我发现examples/retinaface.cpp中，如果开启OMP加速的话似乎在检测到人脸时会发生内存泄漏，但我定位不了这个问题的具体原因。

值得注意的时，如果将qsort_descent_inplace函数中的OMP指令注释掉这个问题就会消失掉。

static void qsort_descent_inplace(std::vector<FaceObject>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;
    ...
    // #pragma omp parallel sections
    {
        // #pragma

I created a virtual environment using conda and I installed darkflow and it worked pretty well. I later exported the environment to try it on another computer to verify that everything works. But conda doesn't install the darkflow even though it exists on the requirement.txt

To get started using this repo quickly using a Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) follow the instructions below. New GCP users are eligible for a $300 free credit offer. Other quickstart options for this repo include our [Google Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov3/blob

Platform (like ubuntu 16.04/win10): Windows 10
Python version: 3.7.4, mmdnn==0.2.5

Running scripts: mmconvert -f caffe -df keras -om test

I know that this command is not supposed to run without passing an input file, but the error message is incorrect and should be improved:

mmconvert: error: argument --srcFramework/-f: invalid choice: 'None' (choose from 'caffe', 'caffe2', 'cn

hi, @Cartucho thanks for you contribution, it do help me a lot with understanding the criteria metric of PASCOL VOC 2012!

I have a similar question to #93 but not exact same. As you provided curve in the readme file:

Considering the first prediction entry is false positive, thus:

I stumbled upon this repo https://github.com/dataplayer12/homesecurity/ which uses the jetson for CCTV system.
Idea: use a raspberry pi zero with a pi-camera-module could be a simple solution for uses cases like: Hardware inside and camera module outside.

• more secure since the pi only costs a fraction of the jetson
• no cable connection between board and camera
• option to connect more than

I see you have added gaussian_yolov3_layer and yolo detection gaussian_box.(from this paper https://arxiv.org/abs/1904.04620)

can you provide a wiki or same example prototxts to explain how to use gaussian yolo correctly

thank you very much

I find run.py is updated.

-# FLAGS.model = "darkflow/cfg/yolo.cfg" # tensorflow model
-# FLAGS.load = "darkflow/bin/yolo.weights" # tensorflow weights
-FLAGS.pbLoad = "tiny-yolo-voc-traffic.pb" # tensorflow model
-FLAGS.metaLoad = "tiny-yolo-voc-traffic.meta" # tensorflow weights

could you share the file of tiny-yolo-voc-traffic.pb and tiny-yolo-voc-traffic.meta ?
thank you very much!

http://zlal32teyptf4tvi.onion/path/foobar

If a search for a path is performed which results in no matches being found, the 404 message has "\n" all over the place.

Mac OSX 10.14.1 (18B75)

Traceback (most recent call last):
File "yolo_opencv.py", line 98, in
draw_prediction(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
File "yolo_opencv.py", line 39, in draw_prediction
cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
TypeError: only length-1 arrays can be converted to Python scalars