    首先  计算方式如下:
      accuracy              =(判断正确的 同一个人+ 判断正确的 不同的人) / 所有数据     
      validation rate     =  判断正确的同一个人的数量 / 所有的同一个人 的数量        (这时有个数字 代码写死为0.001,后面细说)   
    但为什么要有两个指标评价? accuracy不就够了么? 而且貌似val rate比accuracy 更重要,更能反映训练结果的好坏。

     

     

     

     

     

    开始时,我的训练的结果是acc 90%, val rate 10%, 后来提升到了 acc 98%, val rate 60%

    我的疑问/思路,举个例子来说 当acc 90%, val rate 10%时:

    设所有数据是100份,  现在我的accuraccy为90%,则 (判断正确 同一个人+ 判断正确 不同的人 )总和为90人,

    假设同一人的情况 实际数量为50:10%val则 5个判断对,则判断正确不同的人需要90-5=85个,  50+85>100, 不可能
    假设同一人的情况 实际数量为80:10%val则 8个判断对,则判断正确不同的人需要90-8=72个,  80+72>100, 不可能
    假设同一人的情况 实际数量为10:10%val则 1个判断对,则判断正确不同的人需要90-1=89个,  10+89约等于100,有可能

    而最后一种就是不平衡数据集,   而我的测试集是比较平衡的,不会100组数据中只有10个人左右是相同的 。 问题到底在哪呢?


    先看/理解好 知乎这条问题:

    https://www.zhihu.com/question/30750849

     

    现在看垂直的这条分界线 也就是我们最后要设定的判断阈值,把交叉的部分分成了假正 假负 两块区域。

    现在把 误报率 也就是假正 ,变为很小的0.001,就是要把蓝色的线 移到靠近右边的虚线。(把假正降到很小很小的0.001)
    而这时,validtateion rate不高,就说明我们训练的结果 还不够分得开,交叉的地方太大了:假正假负都太多, 我们需要 假正假负都尽量小

    如果我们的是模型训练得好(类似下图,交叉的地方很小),误报率 即使设为很低的0.001, val rate也可以高。下图是我们的训练目标, 上图是我们还没训练得足够好的情况。

     

    所以,与不平衡数据集没关系,是我们的网络训练得不够。


    那为什么模型训练得不好,accuracy仍可达90%或更高以上呢? 我想像中的,只有达到上图的效果了,accuracy才会达到99%, 实际下图这种情况,也可以达到accuracy99%, validation rate 60%。

    另外红线和蓝线,分别就是accuracy和validation rate 的门限值设置方法。也就是为什么有两个评价标准,且val rate比accuracy 更重要,更能反映训练结果的好坏。

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  • In detail, after some epoches of training, resnet50 and se-resnext dygraph training accuracy improve as expected, but validation accuracy is much lower than training accuracy. <p>We do not understand...
  • Validation accuracy is 0.000

    2020-11-23 00:54:29
    <p>When running train.py I get good accuracy on train data but 0.000 accuracy on validation data. At the end all precision, recall and f-1 score is all 0. <p>Could you please address this problem. ...
  • <p>I work pretty regularly with PyTorch and ResNet-50 and was surprised to see the ResNet-50 have only 75.02% validation accuracy. When I use the pretrained ResNet-50 using the code ...
  • <div><p>训练参数为batch size=...而validation acc突然减小至不到0.1且随着训练validation acc会继续下降。请问我的训练参数该如何调整?</p><p>该提问来源于开源项目:MVIG-SJTU/AlphaPose</p></div>
  • <div><p>Using the code below, we see that accuracy improves on training data but not on validation. Here I used a subset (891 training images. 110 validation) of the Carvana Kaggle Image Masking ...
  • However, the experiments on ImageNet with AlexNet achieved about 3% higher accuracy on the ImageNet validation set than the published results. <p><code>supervised</code>: 59.48 (59.70 in the paper) ...
  • <div><p>Hello , I am trying to train the network on NVIDIA Titan RTX 24 GB GPU, I find that both val_loss and val_acc are increasing, while train_loss is reducing and train_acc is increasing as ...
  • <div><p>Caffe has support wherein if you provide 2 top output blobs for the final accuracy layer, it displays a per-class accuracy as well during the testing phase. <p>Is it possible to get something ...
  • 知乎:深度学习为什么会出现validation accuracy大于train accuracy的现象? 概括他们的答案: validation的样本数量一般远小于training的 val的时候是用已经训练了一个epoch的model进行测试的(经过大量的训练学习...

    知乎:深度学习为什么会出现validation accuracy大于train accuracy的现象?

    概括他们的答案:

    1. validation的样本数量一般远小于training的
    2. val的时候是用已经训练了一个epoch的model进行测试的(经过大量的训练学习到东西了)
    3. data augmentation把训练集变得丰富,制造数据的多样性和学习的困难来让network更robust(比如旋转,随机crop,scale),但是val和test的时候一般是不对数据进行data augmentation的
    4. 各种正则化,dropout在训练集上使用,却不会在验证集上使用,导致训练集的 loss 偏大
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  • For training from scratch, fine-tuning and parallel adapter training, the validation accuracy for the aircraft data set I get is 38%, 50%, 44%, however the published test accuracy are 57.10%, 60.34%,...
  • presenting the DNN accuracy. We can also predict the sequence using beam search and get a CER report in the stage "Decoding". However, A high classification accuracy in DNN but a low ASR ...
  • The validation mIoU of VOC_scenes_20000.pth is 71.1. Is this degradation due to randomness? Could you give me any comments? Here is the parameter I used: <p>BATCH_SIZE = 10 DATA_DIRECTORY = &...
  • ve managed to get to over 80% training accuracy while the validation accuracy stays below 20% and is unfortunately seems to be random and nonmonotonic. <p>I think there's an issue with how you are...
  • Am I misunderstanding the graphs reporting validation accuracy on the training job page? (I've attached a sample graph.) <p><img alt="reported+accuracy" src=...
  • <p>The mIoU results that I achieve are at best around 62% mIoU on the Cityscapes validation set for training from scratch. Now I am wondering if I am missing something during training since your ...
  • <div><p>I ran the training script ...Accuracy : 89.38 Error : 10.62 <p>Which seems very low compared to the 94.74% in the readme and paper.</p><p>该提问来源于开源项目:titu1994/DenseNet</p></div>
  • Validation-accuracy = nan ?

    2019-02-20 13:50:23
  • <p>However, using the <code>lorra_best.pth</code> checkpoint achieves only <code>15.60%</code> vqa accuracy. All the code is the same as master, I am using the below command to run inference: <pre>...
  • <div><p>I have added a model which reaches 83% accuracy on validation set. By default the LSTM model will be chosen. There is a command line parameter called model_choice which switches to the second ...
  • I have trained Resnet-20 on cifar-10 using KFAC several times and all the experiments reveals that although KFAC converges faster, but the final validation accuracy is not as good as momentum-...
  • <div><p>As with #5, the validation accuracy for the large model is also well below the stated. I was curious because the stated result, beating the official, with 1.4m less parameters would be ...
  • ] - 252s 492ms/step - loss: 0.1199 - accuracy: 0.9728 - val_loss: 7.3933e-05 - val_accuracy: 1.0000 saved /content/checkpoints.model.0 Finished Epoch 0 <p>I have high accuracies but bad prediction. I ...
  • when I try to apply the pre-train model(resnext-101-64f-kinetics.pth)on the validation set from Kinetic dataset, the accuracy turns out to be very low(much like random prediction). I have check the ...

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