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<!--lint disable list-item-indent-->
## Table of contents
- [Concept](#concept)
- [RC 1 vision](#rc-1-vision)
## Concept
Datumaro is:
- a tool to build composite datasets and iterate over them
- a tool to create and maintain datasets
- Version control of annotations and images
- Publication (with removal of sensitive information)
- Editing
- Joining and splitting
- Exporting, format changing
- Image preprocessing
- a dataset storage
- a tool to debug datasets
- A network can be used to generate
informative data subsets (e.g. with false-positives)
to be analyzed further
### Requirements
- User interfaces
- a library
- a console tool with visualization means
- Targets: single datasets, composite datasets, single images / videos
- Built-in support for well-known annotation formats and datasets:
CVAT, COCO, PASCAL VOC, Cityscapes, ImageNet
- Extensibility with user-provided components
- Lightweightness - it should be easy to start working with Datumaro
- Minimal dependency on environment and configuration
- It should be easier to use Datumaro than writing own code
for computation of statistics or dataset manipulations
### Functionality and ideas
- Blur sensitive areas on dataset images
- Dataset annotation filters, relabelling etc.
- Dataset augmentation
- Calculation of statistics:
- Mean & std, custom stats
- "Edit" command to modify annotations
- Versioning (for images, annotations, subsets, sources etc., comparison)
- Documentation generation
- Provision of iterators for user code
- Dataset downloading
- Dataset generation
- Dataset building (export in a specific format, indexation, statistics, documentation)
- Dataset exporting to other formats
- Dataset debugging (run inference, generate dataset slices, compute statistics)
- "Explainable AI" - highlight network attention areas ([paper](https://arxiv.org/abs/1901.04592))
- Black-box approach
- Classification, Detection, Segmentation, Captioning
- White-box approach
### Research topics
- exploration of network prediction uncertainty (aka Bayessian approach)
Use case: explanation of network "quality", "stability", "certainty"
- adversarial attacks on networks
- dataset minification / reduction
Use case: removal of redundant information to reach the same network quality with lesser training time
- dataset expansion and filtration of additions
Use case: add only important data
- guidance for key frame selection for tracking ([paper](https://arxiv.org/abs/1903.11779))
Use case: more effective annotation, better predictions
## RC 1 vision
In the first version Datumaro should be a project manager for CVAT.
It should only consume data from CVAT. The collected dataset
can be downloaded by user to be operated on with Datumaro CLI.
<!--lint disable fenced-code-flag-->
```
User
|
v
+------------------+
| CVAT |
+--------v---------+ +------------------+ +--------------+
| Datumaro module | ----> | Datumaro project | <---> | Datumaro CLI | <--- User
+------------------+ +------------------+ +--------------+
```
<!--lint enable fenced-code-flag-->
### Interfaces
- [x] Python API for user code
- [x] Installation as a package
- [x] A command-line tool for dataset manipulations
### Features
- Dataset format support (reading, writing)
- [x] Own format
- [x] CVAT
- [x] COCO
- [x] PASCAL VOC
- [x] YOLO
- [x] TF Detection API
- [ ] Cityscapes
- [ ] ImageNet
- Dataset visualization (`show`)
- [ ] Ability to visualize a dataset
- [ ] with TensorBoard
- Calculation of statistics for datasets
- [x] Pixel mean, std
- [x] Object counts (detection scenario)
- [x] Image-Class distribution (classification scenario)
- [x] Pixel-Class distribution (segmentation scenario)
- [ ] Image similarity clusters
- [ ] Custom statistics
- Dataset building
- [x] Composite dataset building
- [x] Class remapping
- [x] Subset splitting
- [x] Dataset filtering (`extract`)
- [x] Dataset merging (`merge`)
- [ ] Dataset item editing (`edit`)
- Dataset comparison (`diff`)
- [x] Annotation-annotation comparison
- [x] Annotation-inference comparison
- [x] Annotation quality estimation (for CVAT)
- Provide a simple method to check
annotation quality with a model and generate summary
- Dataset and model debugging
- [x] Inference explanation (`explain`)
- [x] Black-box approach ([RISE paper](https://arxiv.org/abs/1806.07421))
- [x] Ability to run a model on a dataset and read the results
- CVAT-integration features
- [x] Task export
- [x] Datumaro project export
- [x] Dataset export
- [x] Original raw data (images, a video file) can be downloaded (exported)
together with annotations or just have links
on CVAT server (in future, support S3, etc)
- [x] Be able to use local files instead of remote links
- [ ] Specify cache directory
- [x] Use case "annotate for model training"
- create a task
- annotate
- export the task
- convert to a training format
- train a DL model
- [x] Use case "annotate - reannotate problematic images - merge"
- [x] Use case "annotate and estimate quality"
- create a task
- annotate
- estimate quality of annotations
### Optional features
- Dataset publishing
- [ ] Versioning (for annotations, subsets, sources, etc.)
- [ ] Blur sensitive areas on images
- [ ] Tracking of legal information
- [ ] Documentation generation
- Dataset building
- [ ] Dataset minification / Extraction of the most representative subset
- Use case: generate low-precision calibration dataset
- Dataset and model debugging
- [ ] Training visualization
- [ ] Inference explanation (`explain`)
- [ ] White-box approach
### Properties
- Lightweightness
- Modularity
- Extensibility
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