Classifier Pipeline¶
Structuring Ideas¶
- Data-flow layer + control layer
- E.g. central controller manages component states according to some protocol/rules
- Alternative: Subgraph lifecycles
- E.g. learning subgraph between data buffer and learner)
Requirements¶
Training¶
- Sequential processing: data source, keypoint, descriptor, feature
- Processing Strategies (in CCA speech)
- First possibility
- As fast as possible
- Flow control is necessary
- Usually, all inputs are processed
- Second possibility
- "Live" data source (i.e. camera)
- Multiple phases
- Collection
- Actual training
- Model is transmitted from learner to classifier
- Programmatical interface for parameter setting at runtime (dynamic adjustment from another module)
Classification¶
- Static, linear data flow: source, keypoint, descriptor, feature, classifier
- No buffering/persistence required
Non-functional Requirements¶
- Parallel / distributed processing
- Visualization (when and what)
- Optional: real-time capability?
- Model description / specification (do skeletons exist?)
- Interactive shell for parameter exploration (probably related to parameter server with GUI)
Related work¶
- gstreamer
- IceWing
- data flow specification
- parallel processing (?)
- distributed processing (?)
- support for non-image data is not existent
- usability (?)
- no support for collection, training, classification phases / states
- CCA
- parallel / distributed processing
- processing strategies (would need to be extended towards full flow control)
- support for collection, training, classification phases
- no support for computer vision
- early prototype
- Ecto: A C++/Python Computation Graph Framework
- Leon will look at this...
- does it feature distributed processing?
- LabView
- Simulink
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