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<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/for_training.png" width="200"/>
<div>&nbsp;</div>

[📘Usage Instruction](https://github.com/IndustryEssentials/ymir/blob/master/README_zh-CN.md#3-gui%E4%BD%BF%E7%94%A8-%E5%85%B8%E5%9E%8B%E6%A8%A1%E5%9E%8B%E7%94%9F%E4%BA%A7%E6%B5%81%E7%A8%8B) |
[🛠️Installation](https://github.com/IndustryEssentials/ymir/blob/master/README_zh-CN.md#2-%E5%AE%89%E8%A3%85) |
[📘Usage Instruction](https://github.com/IndustryEssentials/ymir/wiki/Operating-Instructions) |
[🛠️Installation](README.md#2-installation) |
[🚀Projects](https://github.com/IndustryEssentials/ymir/projects) |
[🤔Issues Report](https://github.com/IndustryEssentials/ymir/issues/new/choose) |
[📰Lisence](https://github.com/IndustryEssentials/ymir/blob/master/LICENSE)
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<div align="center">
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/wechat_code.jpg" width="180"/>
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/contact.jpg" width="400"/>

📫 Feedback on usage issues: [email protected] / Professional consulting for server equipment: [email protected]
<div>&nbsp;</div>&nbsp;</div>

Expand All @@ -62,14 +62,15 @@ If you wish to refer to YMIR in your work, please use the following BibTeX entry

# What's new

Version 2.0.0 updated on 10/21/2022
Version 2.0.0 updated on 11/08/2022

YMIR platform
- New dataset/model hiding and restoring functions.
- support for modifying project training categories before the start of an iteration.
- addition of model testing module and model inference result comparison function.
- optimization of code structure.
- the addition of a model deployment module.
- A new model performance diagnosis module.
- A new function for visual evaluation of model inference results.
- Adding a public algorithm library with a variety of built-in high-precision algorithms.
- One-click deployment function, supporting the deployment of algorithms to prerequisite certified devices.
- New operating instruction.
- Refactory code structure.

Docker
- Support [yolov5](https://github.com/ultralytics/yolov5)
Expand All @@ -81,32 +82,41 @@ Docker
- Support [demo sample image creation documentation](https://github.com/modelai/ymir-executor-fork/tree/ymir-dev/det-demo-tmi)
- Support [ymir mirror development extension library](https://github.com/modelai/ymir-executor-sdk)

View more [ymir-executor-fork](https://github.com/modelai/ymir-executor-fork)
View more [ymir-executor-fork](https://github.com/modelai/ymir-executor-fork) .

Within the public dockerimage
- Update yolov5 training image: youdaoyzbx/ymir-executor:ymir1.3.0-yolov5-cu111-tmi
- Update the mmdetection training image: youdaoyzbx/ymir-executor:ymir1.3.0-mmdet-cu111-tmi
- Update the yolov5 training image to support rv1126 chip deployment: youdaoyzbx/ymir-executor:ymir1.3.0-yolov5-cu111-modelstore
- Update the training image to support yolov5-v6.2: youdaoyzbx/ymir-executor:ymir1.3.0-yolov5-v6.2-cu111-tmi

More code updates [ymir-dev](https://github.com/modelai/ymir-executor-fork/tree/ymir-dev)
More code updates [ymir-dev](https://github.com/modelai/ymir-executor-fork/tree/ymir-dev).

# Deployment Prerequisite (optional)

YMIR supports deploying the trained model and public algorithm model directly to the certified device, for more hardware specs, please check [the details](https://i-item.jd.com/10065116628109.html).

<div align="center">
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/certified_device.PNG" width="500"/>
<div>&nbsp;</div>&nbsp;</div>

## Introduction

<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->

**Catalog**

- [Citations](#citations)
- [What's New](#whats-new)
- [Depolyment Prerequisite (optional)](#deployment-prerequisite-optional)
- [1. Introduction to AI SUITE-YMIR](#1-introduction-to-ai-suite-ymir)
- [1.1. Main functions](#11-main-functions)
- [1.2. Apply for trial](#12-apply-for-trial)
- [2. Installation](#2-installation)
- [2.1. Environment dependencies](#21-environment-dependencies)
- [2.2. Installation of YMIR-GUI](#22-installation-of-ymir-gui)
- [2.3. Installation of label studio (optional)](#23-installation-of-label-studio-optional)
- [2.4. Installation of Model Deployment (optional)](#24-installation-of-model-deployment-optional)
- [3. Use YMIR-GUI: typical model production process](#3-use-ymir-gui-typical-model-production-process)
- [4. For advanced users: YMIR-CMD (command line) user's guide](#4-for-advanced-users-ymir-cmd-command-line-users-guide)
- [4.1 Installation](#41-installation)
Expand All @@ -125,9 +135,9 @@ More code updates [ymir-dev](https://github.com/modelai/ymir-executor-fork/tree/
As a streamlined model development product, YMIR(You Mine In Recursion) focuses on the dataset versioning and model iteration in the AI SUITE open-source series.

<div align="center">
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/processing.png" width="800"/>
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/processing.png" width="1500"/>
<div>&nbsp;</div>&nbsp;</div>

AI commercialization is currently reaching a stage of maturity in terms of computing hardwares, algorithms, etc. The adoption of AI often encounter challenges such as a lack of skilled developers, high development costs and long iteration cycles.

