Custom dataset training using YOLOv5

[MrFahad]

I am working on Real-Time Surveillance System based on Face Recognition in which I want to train my own custom dataset using YOLOv5.

I am using 100 different people images (100000). Each person images stored in different folder.

Kindly guide me.

Hardware: details are as under:
Dell Precision 5510
RAM: 16 GB
SSD: 512 GB
GPU: M1000
Software:
Windows 10 Pro (64 bit)

[github-actions]

👋 Hello @MrFahad, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@MrFahad sounds good! You can get started at https://docs.ultralytics.com/yolov5/tutorials/train_custom_data, and you might also want to think of using free Colab and Kaggle GPUs (see Environments in message above).

[MrFahad]

100%
67.7M/67.7M [00:00<00:00, 77.6MB/s]

replace ../coco/LICENSE? [y]es, [n]o, [A]ll, [N]one, [r]ename: N
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 6339M 100 6339M 0 0 43.9M 0 0:02:24 0:02:24 --:--:-- 31.9M
mv: cannot move './test2017' to './coco/images': No such file or directory

I am getting the above error in google colab "COCO test-dev2017"

[glenn-jocher]

@MrFahad to get started in a few clicks I would recommend the Colab notebook. To train COCO128 for example, simply run the Setup cell, and run the Train cell.