darknet | Convolutional Neural Networks | Machine Learning library

I am trying to determine if certain parts of an RGB image are colored or grayscale, using python, opencv and numpy libraries. To be more spesific, in an RGB image I determine face locations using neural networks and when that image contains printed photos I would like to find out if the face location in that image is grayscale or colored.

What I tried so far:

This is my first YOLOv3 project to detect objects with 2 classes. In the resuls I am seeing plenty of detections. Is it possible to show only those boxes which has more than 70% (for example) accuracy or lets say top 3 boxes for each class? Would somebody please help?

my test is executed as follows: !./darknet detector test data/obj.data cfg/yolov3_custom.cfg ~/yolov3_custom_last.weights /content/5.png

Actually in darknet yolov3 model has coco.names file for labels which include 80 classes. Now if I want to train a custom model with two labels only, where one label is already there in coco.names and another is not there.

For example I want to train a model to detect for cell phone and dslr camera, so cell phone class already exist in coco.names whereas dslr camera is not there in its labels file.

So can I train custom model using two classes cell phone and dslr camera and give data of only dslr camera for training and it will predict for both dslr camera and cell phone or shall I train with both data of cell phone and dslr images or is there any other way out.

I am a bit new to ML, so any help would be great Thanks

I have done the model training for Yolov4 objection detection from AlexeyAB Darknet package on Colab. (from https://github.com/AlexeyAB/darknet) Two classes are defined named "scratch" and "blood". Then run the following commands for testing on a single image:

!./darknet detector test data/obj.data cfg/yolo-obj.cfg backup/yolo-object_last.weights images/meat/test/376.jpg -dont show

and the result is shown below. It's expected that there are only one scratch and blood shown with probabilities in the result. However, it shows so many scratch and blood predictions (the last few lines of this post)! The number of classes (=2), class names, and shoud have been set properly in obj.data and yolo-obj.cfg. Can anyone advice why and how to resolve it?

I Am Doing Implementation Of Yolo v3 for multi class object detection

yolo is algorithm based region proposal and the region proposal with max confidence treated as prediction of yolo for more you can read it about here

for this particular task i refer to this murtuza tutorial that guided me from scratch

now as the complex network architecture requires hours of training i prefer to use transfer learning as using pretrained network and weighs(parameters) both of this link you can find here
Architecture configuration:cfg
Network Parameters(weights):weights

i used here yolov3 tiny as i required higher frame rates to process a video but afterall it not giving promising result as the tutorial shows i don't where i am lacking but even changing the network cfg and weight file to the orignal yolov3(320)does not giving true result as i am getting all 5 spatial data as coordinate and confidence [cx,cy,h,w,confidence] but all 80 classes probality is still zero vector[0.0,0.0,0.0---0.0] even changing the video source and choosing another video resulting into zero vector which was in tutorial working fine

Implementation Code:

Code that used to work a month or two ago now posts the following error:

I think a recent update with either Colab or Cuda is throwing off the YOLOv4 model. I have previously built this notebook and trained a complete model with it with no problems but now when I run the exact same code, with no changes, I get this problem:

I'm currently working on a Darknet/YOLO project which detects objects from images received from a live stream using opencv in python. To detect object, the opencv image, which is simply a numpy array with shape (height, width, color_channels) has to be converted into a format that Darknet (written in c) can read (an IMAGE class defined in Darknet with a data attribute of type *float). For this I have written the following code in python:

I am trying to do object detection from a video file by using https://github.com/pjreddie/darknet. I've installed libopencv-dev for opencv. I've set opencv4=1 in Makefile.

And run this code. ./darknet detector demo cfg/coco.data cfg/yolo-tiny-obj.cfg yolov3.weights data/1.mp4 And got error Demo needs OpenCV for webcam images.

Could anyone help me? Thanks.