darknet | Useful functionalities added on the original darknet public | Machine Learning library

So I'm using the Darknet Framework with YoloV4. This is my yolo_image.py code:

I have images that look as follows:

My goal is to detect and recognize the number 31197394. I have already fine-tuned a deep neural network on text recognition. It can successfully identify the correct number, if it is provided it in the following format:

The only task that remains is the detection of the corresponding bounding box. For this purpose, I have tried darknet. Unfortunately, it's not recognizing anything. Does anyone have an idea of a network that performs better on these kind of images? I know, that amazon recognition is able to solve this task. But I need a solution that works offline. So my hopes are still high that there exist pre-trained networks that work. Thank's a lot for your help!

I have finished an object detection project and I want to make a demo of it with a web application. I use YOLOv3 on Darknet for training. I intend to get the weights that I got from the training and use it in the web app. I decided to allow the user to upload an image, then take that image through a Python script, which will load my trained weights and do the detection. I decided to use PHP for the server-side and test everything on the localhost first.

I am totally new to web application and I don't know if I decided right

When I train yolo v4, I get a lot of outputs that I later want to use to plot learning rate changes.

With max_batches = 1543 darknet prints 74064 outputs (with loss etc, I don't count outputs with mAP calculation, network definition etc). With batch = 32, subdivisions = 16, I have only 49376 itarations (defined as single images passed through network)!

Something is definitely off, does anybody know how often this binary is supposed to print values?

Below is a procedure for returning a query to my darknet project. I want to get records for the last 6 months, last 1 week and last 1 month in where.

I will return the 'KAYITTARIHI' according to the query result

I want to edit the bounding box label to show only the probability of detection and not the class label, How shall I do this?

I found a file called image.c in darknet/src which I think is where my edits need to be made. But there are multiple functions in it that seem relevant for this task and I'm not sure which one to edit, and how to edit to get what I want. The code in image.c in quite long, hence kindly refer to this link (official darknet repo) where the code I'm referring to is available.

I tried editing the function void draw_detections at line 465 by simply changing the code at line 511 to printf("%s: %.0f%%", " ", prob * 100);, ran the !make command but the labels were still present in detection.

Do I need to remain connected with Colab and the internet when training a dataset model (Darknet) for Object detection on Google Colab? As the training is going on Colab and connected to my drive, here Weight files will be saved on my google drive folder. So, can I disconnect my internet and exit colab?

this is the last screen shown when I started my training process on COLAB and now I am waiting for my weight files to be saved in my drive

I'm currently trying to implement YOLOv3 object detection model in C(only detection, not training).

I have tested my convolution method with arbitrary values and it seems to be working as I expected.

Before stacking up multiple method calls to do forward propagation, I thought it would be safe to test with the actual pretrained weight file data.

When I look up Darknet's pre-trained weight file, it was a huge chunk of binary files. I tried to convert it to hex and decimals, but it still doesn't look simple to pinpoint what part of values to use.

So, my question is, what should I do to extract the decimal numbers of the weights or the filter values so that I can use them in the same order of the forward propagation happening in YOLOv3?

*I'm currently trying to build my c version of YOLOv3 using the structure image shown in https://www.itread01.com/content/1541167345.html

*My c code will be run on an FPGA board called MicroZed, along with other HDL code.

*I tried to plug some printf functions into some places of Darknet code to see what kinds of data are moving around when YOLOv3 runs, however, when I ran it on in Linux terminal, it didn't show anything new and kept outputting the same results.

Any help or advice will be really appreciated. Thank you!

Main Question:

What changes should I do to the repo's source code to successfully convert my YOLOv4 darknet weight (with custom anchors) to Tensorflow format?

Background:

I used this repo to convert my YOLOv4 darknet weights to Tensorflow format.

I have trained YOLOv4 on a custom dataset using custom anchors (9 anchors) but the number of anchors I used per [yolo] layer is 4, 3, 2, respectively. By default, YOLOv4 uses 3 anchors each [yolo] layer.

Main Problem:

The repo I used is coded in a way that only considers the default anchors, where there are 3 anchors each [yolo] layer.

What I tried to do to solve the main problem:

• I have tried to do some changes to the source code, which are summarized in this link.
• I used the code below to attempt converting the darknet weight to tf format. Here is the log of the conversion process.

python save_model.py --weights data/yolov4-512.weights --output ./checkpoints/yolov4-512 --input_size 512 --model yolov4

• I tested the resulting tf model using the code: python detect.py --weights checkpoints/yolov4-512 --size 512 --model yolov4 --image data/pear.jpg . The process failed and the error can be seen below. I have seen possible problems here but I don't know how to solve them.