Pose Estimation Pose Estimation Contributing. We provide a demo script to run mmdet for hand detection, and mmpose for hand pose estimation. Note: Flip test is used. This is a longer version of the HRNet paper published in CVPR 2019. Our new work High-Resolution Representations for Labeling Pixels and Regions is available at HRNet.Our HRNet has been applied to a wide range of vision tasks, such as image classification, objection detection, semantic segmentation and facial landmark. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. Multi-person Human Pose Estimation with HRNet in PyTorch. Contribute to open-mmlab/mmpose development by creating an account on GitHub. HRNet + OCR + SegFix: Rank #1 (84.5) in Cityscapes leaderboard. Performance ImageNet pretrained models. This is an official implementation of semantic segmentation for HRNet. Clothes Segmentation. Detailed settings or configurations are in configs/hrnet.. This is an unofficial implementation of the paper Deep High-Resolution Representation Learning for Human Pose Estimation. Hand Box Model Preparation: The pre-trained hand box estimation model can be found in det model zoo. [2022/07/31] Training code with predicted camera is released. [2022/08/16] Pretrained model with HRNet-W48 backbone is available. Note: Models are trained with the newly released code and ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" - GitHub - jin-s13/COCO-WholeBody: ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" A much stronger baseline model dark_pose_hrnet_w48+ with WholeBody AP 66.1% is provided for research purpose. Pytorch: 1.2.1 and later: 3d-pose-baseline: A simple baseline for 3d human pose estimation in tensorflow. [2022/07/25] HybrIK is now supported in Alphapose! The code is a simplified version of the official code with the ease-of-use in mind.. Pose estimation plays a critical role in human-centered vision applications. Codes and pretrained models are in HRNets for Image Classification. YOLOv3YOLOv4YOLOv5 YOLOv4YOLOv5Ubuntu Performance ImageNet pretrained models. This is the official code of High-Resolution Representations for Facial Landmark Detection.We extend the high-resolution representation (HRNet) [1] by augmenting HRNet: Deep High-Resolution Representation Learning for Visual Recognition. Person detector has person AP of 60.9 on COCO test-dev2017 dataset. Pitch recognition; Sound classification; Automatic speech recognition with Wav2Vec2; Video Tutorials. Browse through over 200 neural network models, both public and from Intel, and pick the right one for your solution. Code for pose estimation is available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch - HRNet HRNet is a stronger backbone, and acheives superior performance on human pose estimation, semantic segmentation, object detection, face alignment, and so on. Abstract. Presented at ICCV 17. pose_resnet_152 is our previous work of Simple Baselines for Human Pose Estimation and Tracking. Our new work High-Resolution Representations for Labeling Pixels and Regions is available at HRNet.Our HRNet has been applied to a wide range of vision tasks, such as image classification, objection detection, semantic segmentation and facial landmark. Multi-person Human Pose Estimation with HRNet in PyTorch. ; GFLOPs is for convolution and linear layers only. @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{WangSCJDZLMTWLX19, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Jingdong Wang and Ke Sun and [2020/07/05] A very nice blog from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation. [2022/04/26] Achieve SOTA results by adding the 3DPW dataset for training. Lightning is intended for latency-critical applications, while Thunder is intended for HRNet + OCR is reproduced here. This task assigns a category label (including background label) to each pixel in an item.The evaluation metrics is the average precision including ,, computed over masks. This is an unofficial implementation of the paper Deep High-Resolution Representation Learning for Human Pose Estimation. HRNet + OCR is reproduced here. HRNet is a stronger backbone, and acheives superior performance on human pose estimation, semantic segmentation, object detection, face alignment, and so on. FPS: 30 FONT_SCALE: @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{WangSCJDZLMTWLX19, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Jingdong Wang and Ke Sun and Note: Flip test is used. Figure 4: Results of landmark and pose estimation. 8-bit weights and activations are typically used. Abstract. Codes and pretrained models are in HRNets for Image Classification. See benchmark.md for more information. FPS: 30 FONT_SCALE: Figure 4 shows the results of landmark and pose estimation. CropNet: Cassava Disease Detection; Super resolution; HRNet model inference for semantic segmentation; Audio Tutorials. Any contribution from the community to improve PYSKL is appreciated. Note: Models are trained with the newly released code and For some models, 8-bit weights and 16-bit activations Lite Pose slides|paper|video. ; GFLOPs is for convolution and linear layers only. This is a longer version of the HRNet paper published in CVPR 2019. DEVICE_TYPE: cuda cuda:0 cpu cpu cuda:0 . HRNet + OCR is reproduced here. Support for various datasets. See the paper. OCR: object contextual represenations pdf. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. Code for pose estimation is available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch - HRNet DEVICE_TYPE: cuda cuda:0 cpu cpu cuda:0 . Our new work High-Resolution Representations for Labeling Pixels and Regions is available at HRNet.Our HRNet has been applied to a wide range of vision tasks, such as image classification, objection detection, semantic segmentation and facial landmark. Detailed settings or configurations are in configs/hrnet.. All models are trained on COCO train2017 set and evaluated on COCO val2017 set. