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###### Notice:
- If you have not visited our landing page in github, please do so: [https://github.com/TexasInstruments/jacinto-ai-devkit](https://github.com/TexasInstruments/jacinto-ai-devkit)
-- **Issue Tracker for jacinto-ai-devkit:** You can file issues or ask questions at **e2e**: [https://e2e.ti.com/support/processors/f/791/tags/jacinto_2D00_ai_2D00_devkit](https://e2e.ti.com/support/processors/f/791/tags/jacinto_2D00_ai_2D00_devkit). While creating a new issue, the part number should be filled in as **TDA4VM**. Also, kindly include **jacinto-ai-devkit** in the tags (at the end of the page as you create a new issue).
-- **Issue Tracker for TIDL:** [https://e2e.ti.com/support/processors/f/791/tags/TIDL](https://e2e.ti.com/support/processors/f/791/tags/TIDL). Please use part number as **TDA4VM** and tag as **TIDL**
+- **Issue Tracker for jacinto-ai-devkit:** You can file issues or ask questions at **e2e**: [https://e2e.ti.com/support/processors/f/791/tags/jacinto_2D00_ai_2D00_devkit](https://e2e.ti.com/support/processors/f/791/tags/jacinto_2D00_ai_2D00_devkit). While creating a new issue kindly include **jacinto-ai-devkit** in the tags (as you create a new issue, there is a space to enter tags, at the bottom of the page).
+- **Issue Tracker for TIDL:** [https://e2e.ti.com/support/processors/f/791/tags/TIDL](https://e2e.ti.com/support/processors/f/791/tags/TIDL). Please include the tag **TIDL** (as you create a new issue, there is a space to enter tags, at the bottom of the page).
- If you do not get a reply within two days, please contact us at: jacinto-ai-devkit@list.ti.com
### Deep Learning Models / Training / Calibration & Quantization - Using PyTorch<br>
This code provides a set of low complexity deep learning examples and models for low power embedded systems. Low power embedded systems often requires balancing of complexity and accuracy. This is a tough task and requires significant amount of expertise and experimentation. We call this process **complexity optimization**. In addition we would like to bridge the gap between Deep Learning training frameworks and real-time embedded inference by providing ready to use examples and enable **ease of use**. Scripts for training, validation, complexity analysis are also provided.
-This code also includes tools for **Post Training Calibration and Trained Quantization (a.k.a Quantization Aware Training)** that can output an 8-bit Quantization friendly model - these tools can be used to improve the quantized accuracy and bring it near floating point accuracy. For more details, please refer to the section on [Quantization](docs/Quantization.md).
+This code also includes tools for **Quantization Aware Training** that can output an 8-bit Quantization friendly model - these tools can be used to improve the quantized accuracy and bring it near floating point accuracy. For more details, please refer to the section on [Quantization](docs/Quantization.md).
-Our expectation is that these Deep Learning examples, models and tools will find application in a variety of problems, and the users will be able to build upon the **building blocks** that we have provided.
-
-**Several of these models have been verified to work on [TI's Jacinto7 Automotive Processors](http://www.ti.com/processors/automotive-processors/tdax-adas-socs/overview.html).** This code is primarily intended for learning and research.
+**Several of these models have been verified to work on [TI's Jacinto7 Automotive Processors](http://www.ti.com/processors/automotive-processors/tdax-adas-socs/overview.html).** These tools and software are primarily intended as examples for learning and research.
## Installation Instructions
- These instructions are for installation on **Ubuntu 18.04**.
@@ -29,26 +27,23 @@ Our expectation is that these Deep Learning examples, models and tools will find
```
## Examples
-The following examples are currently available. Click on each of the links below to go into the full description of the example.
-* Image Classification<br>
- * [**Image Classification**](docs/Image_Classification.md)<br>
-* Pixel2Pixel prediction<br>
- * [**Semantic Segmentation**](docs/Semantic_Segmentation.md)<br>
- * [Depth Estimation](docs/Depth_Estimation.md)<br>
- * [Motion Segmentation](docs/Motion_Segmentation.md)<br>
- * Multi Task Estimation - coming soon..<br>
-* Object Detection<br>
- * Object Detection - coming soon..<br>
- * Object Keypoint Estimation - coming soon..<br>
-* Quantization<br>
- * [**Quantization Aware Training**](docs/Quantization.md)<br>
+- [**Image Classification**](docs/Image_Classification.md)<br>
+- [**Semantic Segmentation**](docs/Semantic_Segmentation.md)<br>
+- [Depth Estimation](docs/Depth_Estimation.md)<br>
+- [Motion Segmentation](docs/Motion_Segmentation.md)<br>
+- [**Multi Task Estimation**](docs/Multi_Task_Learning.md)<br>
+- Object Detection - coming soon..<br>
+- Object Keypoint Estimation - coming soon..<br>
+- [**Quantization Aware Training**](docs/Quantization.md)<br>
+
+Above are some of the examples are currently available. Click on each of the links above to go into the full description of the example.
+
-Some of the common training and validation commands are provided in shell scripts (.sh files) in the root folder.
## Additional Information
-For information on other similar devkits, please visit:<br>
-- [https://github.com/TexasInstruments/jacinto-ai-devkit](https://github.com/TexasInstruments/jacinto-ai-devkit)<br> AND
-- [https://git.ti.com/jacinto-ai-devkit](https://git.ti.com/jacinto-ai-devkit)<br>
+- Some of the common training and validation commands are provided in shell scripts (.sh files) in the root folder.<br>
+- Landing Page: [https://github.com/TexasInstruments/jacinto-ai-devkit](https://github.com/TexasInstruments/jacinto-ai-devkit)<br>
+- Actual Git Repositories: [https://git.ti.com/jacinto-ai-devkit](https://git.ti.com/jacinto-ai-devkit)<br>
## Acknowledgements