add related projects (#861)

README.md

@@ -68,6 +68,7 @@ English | [简体中文](README_zh-CN.md)
 - [🛠️ Installation](#%EF%B8%8F-installation-)
 - [👨‍🏫 Tutorial](#-tutorial-)
 - [📊 Overview of Benchmark and Model Zoo](#-overview-of-benchmark-and-model-zoo-)
+- [🧰 Projects Based on MMYOLO](#-projects-based-on-mmyolo-)
 - [❓ FAQ](#-faq-)
 - [🙌 Contributing](#-contributing-)
 - [🤝 Acknowledgement](#-acknowledgement-)
@@ -366,6 +367,33 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 
 </details>
 
+## 🧰 Projects Based on MMYOLO [🔝](#-table-of-contents)
+
+### Algorithm
+
+- [YOLO-MS](https://github.com/FishAndWasabi/YOLO-MS)
+  Rethinking Multi-Scale Representation Learning for Real-Time Object Detection
+
+- [RSPrompter](https://github.com/KyanChen/RSPrompter)
+  Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
+
+- [EAEFNet](https://github.com/FreeformRobotics/EAEFNet)
+  Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
+
+- [CVPRW2023 Track 1: Multi-Camera People Tracking](https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI)
+  Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
+
+### Deployment
+
+- [EdgeLab](https://github.com/Seeed-Studio/EdgeLab)
+  Seeed Studio EdgeLab is an open-source project focused on embedded AI. They have optimized excellent algorithms from OpenMMLab for real-world scenarios and made implementation more user-friendly, achieving faster and more accurate inference on embedded devices.
+
+- [AX-Samples](https://github.com/AXERA-TECH/ax-samples/tree/main/examples/ax620)
+  AX-Samples is developed by Axera. This project implements the sample code of common deep learning open source algorithms on Axera's AI SoC. There're some MMYOLO case examples in this project.
+
+- [PPQ ONNX Quantization Model Zoo](https://github.com/openppl-public/ppq/blob/master/ppq/samples/QuantZoo/Readme.md#33-%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B)
+  OnnxQuant is the largest model quantization dataset available that contains ONNX models, dataset, and associated test scripts. They use MMYOLO to build the object detection benchmark.
+
 ## ❓ FAQ [🔝](#-table-of-contents)
 
 Please refer to the [FAQ](docs/en/tutorials/faq.md) for frequently asked questions.

README_zh-CN.md

@@ -68,6 +68,7 @@
 - [🛠️ 安装](#️%EF%B8%8F-安装-)
 - [👨‍🏫 教程](#-教程-)
 - [📊 基准测试和模型库](#-基准测试和模型库-)
+- [🧰 基于 MMYOLO 开发的项目](#-基于-mmyolo-开发的项目-)
 - [❓ 常见问题](#-常见问题-)
 - [🙌 贡献指南](#-贡献指南-)
 - [🤝 致谢](#🤝-致谢-)
@@ -388,6 +389,33 @@ MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也
 
 </details>
 
+## 🧰 基于 MMYOLO 开发的项目 [🔝](#-table-of-contents)
+
+### 算法
+
+- [YOLO-MS](https://github.com/FishAndWasabi/YOLO-MS)
+  实时物体检测的多尺度表征学习
+
+- [RSPrompter](https://github.com/KyanChen/RSPrompter)
+  基于视觉基础模型的遥感实例分割提示学习
+
+- [EAEFNet](https://github.com/FreeformRobotics/EAEFNet)
+  针对 RGB 热感知任务的显式注意力增强融合
+
+- [CVPRW2023 Track 1: Multi-Camera People Tracking](https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI)
+  利用锚点引导聚类和时空一致性 ID 重新分配增强多摄像头人员跟踪能力
+
+### 部署
+
+- [EdgeLab](https://github.com/Seeed-Studio/EdgeLab)
+  Seeed Studio EdgeLab 是一个专注于嵌入式人工智能的开源项目。他们对 OpenMMLab 中的优秀算法进行了优化，使其适用于实际应用场景，并使实现过程更加友好，从而在嵌入式设备上实现更快、更准确的推理。
+
+- [AX-Samples](https://github.com/AXERA-TECH/ax-samples/tree/main/examples/ax620)
+  AX-Samples 由 爱芯元智 主导开发。该项目实现了常见的 深度学习开源算法 在 爱芯元智 的 AI SoC 上的示例代码，其中也包含了 MMYOLO 的相关示例。
+
+- [PPQ ONNX Quantization Model Zoo](https://github.com/openppl-public/ppq/blob/master/ppq/samples/QuantZoo/Readme.md#33-%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B)
+  OnnxQuant 是目前最大的模型量化数据集，它包含 ONNX 模型，数据，以及相关的测试脚本。 他们使用 MMYOLO 作为目标检测量化算法的基准测试。
+
 ## ❓ 常见问题 [🔝](#-table-of-contents)
 
 请参考 [FAQ](docs/zh_cn/tutorials/faq.md) 了解其他用户的常见问题。