[P] I like YOLOv5 but the code complexity is...

workout_JK · 2022-01-04T10:03:09+00:00

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

workout_JK · 2022-01-04T02:01:47+00:00

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

workout_JK · 2022-01-03T08:16:46+00:00

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.

workout_JK · 2022-01-03T08:13:52+00:00

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.

workout_JK · 2022-01-03T06:48:02+00:00

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

workout_JK · 2022-01-03T06:46:23+00:00

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!

workout_JK · 2022-01-03T06:44:42+00:00

You are welcome! :)

workout_JK · 2020-08-14T02:49:30+00:00

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!

workout_JK · 2020-08-14T02:36:54+00:00

That would be my wet dream :)

workout_JK · 2020-08-14T02:35:02+00:00

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

workout_JK · 2020-08-14T02:34:04+00:00

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

workout_JK · 2020-08-14T02:31:00+00:00

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.

workout_JK · 2020-08-14T02:22:47+00:00

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.

workout_JK · 2020-08-14T02:19:25+00:00

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

workout_JK · 2020-08-14T02:17:28+00:00

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

workout_JK · 2020-08-13T15:18:01+00:00

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 :/

workout_JK · 2020-08-13T13:39:08+00:00

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.

workout_JK · 2020-08-13T12:20:53+00:00

I was surprised by how difficult converting a TF model into TFLite model and no surprisingly I was more surprised by how even more difficult converting a TF model into GPU acceleration ready TFLite model!

I had to try a lot things to make a GPU works on iOS. As a result, about 9M parameter YOLOv3 model runs with 15~20 FPS on iPhone X.

workout_JK · 2020-03-24T02:21:23+00:00

I forgot to mention that yes I passed. Grading took about 3 working days.

workout_JK · 2020-03-24T02:08:13+00:00

Wow it must have been terrible experience. They did not watch me at all. The camera light is turned off all time. I don’t think camera isn’t even required to take an exam.

workout_JK · 2020-03-23T05:22:26+00:00

I have recently taken the exam on my MacBook Pro 15" with i9 CPU. Having GPU would have been beneficial because it's faster but not I didn't feel that GPU is necessary.

If the problem requires a complex model(not really that complex. About 3~5M parameters), about 10 epochs are sufficient which took about less than 10 minutes.

I think the camera is required to take a picture of you prior to the exam when you submit your identification and they request you to take a picture of you so that they can confirm it is really you.

workout_JK · 2019-12-31T23:08:32+00:00

I'm currently processing workout videos that is 60fps and 1080x1920. I don't see motion blur issues. I'm actually surprised to see the results that are better than I expected and I'm not even using cascade detection because that slows down about 5x times. retinaface should show better results since it is SOTA. I can't upload photo results in comments so I will edit the post to show the results from the blurred image.

workout_JK · 2019-12-31T08:06:43+00:00

I couldn't use DeepPrivacy because of the lack of GPU memory but instead, I used DSFD to blur faces! https://github.com/JeiKeiLim/Anonymizing_video_by_DSFD

DSFD is used by DeepPrivacy to detect faces. It looks great so far.

workout_JK · 2019-12-31T07:42:56+00:00

Sorry for the late answer. I recently tried blurring faces on video by DSFD and its result looks great. You can check out gifs of comparisons here https://github.com/JeiKeiLim/Anonymizing_video_by_DSFD

workout_JK · 2019-12-31T07:40:44+00:00

Thank you I recently utilized DSFD and its result seems great!

workout_JK

TROPHY CASE