As a platform, YMIR provides an end-to-end AI development system. This platform reduces costs for companies using artificial intelligence and accelerates the adoption of artificial intelligence. YMIR provides ML developers with one-stop services for data processing, model training, and other steps required in the AI development cycle.
Expand Down Expand Up @@ -168,7 +178,8 @@ YMIR platform mainly meets the needs of users to produce models at scale, provid
|Project Management|Model Management|Training Models|Support to select datasets, labels, and adjust training parameters to train models according to requirements, and view the corresponding model results after completion|
|Project Management|Model Management|Model Validation|Support uploading a single image to check the performance of the model in real images through visualization to verify the accuracy of the model|
|Tag management|Tag management|Add tags|Support adding primary names and aliases of training tags|
|Model Deployment|Algorithm Management|Public Algorithms|Support algorithm customization, view public algorithms uploaded by others and add them to my algorithms|
|Model Deployment|Algorithm Management|Public Algorithm|Support algorithm customization, view public algorithms and try them out, support adding to my algorithms|
|Model Deployment|Algorithm Management|Public Algorithm|Support publishing my algorithms to public algorithms|
|Model Deployment|Algorithm Management|My Algorithms|Support for viewing and editing my published algorithms and added algorithms|
|Model Deployment|Algorithm Management|Deploy Algorithms|Support deploying my algorithms to devices and viewing deployment history|
|Model Deployment|Device Management|View Devices|Support viewing device information and deployment history|
Expand Down Expand Up @@ -268,6 +279,14 @@ LABEL_TOOL_HOST_PORT=set_your_label_tool_HOST_PORT
```

* The default port number for YMIR's Model Deployment module is 18801. If there is a conflict that needs to be modified, you need to go to the YMIR directory and modify the .env file to configure the ModelDeployment port and MySQL access password:
```
DEPLOY_MODULE_HOST_PORT=18801
DEPLOY_MODULE_URL=${DEPLOY_MODULE_HOST_PORT}
DEPLOY_MODULE_MYSQL_ROOT_PASSWORD=deploy_db_passwd
```
Execute the start command after the modification: `bash ymir.sh start`.
4. After the service successfully started, YMIR will be available at [http://localhost:12001/](http://localhost:12001/). If you need to **stop the service**, run the command: `bash ymir.sh stop`
Expand Down Expand Up @@ -329,30 +348,12 @@ The user can access label studio through the default URL [http://localhost:12007
```sh
docker-compose -f docker-compose.label_studio.yml down
```
## 2.4. Installation of Model Deployment (optional)
ModelDeployment is a model deployment system supported by YMIR and can be installed as an optional model deployment tool.
1. In the YMIR directory in the previous section, modify the .env file to configure the ModelDeployment port and MySQL access password as follows.
```
DEPLOY_MODULE_HOST_PORT=18801
DEPLOY_MODULE_URL=${DEPLOY_MODULE_HOST_PORT}
DEPLOY_MODULE_MYSQL_ROOT_PASSWORD=deploy_db_passwd
```
2. start the installation ModelDeployment command as follows.
`docker-compose -f docker-compose.modeldeploy.yml up -d`
3.The command to check the status of ModelDeployment after completion is as follows.
`docker-compose -f docker-compose.modeldeploy.yml ps`
4. Stop the ModelDeployment service with the following command.
`docker-compose -f docker-compose.modeldeploy.yml down`
# 3. Use YMIR-GUI: typical model production process
![YMIR-GUI process](https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/YMIR-GUI-process.jpeg)
<div align="center">
<img src="https://github.com/IndustryEssentials/ymir-images/blob/main/doc_images/YMIR-GUI-process.jpeg" width="800"/>
<div>&nbsp;</div>&nbsp;</div>
As shown in the figure, YMIR divides the model development process into multiple steps. Details about how to run each step are listed in the subsequent sections.
Expand All @@ -362,7 +363,7 @@ Therefore, YMIR platform, through active learning, first attains an initial mode
The updated dataset is used to train the model again to improve the model capability. The YMIR platform provides a more efficient approach than labeling the entire data and then training it, reducing the cost of labeling low-quality data. Through the cycle of mining, labeling, and training, high quality data is expanded and the model capability is improved.
This section uses a complete model iteration process as an example to illustrate how to use the YMIR platform. Please check [Operating Instructions](https://github.com/IndustryEssentials/ymir/wiki/Operating-Instructions)
This section uses a complete model iteration process as an example to illustrate how to use the YMIR platform. Please check [Operating Instructions](https://github.com/IndustryEssentials/ymir/wiki/Operating-Instructions).
# 4. For advanced users: YMIR-CMD (command line) user's guide

Expand Down Expand Up @@ -409,7 +410,7 @@ Also check out [MSFT Encoding Style](https://github.com/Microsoft/Recommenders/w

## 5.2. About training, inference and mining docker images

[Check this document](docs/ymir-cmd-container.md) for details
[Check this document](https://github.com/IndustryEssentials/ymir/blob/dev/dev_docs/ymir-cmd-container.md) for details.

# 6. Design concept

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The training image `industryessentials/executor-det-yolov4-training:release-0.1.2` has a configuration file template located at: `/img-man/training-template.yaml`

Mining and inference mirrors The configuration file templates for `industryessentials/executor-det-yolov4-mining:release-0.1.2` are located at: `/img-man/mining-template.yaml` (mining) and `/img-man/infer-template. yaml` (infer)
Mining and inference mirrors The configuration file templates for `industryessentials/executor-det-yolov4-mining:release-0.1.2` are located at: `/img-man/mining-template.yaml` (mining) and `/img-man/infer-template. yaml` (infer).

**How can the trained model be used outside the system?**

Expand All @@ -441,7 +442,7 @@ It has not been fully tested on Windows server, so we cannot provide service sup

**How to import models I've already trained?**
See [this document](docs/import-extra-models.md)
See [this document](https://github.com/IndustryEssentials/ymir/blob/dev/dev_docs/import-extra-models.md).
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[![All Contributors](https://img.shields.io/badge/All%20Contributors-9-brightgreen)](#contributors-)
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