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gbr1 / ros_openvino

A ROS package to wrap openvino inference engine and get it working with Myriad and GPU
https://gbr1.github.io
GNU Affero General Public License v3.0
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Unable to use YOLOv3 model #6

Open Thomas-Merlet opened 4 years ago

Thomas-Merlet commented 4 years ago

I'm trying to change the model used from MobileNet SSD to YOLOv3. I am using the Intel RealSense Camera D435 on Ubuntu 16.04 (LTS).

Steps taken:

Issue:

Terminal output as follows:

[object_detection-4] process has died [pid 20105, exit code 255, cmd /home/USERNAME/catkin_ws/devel/lib/ros_openvino/object_detection /object_detection/input_image:=/camera/color/image_raw /object_detection/input_depth:=/camera/aligned_depth_to_color/image_raw /object_detection/camera_info:=/camera/aligned_depth_to_color/camera_info __name:=object_detection __log:=/home/USERNAME/.ros/log/da83b362-b530-11ea-87a9-e454e8a1df6c/object_detection-4.log].

No output image is visible with boxes.

Best regards, Thomas

gbr1 commented 4 years ago

Hi Thomas, could be this issue related?

Thomas-Merlet commented 4 years ago

Hi Giovanni,

Yes, might be a similar issue. I think this issue is related as well. YOLOv3 has 3 output layers which is not the current network output size.

gbr1 commented 4 years ago

@Thomas-Merlet I think that the best way to preceed is to create a new node, called yolo3 to improve the performance

ajtudela commented 4 years ago

Hi, @Thomas-Merlet , I'm also interested in making this work. Did you fixed it?

Thanks

Thomas-Merlet commented 4 years ago

Hi @ajtudela, the best way would be indeed to create a new detection node for YOLOv3, unfortunately I had no time to look at it yet.

gbr1 commented 4 years ago

@ajtudela @Thomas-Merlet , probably in August I will have free time to work on it. I would also rearrange sources file creating some common interfaces/libraries.

N.B. If you are using only 1 myriad 2 or 1 myriad x chip, yolo v3 will be at slow fps. It is suggested to use one of the AAEON PCI board with more myriad x chip.

ajtudela commented 4 years ago

Hi, thanks @gbr1 ! I have one NCS2, it will go faster on a cpu rather than iin the myriad? Also, how the fps would be if using tiny-yolo v3?

gbr1 commented 4 years ago

@ajtudela I used only mobilenet-ssd. With only 2d analysis I reach about 30fps on 800x600, if I remeber correctly. Obviously if you have a good cpu, it will be better. NCS2 is good for embedded. For example on Erwhi I used a Myriad X to not use CPU time for object detection.

Thomas-Merlet commented 4 years ago

Hi @gbr1 , about this, do you have a way to make sure it runs on NCS2? I don't see much difference in performance when changing the target, ant the top command gives the same usage.

gbr1 commented 4 years ago

@Thomas-Merlet uhm, probably you need to ask on OpenVINO forum. My package runs only on VPU (or GPU) so you can be sure that it isn't running on CPU.

Note: CPU need FP32 models, Myriad FP16 models.

IoTman commented 3 years ago

Not sure if you are still having issues, but YoloV3 is now fully supported by OpenVINO. But the best news is that they have included the conversion utility that will run all the downloads (eg darknet etc) and patches to TensorFlow to get it working. Have a look at the latest OpenVINO model Zoo, (and see the yaml file for details) https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v3-tf/model.yml Yolov4 is also available now.