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authorManu Mathew2020-08-05 09:39:39 -0500
committerManu Mathew2020-08-05 09:39:39 -0500
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parente5a2ce7b98a6be8a3472c2bf6c75d52c2fcff7b4 (diff)
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@@ -15,14 +15,15 @@ Please see [LICENSE](./LICENSE) file.
15 15
16## Installation 16## Installation
17 17
18This repository requires **mmdet** Python package from mmdetection to be installed. 18This repository requires [**mmdetection**](https://github.com/open-mmlab/mmdetection) and [**mmcv**](https://github.com/open-mmlab/mmcv) to be installed.
19 19
20Please refer to [installation instructions for mmdetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/install.md) for installation instructions and also for dataset preparation. If you get any issues with the master branch of mmdetection, please try after checking out the latest release tag. 20Please refer to [installation instructions for mmdetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/install.md) for installation instructions. If you get any issues with the master branch of mmdetection, please try after checking out the latest release tag. After installation, a python package called "mmdet" will be listed if you do *pip list*
21 21
22Note: mmdetection also requreis **mmcv** and several other dependencies to be installed as described in the above URL. 22To install mmcv, browse to the github page of [mmcv](https://github.com/open-mmlab/mmcv), and see the section that says "**Install with pip**". Install the full version of mmcv using the instruction given there. Please check your CUDA version and PyTorch version and select the appropriate installation instructions.
23 23
24After installing **mmdet**, please clone and install [**PyTorch-Jacinto-AI-DevKit**](https://git.ti.com/cgit/jacinto-ai/pytorch-jacinto-ai-devkit/about/) using the link given in [jacinto-ai-devkit](https://github.com/TexasInstruments/jacinto-ai-devkit) as this repository uses several components from there - especially to define low complexity models and to do Quantization Aware Training (QAT) or Calibration. 24After installing **mmdetection** and **mmcv**, please clone and install [**PyTorch-Jacinto-AI-DevKit**](https://git.ti.com/cgit/jacinto-ai/pytorch-jacinto-ai-devkit/about/) using the link given in [jacinto-ai-devkit](https://github.com/TexasInstruments/jacinto-ai-devkit) as this repository uses several components from there - especially to define low complexity models and to do Quantization Aware Training (QAT) or Calibration.
25 25
26Please see our minimal installation script [setup.sh](./setup.sh) and modify for your system.
26 27
27## Get Started 28## Get Started
28 29
@@ -33,13 +34,13 @@ Please see [Usage](./docs/det_usage.md) for training and testing with this repos
33 34
34## Benchmark and Model Zoo 35## Benchmark and Model Zoo
35 36
36Several trained models with accuracy report is available at [Jacinto-AI-Detection Model Zoo](./docs/det_modelzoo.md) 37Accuracy report of several trained models is available at the [Model Zoo](./docs/det_modelzoo.md)
37 38
38Note: This Model Zoo is not publicly available yet - we shall make this available shortly.
39 39
40## Quantization 40## Quantization
41 41
42This tutorial explaines how to do [Quantization Aware Training](./docs/det_quantization.md) of detection models. We also provide sample QAT models. 42This tutorial explains how to do [Quantization Aware Training (QAT)](./docs/det_quantization.md) of detection models.
43
43 44
44## ONNX & Prototxt Export 45## ONNX & Prototxt Export
45- **Export of ONNX model (.onnx) and additional meta information (.prototxt)** is supported. The .prototxt contains meta information specified in **[TIDL](https://software-dl.ti.com/jacinto7/esd/processor-sdk-rtos-jacinto7/latest/exports/docs/psdk_rtos_auto/docs/user_guide/sdk_components.html#ti-deep-learning-library-tidl)** for object detectors. 46- **Export of ONNX model (.onnx) and additional meta information (.prototxt)** is supported. The .prototxt contains meta information specified in **[TIDL](https://software-dl.ti.com/jacinto7/esd/processor-sdk-rtos-jacinto7/latest/exports/docs/psdk_rtos_auto/docs/user_guide/sdk_components.html#ti-deep-learning-library-tidl)** for object detectors.
@@ -48,6 +49,7 @@ This tutorial explaines how to do [Quantization Aware Training](./docs/det_quant
48 49
49- For more information please see [Usage](./docs/det_usage.md) 50- For more information please see [Usage](./docs/det_usage.md)
50 51
52
51## Acknowledgement 53## Acknowledgement
52 54
53This is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. 55This is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.