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Tensorflow object detection API RCNN is slow on CPU: 1 frame per min


Tensorflow object detection API RCNN is slow on CPU: 1 frame per min

By : SlowBoss
Date : November 21 2020, 03:00 PM
may help you . Hopefully this will help other users choose models. Here is my reported average times for 3.1 Ghz CPU processor on OSX (more info above).
faster_rcnn_inception_resnet_v2_atrous_coco: 45 sec/image
code :


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tensorflow object detection faster rcnn randomly fails

tensorflow object detection faster rcnn randomly fails


By : fdjf
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The Tensorflow Object Detection API assumes that the '0' label is reserved for 'none_of_the_above', so one immediate thing to do is to add 1 to every label index in your label map.
It's unclear why things fail (in a hard way) for Faster R-CNN and not for SSD (probably something for us to dig into) --- but I'd be a bit surprised if you got very good results with SSD using that label map.
Faster RCNN tensorflow object detection API : dealing with big images

Faster RCNN tensorflow object detection API : dealing with big images


By : Hayan Aljoussef
Date : March 29 2020, 07:55 AM
it fixes the issue You need to keep training images and images to test on of roughly same dimension. If you are using random resizing as data augmentation, you can vary the test images by roughly that factor.
Best way to deal with this problem is to crop large image into images of same dimension as used in training and then use Non-maximum suppression on crops to merge the prediction.
Tensorflow Object Detection API Faster-RCNN converges but detection is innacurate

Tensorflow Object Detection API Faster-RCNN converges but detection is innacurate


By : Etherion
Date : March 29 2020, 07:55 AM
Does that help Eventually I gave up the method of placing my logo images onto a random background, instead I manually labelled them, and then used image augmentation to increase my training set size. This seemed to greatly improve my results. I think this has something to do with a contextually accurate background actually being quite important in training.
Hopefully this is helpful to some, thanks for the help.
While training Mask RCNN using TensorFlow Object Detection API, what is the 'loss'?

While training Mask RCNN using TensorFlow Object Detection API, what is the 'loss'?


By : user2331249
Date : March 29 2020, 07:55 AM
it helps some times The loss function of the Mask R-CNN paper combines a weighted sum of 3 losses (the 3 outputs): classification, localization and segmentation mask:
Tensorflow Object Detection API Untrained Faster-RCNN Model

Tensorflow Object Detection API Untrained Faster-RCNN Model


By : ikitty
Date : March 29 2020, 07:55 AM
I hope this helps you . To train your own model from scratch do the following:
Comment out the following lines
code :
    # fine_tune_checkpoint: <YOUR PATH>
    # from_detection_checkpoint: true
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