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Ianvs Roadmap updated #181

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29 changes: 16 additions & 13 deletions docs/roadmap.md
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# Roadmap

Upon the release of ianvs, the roadmap would be as follows
- AUG 2022: Release Another Use Case and Advanced Algorithm Paradigm - Non-structured lifelong learning paradigm in ianvs
- SEP 2022: Release Another Use Case, Dataset, and Algorithm Paradigm - Another structured dataset and lifelong learning paradigm in ianvs
- OCT 2022: Release Advanced Benchmark Presentation - shared space for story manager to present your work in public
- NOV 2022: Release Advanced Algorithm Paradigm - Re-ID with Multi-edge Synergy Inference in ianvs
- DEC 2022: Release Simulation Tools
- JUN 2023: More datasets, algorithms, and test cases with ianvs
- DEC 2023: Standards, coding events, and competitions with ianvs



# Ianvs v0.1.0 release
## 1. Release the Ianvs distributed synergy AI benchmarking framework.
a) Release test environment management and configuration.
Expand All @@ -29,3 +16,19 @@ Ianvs is the first open-source site for that dataset.
## 4. Release PCB-AoI benchmark cases based on the two new paradigms.
a) Release PCB-AoI benchmark cases based on single-task learning, including leaderboards and test reports.
b) Release PCB-AoI benchmark cases based on incremental learning, including leaderboards and test reports.

# Ianvs v0.2.0 release

This version of Ianvs supports the following functions of unstructured lifelong learning:

## 1. Support lifelong learning throughout the entire lifecycle, including task definition, task assignment, unknown task recognition, and unknown task handling, among other modules, with each module being decoupled.
- Support unknown task recognition and provide corresponding usage examples based on semantic segmentation tasks in [this example](https://github.com/kubeedge/ianvs/tree/main/examples/robot-cityscapes-synthia/lifelong_learning_bench/semantic-segmentation).
- Support multi-task joint inference and provide corresponding usage examples based on object detection tasks in [this example](https://github.com/kubeedge/ianvs/tree/main/examples/MOT17/multiedge_inference_bench/pedestrian_tracking).

## 2. Provide classic lifelong learning testing metrics, and support for visualizing test results.
- Support lifelong learning system metrics such as BWT and FWT.
- Support visualization of lifelong learning results.

## 3. Provide real-world datasets and rich examples for lifelong learning testing, to better evaluate the effectiveness of lifelong learning algorithms in real environments.
- Provide cloud-robotics datasets in [this website](https://kubeedge-ianvs.github.io/).
- Provide cloud-robotics semantic segmentation examples in [this example](https://github.com/kubeedge/ianvs/tree/main/examples/robot/lifelong_learning_bench).
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