What's New
Supports Unstructured Lifelong Learning
The new unstructured lifelong learning feature try to improve the efficiency and reduce cost of high-value data collection, in many scenarios like robot, factory, etc.. Those scenarios usually require a lot of unstructured image data to be processed, and will also face various corner cases. For example, robots need to process image data from cameras for perception, they may encounter different curb on different roads while the class of curb already exists at training phase , or a different new class like flower bed. If these classes are not recognized correctly, the robot will wrestle and even cause environmental damage.
Therefore, this version of unstructured lifelong learning supports the following functions to solve the preceding problems:
- Supports unstructured lifelong learning: implement closed-loop data of unstructured data, and provides robot delivery example demo vedio and code based on image segmentation.
- Supports unseen task recognition.
- Supports early warning of unseen task: For example, robots can stop in time when encountering image samples that cannot be processed by models for manual intervention or other optional measures.
- Supports High-value data filtering: Only inference samples those cannot be inferenced correctly by models are considered high-value data, and will be collected then used for model training and update.
- Supports displaying knowledge base: Displays knowledge base information to users, including the number of unseen samples (or high-value samples) and the corresponding number of trained models.
The detailed pull requests are as follows:
- Proposal and tutorial of unstructured lifelong learning by @luosiqi in #391
- Unstructured Sedna Lifelong Learning Architecture by @luosiqi in #392
- Unstructured lifelong learning with cloud robotics example by @luosiqi in #382
- Support displaying knowledge base by @jaypume in #406
O&M and Monitoring
- Supports observability management for displaying logs and metrics of sedna in real time.
- Supports lifelong learning exporter and Visualization.
The detailed pull requests are as follows:
- The proposal for Observability management by @Kanakami in #340
- The implementation for Observability management by @Kanakami in #366
- The proposal for Lifelong Learning O&M by @wjf222 in #335
- The implementation for Lifelong Learning exporter and Visualization by @wjf222 in #369
More Examples Provided：
- Provides a Mindspore demo by @Lj1ang in #376
- Provides a high-frequency Sedna-based end-to-end use case in ModelBox by @Ymh13383894400 in #368
- Provides a tutorial for ATCII Lifelong Learning Job by @qxygxt in #405
By @Lj1ang @Ymh13383894400 @qxygxt
Other Notable Changes
- Supports JSON format Dataset Parse in Python Lib by @yqhok1 in #375
- Supports TinyMS backend in Python SDK by @Lj1ang in #341
Bug Fixes
- Fixed wrong url in document by @RyanZhaoXB in #394
- Fixed wrong path of installation document in README.md by @RyanZhaoXB in #385
- Fixed ci e2e running failed issue. by @jaypume in #410
New Contributors
- @Kanakami made their first contribution in #340
- @wjf222 made their first contribution in #335
- @Lj1ang made their first contribution in #376
- @yqhok1 made their first contribution in #375
- @Ymh13383894400 made their first contribution in #368
- @RyanZhaoXB made their first contribution in #394
- @qxygxt made their first contribution in #405
Full Changelog: v0.5.1...v0.6.0
