object-detection-api

I am running sample program which comes packaged with Tensorflow object detection API(object_detection_tutorial.ipynb). Program runs fine with no error, but bounding boxes are not diaplayed at all. My environment is as follows: Windows 10 Python 3.6.3 What can be the reason? With regards Manish Please try updated SSD models in the detection zoo : https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md . This should be fixed. It seems that the latest version of the model ssd_mobilenet_v1_coco_2017_11_08 doesn't work and outputs abnormally low

I used tensoflow object detection API to count the number of cars detected. But now i want to calculate the speed of all the cars detected. My question is is there any way to do this using tensorflow object detection API. You have keep track of the location of the cars with respect to pixels in intervals of time. You can start and stop the recording of time using 'time' library. Also, if you are planning to calculate car speeds, you have to take the relative speed with respect to the camera movement. It would be easier if your camera is in a still position. 来源： https://stackoverflow.com

Im trying to train a model to check images, identify specified objects and tell me its coodinates (i dont even need to see an square around the object). For this im using Tensorflow's object detection and most of what I did was looking this tutorial: How To Train an Object Detection Classifier for Multiple Objects Using TensorFlow (GPU) on Windows 10 But some things changed, probably because of updates, and then I had to do somethings on my own. I can actually train the model (I guess) but I don't understand the evaluation results. Im used to see loss and current step but this output is

I have trained a faster rcnn model with a custom dataset using Tensorflow's Object Detection Api. Over time I would like to continue to update the model with additional images (collected weekly). The goal is to optimize for accuracy and to weight newer images over time. Here are a few alternatives: Add images to previous dataset and train a completely new model Add images to previous dataset and continue training previous model New dataset with just new images and continue training previous model Here are my thoughts: option 1: would be more time consuming, but all images would be treated

This is a follow-up question from this Github issue . To cut a long story short, I tried to use the Tensorflow Object detection API with my own dataset. Everything was working just fine until all of a sudden it crashed with the following error messages : ... INFO:tensorflow:global step 10635: loss = 0.3392 (0.822 sec/step) INFO:tensorflow:global step 10636: loss = 0.3529 (0.823 sec/step) INFO:tensorflow:global step 10637: loss = 0.3305 (0.831 sec/step) 2017-09-14 20:02:02.545415: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\framework\op_kernel.cc:1192] Invalid