1. Added support for mixed precision training on Tensor Core.

2. Added support for NVIDIA-Caffe and integrated into DLP Docker container.

3. Updated official DLP docker image.

4. Updated user guide to 2.5 (CH).

5. Fixed numerous bugs.

Data module

1. In image classification,when converting data format,users can choose whether to export original raw images.

Net module

1. Added Python layer for Caffe-BVLC and Caffe-NVIDIA to allow users to define their own layers.

2. Added "PReLu" and "ELU" for activation layers.

3. Added dozens of networks into network library.

Train module

1. In job property panel, added "Start time" and "End time" to record model training time.



1. Add support for Docker and Nvidia-Docker.

2. Provide a docker container which integrates DLP dependencies.

3. Provide a Chinese version user guide.

4. Add license manager.

5. Provide a new font, and fix some bugs.

Data module

1. Add labeling function for object detection image processing.

2. Provide unique format for each detection algorithm.

3. Update image segmentation labeling functions with better user experience.

Net module

1. Add support for SSD, YOLO and FasterRCNN Caffe versions.

2. Add 2 more image classification networks and 4 object detection networks in net library.

3. Add the function to generate deployment ".prototxt" files.

Train module

1. Add the function parse Caffe log files and generate plotings.

2. Add "Train" button for launching training out of DLP net module. Currently only support official Caffe.

3. Add mAP curve plotting fo object detection task.

Deploy module

1. Add support for object detection inference.

2. Add support to do inference in Docker.

3. Add the function to automatically parse ".prototxt" file and fill in parameters.

4. Add input textfields for image size to define input dimension for inference.



1. Added HDPI icons for MacBook Retina display.

Data module

1. Fixed the latency issue when adding multiple images to a class.

2. Improved the workspace switching speed among classes.


Data module

1. Image organization and storing for classification.

2. Image zoom in, out and reset functions.

3. Image selection, and mouse slide for multiple image selection.

4. Set the image size, and preview the image.

5. Export organized images to a folder; import the exported data and instantly load your data.

6. In segmentation module, after switching to labeling mode, double click on the image to start drawing, left click will add a key point, right click will delete last key point, double click again will stop drawing.

Net module

1. Drag a layer from net component panel, and define parameters in the setting panel.

2. Connecting layers by drawing lines.

3. Copy and paste selected net components by right click on the net component.

4. Drag a existing network from network librarty

5. Right press mouse and move in the workspace to translate the whole network.

6. Left press mouse and move in the workspace to select net components.

7. Use mouse wheel to zoom in and out the network.

8. Use alignment tools to organize net components to make them in order.

9. Export network to .prototxt file; import existing .prototxt to DLP.

Train module

1. Click the yellow button in Job Manager to view solver parameters.

2. Click the circle button in Job Manager to show or hide the plot.

3. Zoom in and out, and reset the plots.

4. Export all plots and job parameters into one .log file.

5. Import existing log file to view the plots.

6. Drag mouse from up-left to down-right to zoom in selected region of the curve.

7. Drag mouse from down-right to up-left to zoom out selected region of the curve.

Deploy module

1. Configure inference information in the info panel.

2. Click the switch button to launch the inference.

3. Add image as input and plot the output.

4. Support both classification and segmentation inference.