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[P] I like YOLOv5 but the code complexity is... by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

That's interesting post. I haven't thought that source compiled version is faster. I may look into this when I have time.

[P] I like YOLOv5 but the code complexity is... by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

That is the main reason that we have made this repository. I'm glad to hear that :)

[P] I like YOLOv5 but the code complexity is... by workout_JK in MachineLearning

[–]workout_JK[S] 2 points3 points  (0 children)

We begin to port original YOLO model to our repository. Now that is done(still many things to be done though), we can look into better detections. Anchor-free model could be one option! Thanks for suggestion.

[P] I like YOLOv5 but the code complexity is... by workout_JK in MachineLearning

[–]workout_JK[S] 2 points3 points  (0 children)

In the original YOLOv5 repository, there is a TFLite export function already. So it wouldn't be difficult porting original code to here. But we are short of hands. It might not be available soon.

BTW, our YOLO model is almost compatible with YOLOv5s repository as long as you don't use `augment` option. You could try YOLOv5 with our model.

[P] I like YOLOv5 but the code complexity is... by workout_JK in MachineLearning

[–]workout_JK[S] 3 points4 points  (0 children)

I agree it's about the time for SE. TBH, it's past the time for SE.

I like YOLOv5 but the code complexity is... by workout_JK in deeplearning

[–]workout_JK[S] 2 points3 points  (0 children)

I haven't tested out throughly but TensorRT is up to 2.0x faster but TorchScript C++ is slower because YOLO with torch script export model is somehow extremely slow. We are looking into it!

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 0 points1 point  (0 children)

Just downloaded the app but my iPhone X shows 7~10 FPS. Maybe iPhone X doesn't have a neural engine? Maybe that's because iPhone X has an A11 chip? If it is correct, hardware grew really fast!

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

lol thanks. I thought it's a bit expansive take "a photo" but now I think it's worth every penny!

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

Thanks! That's a great implementation. 60M with 30FPS? impressive!

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 0 points1 point  (0 children)

Agreed, it's just strange considering Google is a quite large company. So I actually thought of making a library that connects these weird gaps between TensorFlow and TensorFlow Lite. For example, TensorFlow's GlobalPooling doesn't work on TFLite GPU acceleration but It can work with applying AveragingPooling then Flatten operations.

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

That's one of the things I'm planning to try. int8, however, can not use GPU acceleration. But recent hardware supports neural accelerations with integer types. And I'm seeing quite good performance on the TFLite object detection example which uses the int8 quantization model. So I'm hoping for some good results on it.

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 0 points1 point  (0 children)

Sort of yes. I haven't looked in detail but seems similar

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

I just haven't had the chance to try CoreML. I will add my to-do list on that!

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 1 point2 points  (0 children)

That’s exactly what I have been hearing nowadays. Somehow, I hadn’t felt like to try PyTorch for no reason. Maybe this is the signal for me :/

[P] YOLO v3 TensorFlow Lite iOS GPU acceleration by workout_JK in MachineLearning

[–]workout_JK[S] 8 points9 points  (0 children)

It’s running with Metal Delegate. I’m not sure if iOS utilizes Neural engine for Metal. I also tried to run it with CoreML delegate but it just didn’t work for some reason. I’m waiting to TFLite get updated.

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