We propose UniPose, a unified framework for human pose estimation, based on our “Waterfall” Atrous Spatial Pooling architecture, that achieves state-of-art-results on several pose estimation metrics. Current pose estimation methods utilizing standard CNN architectures heavily rely on statistical postprocessing or predefined anchor poses for joint localization. UniPose incorporates contextual seg- mentation and joint localization to estimate the human pose in a single stage, with high accuracy, without relying on statistical postprocessing methods. The Waterfall module in UniPose leverages the efficiency of progressive filter- ing in the cascade architecture, while maintaining multi- scale fields-of-view comparable to spatial pyramid config- urations. Additionally, our method is extended to UniPose- LSTM for multi-frame processing and achieves state-of-the- art results for temporal pose estimation in Video. Our re- sults on multiple datasets demonstrate that UniPose, with a ResNet backbone and Waterfall module, is a robust and efficient architecture for pose estimation obtaining state-of- the-art results in single person pose detection for both sin- gle images and videos.
Hello,
I'm trying to test the lstm_unipose model using the pretrained UniPose_LSTM_PennAction weights. I'm getting this error:
Error(s) in loading state_dict for unipose:
size mismatch for backbone.conv1.weight: copying a param with shape torch.Size([64, 4, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 3, 7, 7]).
size mismatch for decoder.last_conv.8.weight: copying a param with shape torch.Size([14, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([19, 256, 1, 1]).
size mismatch for decoder.last_conv.8.bias: copying a param with shape torch.Size([14]) from checkpoint, the shape in current model is torch.Size([19]).
bmartacho
commented
3 years ago
Hello, I'm trying to test the lstm_unipose model using the pretrained UniPose_LSTM_PennAction weights. I'm getting this error:
Error(s) in loading state_dict for unipose: size mismatch for backbone.conv1.weight: copying a param with shape torch.Size([64, 4, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 3, 7, 7]). size mismatch for decoder.last_conv.8.weight: copying a param with shape torch.Size([14, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([19, 256, 1, 1]). size mismatch for decoder.last_conv.8.bias: copying a param with shape torch.Size([14]) from checkpoint, the shape in current model is torch.Size([19]).