Comments (4)
Hi, personally, I think ResNet would be better than hourglass because recent 2d human pose estimation methods such as TF-SimpleHumanPose achieve better accuracy than hourglass. Why do you need to apply hourglass?
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Hi, the reason why I am trying to apply hourglass is our project already has a hourglass backbone for other purpose, so I am thinking just using the synthetic part in the paper. And also, I'm wondering can the idea of posefix applys on some regular 2d bbox detection for refinement or not? Thx for ur reply.
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I think you should try, but as I said, in my opinion, I think ResNet would be better.
Also, can you clarify which idea do you want to use?
If that is model-agnostic refinement, PoseFix assumes existing methods have similar error distribution as analyzed in Ronchi etal. If you can categorize object detection error and analyze error of several methods, which should have similar error distribution, then, model-agnostic refinement can work.
If that is just synthetic error augmentation based on your defined bbox jittering, that is just a data augmentation. It should work, especially can prevent overfitting, but I wonder that could have a big meaning or contribution.
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Thank you, I will give it a try
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Related Issues (20)
- ap HOT 2
- About how to apply this work on my own data? HOT 17
- calculate miss_prob , jitter_prob and inv_prob on other dataset HOT 1
- Statistics on latency and throughput HOT 1
- demo for a single image HOT 4
- About Fig.5 in your paper HOT 2
- Can we directly implement Posefix on Inference phase or should we have to do it from training itself? HOT 3
- Combining all the three datasets HOT 3
- I can't use my GPU? HOT 1
- Find data HOT 6
- converting model to ONNX for C++ inference with TensorRT HOT 3
- Is there any plan to provide pre-trained weights available? HOT 2
- inefficient heavy-CPU training HOT 7
- input image normalization HOT 4
- the reason behind the usage of render_onehot_heatmap for training target instead of render_gaussian_heatmap HOT 3
- the usage of the softmaxed heatmaps as the keypoint confidence scores
- Format of result.json HOT 1
- Missing modules in requirements
- FileNotFoundError: [Errno 2] No such file or directory: 'tmp_result_0.pkl' and IndexError: list index out of range HOT 3
- tensorflow 2
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