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# Kneron AI Training/Deployment Platform (mmDetection-based)
## Introduction
[kneron-mmdetection](https://github.com/kneron/kneron-mmdetection) is a platform built upon the well-known [mmdetection](https://github.com/open-mmlab/mmdetection) for detection and instance segmentation. We encourage you to start with [YOLOX: Step-By-Step](docs_kneron/yolox_step_by_step.md) to build basic knowledge of Kneron-Edition mmdetection, and read [mmdetection docs](https://mmdetection.readthedocs.io/en/latest/) for detailed mmdetection usage.
In this repository, we provide an end-to-end training/deployment flow to realize on Kneron's AI accelerators:
1. **Training/Evalulation:**
- Modified model configuration file and verified for Kneron hardware platform
- Please see [Overview of Benchmark and Model Zoo](#Overview-of-Benchmark-and-Model-Zoo) for Kneron-Verified model list
2. **Converting to ONNX:**
- pytorch2onnx_kneron.py (beta)
- Export *optimized* and *Kneron-toolchain supported* onnx
- Automatically modify model for arbitrary data normalization preprocess
3. **Evaluation**
- test_kneron.py (beta)
- Evaluate the model with *pytorch checkpoint, onnx, and kneron-nef*
4. **Testing**
- inference_kn (beta)
- Verify the converted [NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model on Kneron USB accelerator with this API
5. **Converting Kneron-NEF:** (toolchain feature)
- Convert the trained pytorch model to [Kneron-NEF](http://doc.kneron.com/docs/#toolchain/manual/#5-nef-workflow) model, which could be used on Kneron hardware platform.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
N/A
## Overview of Benchmark and Kneron Model Zoo
| Backbone | size | Mem (GB) | box AP | Config | Download |
|:---------:|:-------:|:-------:|:-------:|:--------:|:------:|
| YOLOX-s | 640 | 7.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py) |[model](https://github.com/kneron/Model_Zoo/blob/main/mmdetection/yolox_s/latest.zip)
## Installation
- Please refer to [YOLOX: Step-By-Step, Step 0. Environment](docs_kneron/yolox_step_by_step.md) for installation.
- Please refer to [Kneron PLUS - Python: Installation](http://doc.kneron.com/docs/#plus_python/introduction/install_dependency/) for the environment setup for Kneron USB accelerator.
## Getting Started
### Tutorial - Kneron Edition
- [YOLOX: Step-By-Step](docs_kneron/yolox_step_by_step.md): A tutorial for users to get started easily. To see detailed documents, please see below.
### Documents - Kneron Edition
- [Kneron ONNX Export] (under development)
- [Kneron Inference] (under development)
- [Kneron Toolchain Step-By-Step (YOLOv3)](http://doc.kneron.com/docs/#toolchain/yolo_example/)
- [Kneron Toolchain Manual](http://doc.kneron.com/docs/#toolchain/manual/#0-overview)
### Original mmdetection Documents
- [Original mmdetection getting started](https://github.com/open-mmlab/mmdetection#getting-started): It is recommended to read the original mmdetection getting started documents for other mmdetection operations.
- [Original mmdetection readthedoc](https://mmdetection.readthedocs.io/en/latest/): Original mmdetection documents.
## Contributing
[kneron-mmdetection](https://github.com/kneron/kneron-mmdetection) a platform built upon [OpenMMLab-mmdetection](https://github.com/open-mmlab/mmdetection)
- For issues regarding to the original [mmdetection](https://github.com/open-mmlab/mmdetection):
We appreciate all contributions to improve [OpenMMLab-mmdetection](https://github.com/open-mmlab/mmdetection). Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmdetection/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
- For issues regarding to this repository [kneron-mmdetection](https://github.com/kneron/kneron-mmdetection): Welcome to leave the comment or submit pull requests here to improve kneron-mmdetection
## Related Projects
- kneron-mmsegmentation: Kneron training/deployment platform on [OpenMMLab - mmSegmentation](https://github.com/open-mmlab/kneron-mmsegmentation) semantic segmentation toolbox