https://github.com/rajdeep2804/darknet_yolo_tutorial

This repo acts like a training guide for yolo object detection model for custom data.
https://github.com/rajdeep2804/darknet_yolo_tutorial

darknet object-detection training tutorial yolo yolov3

Last synced: 10 days ago
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This repo acts like a training guide for yolo object detection model for custom data.

• Host: GitHub
• URL: https://github.com/rajdeep2804/darknet_yolo_tutorial
• Owner: rajdeep2804
• License: other
• Created: 2021-12-10T18:34:41.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-10T18:39:47.000Z (almost 3 years ago)
• Last Synced: 2024-03-23T12:08:43.057Z (8 months ago)
• Topics: darknet, object-detection, training, tutorial, yolo, yolov3
• Language: C
• Homepage:
• Size: 8.98 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Darknet_yolo_tutorial

# Training Framework

A generalised training framework consisting of all kinds of models like Yolo, RCNN etc. with default model configurations.

## Description

This repo acts like a training guide for yolo object detection model got custom data.

## Testing CUDA,

To test that CUDA works, go to the CUDA demo suite directory:

`cd /usr/local/cuda/extras/demo_suite/`

./deviceQuery

## Executing  darknet

Download the yolov3 weights in Darknet dir:

`wget https://pjreddie.com/media/files/yolov3.weights`

Make Sure in `Makefile` 'gpu == 1'.

From darknet dir run `make`

For Testing Darknet:

./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

###  Custom Training,

Copy "yolov3.cfg" file from cfg to custom_data/cfg dir, and rename to `yolov3-custom.cfg`

## Making changes in yolov3-custom.cfg file:

The maximum number of iterations for which our network should be trained is set with the param `max_batches=4000`. Also update `steps=3200,3600` which is 80%, 90% of max_batches, you can set value based on your training requirements.

classes param in the yolo layers to based on number of classes you are workning with like for one or `2 class` at `line numbers: 610, 696, 783`.

Similarly we will need to update the filters param based on the classes count `filters=(classes + 5) * 3`. 

For a single class we should set `filters=18` at `line numbers: 603, 689, 776`.

## Updating custom_data dir,

### Updating "custom.names" file : Mention all class name,

### Updating "detector.names" file : 

`classes=1`

train=custom_data/train.txt //Path to text file of images path for training.

valid=custom_data/test.txt // Path to text file of images path for testing.

names=custom_data/custom.names //Path to the class names

backup=backup/ //path to save weights

`Test.txt` need to store the path of each image used for testing

`Train.txt`  need to store the path of each image used for training

## Command to initialise training, 

```bash

./darknet detector detector train custom_data/detector.data custom_data/cfg/yolov3-custom.cfg yolov3.weights

```

## Evaluating your training,

```bash 

./darknet detector test custom_data/detector.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights -ext_output -out eval.json < eval.txt

./darknet detector map data/obj.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights

./darknet detector recall data/obj.data custom_data/cfg/yolov3-custom.cfg backup/yolov3_final.weights

```

#note eval.json will store all the output bounding box for each input image path stored in the eval.txt, //eval.txt will be prepared exactly like `test.txt/train.txt`