Empty Detections

[mtlouie-unm]

When executing test_frcnn.py it seems that I pass in the path to where my test images are located, but I get zero detections when testing the model that was successfully trained. Wouldn't the testing phase need labeled data to see how well the model detects?

[kentaroy47]

right now, test.py just generates images with detection results. (with --write options).
I guess you want to see the calculated mAP? I will work with that feature so plz wait..

[mtlouie-unm]

Thanks for responding.

Oh it seems that the network is classifying each of the test images as simply background (bg). Am I supposed to give some training images of just background?

If so, in order to give the network to train on images of just background would my simple data text file look like this:

/data/imgs/img_001.jpg,837,346,981,456,cow
/data/imgs/img_002.jpg,215,312,279,391,cat
/data/imgs/img_002.jpg,22,5,89,84,bird
/data/imgs/img_003.jpg,,,,,

Where the last line (/data/imgs/img_003.jpg,,,,,) would be an example of background.

[franyoadam]

Hi,

I have same problem, the training results seemed good but all test images are empty detection. If I tested on train dataset, the result was same. Have you managed to fix this?

[kentaroy47]

@mtlouie-unm @franyoadam
I think this issue is fixed by the past commits.
Pulling the newest git should pass this issue,

git pull

[tossy-yossy]

I have the same problem.
I am using the latest code, but all images are empty detection.

[kentaroy47]

@tossy-yossy
Yes, I still found this issue when trained with pascal2007 images.
since it was working previously, let me revert my enviroment (tf+keras) back and check if it cures.
if it's urgent, I recommend using pytorch object detection repos which supports coco training as well.
https://github.com/jwyang/faster-rcnn.pytorch
https://github.com/kentaroy47/ObjectDetection.Pytorch

(I have a paper submission this week and get back after that's finished)

[kentaroy47]

@tossy-yossy @mtlouie-unm @franyoadam
This issues have been fixed.
The cause was the positive-negative ratio of the detections.
I fixed the number of RPNs so that the postive-negative ratio will be about 1:3, as in other implementations. Please pull the newest version to activate this.

The training will be stable with using pretrained RPN models.

The pretrained RPN for VGG is uploaded to:
https://drive.google.com/file/d/1teuXIRN4mvmbnIfWlxAAM69hJEceTpUm/view?usp=sharing
The trained vgg frcnn model is uploaded (is underfitting..):
https://drive.google.com/file/d/1IgxPP0aI5pxyPHVSM2ZJjN1p9dtE4_64/view?usp=sharing

Here is the example command.

python train_frcnn.py --network vgg -p to/your/voc --load rpn-mode.pth

[ianstath]

@tossy-yossy @mtlouie-unm @franyoadam
This issues have been fixed.
The cause was the positive-negative ratio of the detections.
I fixed the number of RPNs so that the postive-negative ratio will be about 1:3, as in other implementations. Please pull the newest version to activate this.

The training will be stable with using pretrained RPN models.

The pretrained RPN for VGG is uploaded to:
https://drive.google.com/file/d/1teuXIRN4mvmbnIfWlxAAM69hJEceTpUm/view?usp=sharing
The trained vgg frcnn model is uploaded (is underfitting..):
https://drive.google.com/file/d/1IgxPP0aI5pxyPHVSM2ZJjN1p9dtE4_64/view?usp=sharing

Here is the example command.

python train_frcnn.py --network vgg -p to/your/voc --load rpn-mode.pth

Hello,
I have the same problem. I trained the whole network (i didnt pretrained the rpn) with mobilenetv2.
Should I change something at test.py or train.py? Any Ideas? I pulled the latest code

[kentaroy47]

@ianstath
if the mean object per image is under 2 during training, you should pretrain the rpn and use it to train frcnn. or you may train with option -n 6, which will reject more negative proposals.
I haven't tried pascal_voc with mobilenetv2, but I'm thinking that is the case..

[ianstath]

@ianstath
if the mean object per image is under 2 during training, you should pretrain the rpn and use it to train frcnn. or you may train with option -n 6, which will reject more negative proposals.
I haven't tried pascal_voc with mobilenetv2, but I'm thinking that is the case..

mean object per image? you mean one finding per image?Yes, in most o the Images, I have only one object to be detected.
Train with -n 6 the rpn or the whole? Now the default is -n 10. Will it make a difference? What if I trained only the mobilenet? Also, I am using my own dataset of images( images of brain MRI with tumors)

Also, I thought that -n 6 controls the size of the batch. If not, how I control the batch size in order not to run out of memory

[kentaroy47]

@ianstath

python train_frcnn.py --network mobilenetv2  -p ../VOCdevkit/ -n 6

may help. I haven't tried on mobilenet so would help if you can.
Thanks.