object-detection

问题 I am trying to detect objects in image using AlexeyAB darknet.But it is detecting only 2 or 3 object.It can't detect small objects(for example hat).I am using this command: ./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg /weight_path/ /image_path/ How can I do it? 回答1: According to the AlexeyAB page for small objects you can do this: for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set layers = -1, 11 instead of https://github.com/AlexeyAB

问题 I am trying to detect objects in image using AlexeyAB darknet.But it is detecting only 2 or 3 object.It can't detect small objects(for example hat).I am using this command: ./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg /weight_path/ /image_path/ How can I do it? 回答1: According to the AlexeyAB page for small objects you can do this: for training for small objects (smaller than 16x16 after the image is resized to 416x416) - set layers = -1, 11 instead of https://github.com/AlexeyAB

问题 I am training tensorflow object detection API model on the custom dataset i.e. License plate dataset. My goal is to deploy this model to the edge device using tensorflow lite so I can't use any RCNN family model . Because, I can't convert any RCNN family object detection model to tensorflow lite model (this is the limitation from tensorflow object detection API). I am using ssd_mobilenet_v2_coco model to train the custom dataset. Following is the code snippet of my config file: model { ssd {

问题 I am trying to use a CNN for object detection using Tensorflow with Keras. I am fairly new to this, so I was using a tutorial as a guide but with my own set and a few other things. The error I get is Tensorflow's incompatible shapes with [x,2] vs. [x], where x is any number of training images I have and 2 is the number of classes. I was using a small number of images just for testing, but I am pretty sure that is not the problem? I have tried different multiples of training images with no

问题 How to find predicted image id and Box from SSD, I am using this GitHub link here is the test function which I want to save the image id and box def test(loader, net, criterion, device): net.eval() running_loss = 0.0 running_regression_loss = 0.0 running_classification_loss = 0.0 num = 0 for _, data in enumerate(loader): images, boxes, labels = data images = images.to(device) boxes = boxes.to(device) labels = labels.to(device) num += 1 with torch.no_grad(): confidence, locations = net(images)

问题 I ask the question because I wanted to solve the error I experienced. I want to use 'SSD lite Mobilenet V2' in Raspberry Pi 3 B+ and NCS(not 2, it is NCS1). So I installed OpenVINO 2019_R3 on my Pi(Raspbian stretch) and Laptop(Linux, not all programs, just Model optimizer). When I optimize SSD lite mobilenet v2(trained zoo model), it was fine. So, i trained my model in Google Colab using Tensorflow object detection api. But when I optimize my own SSD lite model, here is log and what I typed

问题 I ask the question because I wanted to solve the error I experienced. I want to use 'SSD lite Mobilenet V2' in Raspberry Pi 3 B+ and NCS(not 2, it is NCS1). So I installed OpenVINO 2019_R3 on my Pi(Raspbian stretch) and Laptop(Linux, not all programs, just Model optimizer). When I optimize SSD lite mobilenet v2(trained zoo model), it was fine. So, i trained my model in Google Colab using Tensorflow object detection api. But when I optimize my own SSD lite model, here is log and what I typed

问题 I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video. After some research I've figured out that I need to add negative images to the training process. How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here. Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to

问题 I'm training an object detector using tensorflow and the faster_rcnn_inception_v2_coco model and am experiencing a lot of false positives when classifying on a video. After some research I've figured out that I need to add negative images to the training process. How do I add these to tfrecord files? I used the csv to tfrecord file code provided in the tutorial here. Also it seems that ssd has a hard_example_miner in the config that allows to configure this behaviour but this doesn't seem to

问题 I am using to compile and run code from Features2D + Homography to find a known object tutorial, and I am getting this OpenCV Error: Assertion failed (npoints >= 0 && points2.checkVector(2) == npoint s && points1.type() == points2.type()) in unknown function, file c:\Users\vp\wor k\ocv\opencv\modules\calib3d\src\fundam.cpp, line 1062 run-time error. after debugging I find that the program is crashing at findHomography function. Unhandled exception at 0x760ab727 in OpenCVTemplateMatch.exe: