Comments (20)
Here's the anouncement of the posenet 2.0 models:
It seems there's an improved version for Mobilenet, and there's also the new resnet model, which is slower, but highly accurate... looking at the source code, it seems its downloading the models from here: https://github.com/tensorflow/tfjs-models/blob/master/posenet/src/checkpoints.ts#L18
There's also a similar demo for hand and finger tracking:
https://github.com/tensorflow/tfjs-models/tree/master/handpose
Which might be interesting to have too!
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Hi again... after further investigation, it seems some people already succeeded in converting the models to protobuf: https://github.com/atomicbits/posenet-python
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If you're in a hurry, you can download saved_model with this shell script.
https://github.com/PINTO0309/PINTO_model_zoo/blob/master/03_posenet/02_posenet_v2/01_float32/download_saved_model.sh
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My model is from old version 1. I didn't know that a version 2 model with high accuracy was released. Thank you for sharing. I need to investigate for a while to see if v2 can be converted and put into my repository. It will take some time.
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Awesome!! The information you share is very useful to other engineers. Thank you again!
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The skeleton detection of extraterrestrial life seems to be very difficult.
Test. 1
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Looks nice!, this is with the new posenet 2 models?
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The heaviest parameter, Resnet50.
--model resnet50 --stride 16 --quant_bytes 4
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For some reason, I am unable to run that python code.... I think I have a problem with some of the tensor flow dependencies. That's why I was using your repos for downloading the models.... I hope you can upload the .pb files soon!
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The .pb (saved_model) alone is ready to commit immediately. We are in the process of converting to tflite.
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Great!
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Hi again; I've been having some trouble consuming the new posenet saved models produced by your script.
In particular, I'm trying to load them with Emgu.TF, but it complains about invalid format.
I've been inspecting the models with Netron, and I've found some subtle differences;
for example, this is what is typically expected:
And this is what I see in your saved models:
So, maybe setting the format as "TensorFlow Saved Model v1" instead of "TensorFlow Graph" may be causing some differences?
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I plan to commit saved_model, freeze_graph, tflite that will solve all your problems within a few hours.
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great!
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I have committed to all materials. Unfortunately, only Full Integer Quantization didn't work properly.
Posenet V2 ResNet50 225x225 - 513x513 - tflite
Weight Quantization, Integer Quantization, Float16 Quantization, saved_model, Freeze_graph
https://github.com/PINTO0309/PINTO_model_zoo/tree/master/03_posenet/02_posenet_v2
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Thanks!, the new models are working!
I have a question, though... I've noticed you've converted only the new RestNet models, which is great, since they're the most accurate ones...
But, what about the mobilenet models? The TensorFlow.JS online demo allowed to choose between mobilenet and resnet..... and with the online mobilenet models I got significantly better detection than with the old mobilenet versions.... so maybe the mobilenet versions have been retrained for posenet 2.0? maybe it was just my impression...
Anyway, even if Resnet is much more accurate, mobilenet is still very useful for low end machines.
And, did you take a look at the handpose models?
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I was so busy and tired from doing my main business on weekdays that I performed only minimal conversions. There is no other reason not to convert the MobileNet version. rest assured. I have plenty of time to work at home on Saturdays and Sundays, so I plan to convert MobileNet as well.
But, what about the mobilenet models? The TensorFlow.JS online demo allowed to choose between mobilenet and resnet..... and with the online mobilenet models I got significantly better detection than with the old mobilenet versions.... so maybe the mobilenet versions have been retrained for posenet 2.0? maybe it was just my impression...
really? I never expected the MobileNet version to be more accurate. In any case, the conversion process is easy, so I will try it.
And, did you take a look at the handpose models?
Yes. The MediaPipe demo is very interesting. I've been observing well for more than half a year.
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Don´t worry, I'm not in a hurry, actually I believe your responses have been incredibly fast!
And thanks a lot for the hard work!
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Btw, the video below is the result of my trial of 3D Pose Estimation, which is quite interesting.
3D PoseEstimation + OpenVINO + Corei7 CPU only + 720p(1280x720) USB Camera + Sync Real TIme
https://www.youtube.com/watch?v=DgKw0Ty22PE&feature=youtu.be
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I have committed a full version of MobileNet/ResNet model. Please update the cloned repository and execute the download script again.
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Related Issues (20)
- Zoedepth ONNX conversion Script HOT 3
- gfpgan coreml model HOT 1
- License of RAFT models HOT 3
- Script to convert RAFT models HOT 3
- Blazeface onnx model HOT 1
- BodyPix on MacOS - Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER HOT 8
- TOPK operator for RKNN export HOT 1
- InstructIR
- 064_Dense_Depth seems to have wrong dimensions HOT 1
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- Difference on model outputs (tflite, openvino IR, and Onnx) in model 227_face-detection-adas-0001 HOT 1
- bad results for 342_ALIKE HOT 1
- Aborted (core dumped) for full quantized tinyhitnet model
- 091_gaze-estimation-adas-0002 network HOT 1
- Release new 303_FAN with heatmaps HOT 3
- dataset HOT 1
- 410_FaceMeshV2 quantized tflite models are not functional HOT 1
- 053_BlazePose / 058_BlazePose_Full_Keypoints source HOT 3
- How to retrain simple MLP (palm_detection_full_inf_post_192x192.onnx) model with custom hand dataset ? HOT 2
- How to retrain simple MLP (palm_detection_full_inf_post_192x192.onnx) model with custom hand dataset ? HOT 2
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