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. Introduction. This is an official implementation of semantic segmentation for HRNet. Introduction. --use-frames: . A modified HRNet combined with semantic and instance multi-scale context achieves SOTA panoptic segmentation result on the Mapillary Vista challenge. Assume that you have already installed mmdet. [][][][] BlueSky QQ : 352707983 Github . 15 See the paper. MoveNet is an ultra fast and accurate model that detects 17 keypoints of a body. The code is a simplified version of the official code with the ease-of-use in mind.. HRNet: Deep High-Resolution Representation Learning for Visual Recognition. For details see SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation by Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang and Shu-Tao Xia. Codes and pretrained models are in HRNets for Image Classification. This is a longer version of the HRNet paper published in CVPR 2019. lightweight-human-pose-estimation-3d: Real-time 3D multi-person pose estimation demo in PyTorch. Introduction. 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection News! or [pdf at arXiv]. PYSKL is an OpenSource Project under the Apache2 license. Pitch recognition; Sound classification; Automatic speech recognition with Wav2Vec2; Video Tutorials. [2020/07/05] A very nice blog from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. Introduction. 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection Figure 4 shows the results of landmark and pose estimation. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). pose_resnet_152 is our previous work of Simple Baselines for Human Pose Estimation and Tracking. News! Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. Browse through over 200 neural network models, both public and from Intel, and pick the right one for your solution. OCR: object contextual represenations pdf. Figure 4: Results of landmark and pose estimation. Figure 4 shows the results of landmark and pose estimation. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. We provide a demo script to run mmdet for hand detection, and mmpose for hand pose estimation. OpenVINO backend can be used for fast inference on CPU. Thanks Google and UIUC researchers. [2022/08/16] Pretrained model with HRNet-W48 backbone is available. or [pdf at arXiv]. 8-bit weights and activations are typically used. HRNet + OCR + SegFix: Rank #1 (84.5) in Cityscapes leaderboard. [2022/04/26] Achieve SOTA results by adding the 3DPW dataset for training. Poseur: Direct Human Pose Regression with Transformers, Weian Mao*, Yongtao Ge*, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton van den Hengel In: European Conference on Computer Vision (ECCV), 2022 arXiv preprint (arXiv 2201.07412) (* equal contribution). Hand Box Model Preparation: The pre-trained hand box estimation model can be found in det model zoo. OpenMMLab Pose Estimation Toolbox and Benchmark. This is a preview for Poseur, which Contribute to open-mmlab/mmpose development by creating an account on GitHub. [][][][] BlueSky QQ : 352707983 Github . 15 [2022/07/31] Training code with predicted camera is released. Pose estimation plays a critical role in human-centered vision applications. Accepted by TPAMI. HRNetV2 ImageNet pretrained models are now available! For significant contributions (like supporting a novel & important task), a corresponding part will be added to our updated tech report, while the contributor will also be added to the author list.. Any user can open a PR to contribute to PYSKL. Pose Estimation Contribute to open-mmlab/mmpose development by creating an account on GitHub. [1] Original FP32 model source [2] FP32 model checkpoint [3] Quantized Model: For models quantized with post-training technique, refers to FP32 model which can then be quantized using AIMET. For some models, 8-bit weights and 16-bit activations 16. Pytorch: 1.2.1 and later: 3d-pose-baseline: A simple baseline for 3d human pose estimation in tensorflow. Pitch recognition; Sound classification; Automatic speech recognition with Wav2Vec2; Video Tutorials. Poseur: Direct Human Pose Regression with Transformers. A repository for storing models that have been inter-converted between various frameworks. PYSKL is an OpenSource Project under the Apache2 license. Lite Pose slides|paper|video. DEVICE_TYPE: cuda cuda:0 cpu cpu cuda:0 . A modified HRNet combined with semantic and instance multi-scale context achieves SOTA panoptic segmentation result on the Mapillary Vista challenge. Multi-person demo with pose-tracking is available. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. Pytorch: 1.2.1 and later: 3d-pose-baseline: A simple baseline for 3d human pose estimation in tensorflow. OpenMMLab Pose Estimation Toolbox and Benchmark. A repository for storing models that have been inter-converted between various frameworks. This is a preview for Poseur, which [2022/07/25] HybrIK is now supported in Alphapose! ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" - GitHub - jin-s13/COCO-WholeBody: ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" A much stronger baseline model dark_pose_hrnet_w48+ with WholeBody AP 66.1% is provided for research purpose. Simple Baselines for Human Pose Estimation and Tracking News. Person detector has person AP of 60.9 on COCO test-dev2017 dataset. - GitHub - PINTO0309/PINTO_model_zoo: A repository for storing models that have been inter-converted between various frameworks. This is an unofficial implementation of the paper Deep High-Resolution Representation Learning for Human Pose Estimation. Action recognition; View on GitHub: The model is offered on TF Hub with two variants, known as Lightning and Thunder. lightweight-human-pose-estimation-3d: Real-time 3D multi-person pose estimation demo in PyTorch. --use-frames: . Multi-person Human Pose Estimation with HRNet in PyTorch. The code is fully compatible with the official pre-trained weights and the results are the same of the [2022/07/25] HybrIK is now supported in Alphapose! [2020/03/12] Support Figure 4: Results of landmark and pose estimation. [2022/04/27] is ready to use. Multi-person demo with pose-tracking is available. Clothes Segmentation. Action recognition; View on GitHub: A repository for storing models that have been inter-converted between various frameworks. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. The model is offered on TF Hub with two variants, known as Lightning and Thunder. Pose Estimation is a general problem in Computer Vision where the goal is to detect the position and orientation of a person or an object. For models optimized with QAT, refers to model checkpoint with fine-tuned weights. In this work, we present an efficient high-resolution network, Lite-HRNet, for human pose estimation. CropNet: Cassava Disease Detection; Super resolution; HRNet model inference for semantic segmentation; Audio Tutorials. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. [1] Original FP32 model source [2] FP32 model checkpoint [3] Quantized Model: For models quantized with post-training technique, refers to FP32 model which can then be quantized using AIMET. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. ; Our new work Deep High-Resolution Representation Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. This is an official implementation of semantic segmentation for HRNet. [2022/08/16] Pretrained model with HRNet-W48 backbone is available. ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" - GitHub - jin-s13/COCO-WholeBody: ECCV2020 paper "Whole-Body Human Pose Estimation in the Wild" A much stronger baseline model dark_pose_hrnet_w48+ with WholeBody AP 66.1% is provided for research purpose. Contributing. Human Pose Estimation; Additional image tutorials. Action recognition; View on GitHub: See the paper. For models optimized with QAT, refers to model checkpoint with fine-tuned weights. Introduction. HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (CVPR 2020) News [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) HRNet-DEKR! HRNet: Deep High-Resolution Representation Learning for Visual Recognition. [2020/03/12] Support 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection YOLOv3YOLOv4YOLOv5 YOLOv4YOLOv5Ubuntu Browse through over 200 neural network models, both public and from Intel, and pick the right one for your solution. [2022/04/26] Achieve SOTA results by adding the 3DPW dataset for training. Support for various datasets. OpenVINO backend can be used for fast inference on CPU. The code is fully compatible with the official pre-trained weights and the results are the same of the For significant contributions (like supporting a novel & important task), a corresponding part will be added to our updated tech report, while the contributor will also be added to the author list.. Any user can open a PR to contribute to PYSKL. See benchmark.md for more information. [1] Original FP32 model source [2] FP32 model checkpoint [3] Quantized Model: For models quantized with post-training technique, refers to FP32 model which can then be quantized using AIMET. Accepted by TPAMI. or [pdf at arXiv]. We achieve faster training speed and higher accuracy than other popular codebases, such as HRNet. Assume that you have already installed mmdet. For some models, 8-bit weights and 16-bit activations Detailed settings or configurations are in configs/hrnet.. YOLOv3YOLOv4YOLOv5 YOLOv4YOLOv5Ubuntu This task assigns a category label (including background label) to each pixel in an item.The evaluation metrics is the average precision including ,, computed over masks. ; Our new work Deep High-Resolution Representation However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{WangSCJDZLMTWLX19, title={Deep High-Resolution Representation Learning for Visual Recognition}, author={Jingdong Wang and Ke Sun and Poseur: Direct Human Pose Regression with Transformers, Weian Mao*, Yongtao Ge*, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton van den Hengel In: European Conference on Computer Vision (ECCV), 2022 arXiv preprint (arXiv 2201.07412) (* equal contribution). In this work, we present an efficient high-resolution network, Lite-HRNet, for human pose estimation. Lightning is intended for latency-critical applications, while Thunder is intended for Any contribution from the community to improve PYSKL is appreciated. High-resolution networks (HRNets) for facial landmark detection News [2020/03/13] Our paper is accepted by TPAMI: Deep High-Resolution Representation Learning for Visual Recognition. All models are trained on COCO train2017 set and evaluated on COCO val2017 set. We hope proposed SimDR will motivate the community to rethink the design of coordinate representation for 2D human pose estimation. [2020/03/12] Support Simple Baselines for Human Pose Estimation and Tracking News. 16. HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (CVPR 2020) News [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) HRNet-DEKR! HRNetV2 ImageNet pretrained models are now available! For models optimized with QAT, refers to model checkpoint with fine-tuned weights. ; Our new work Deep High-Resolution Representation For significant contributions (like supporting a novel & important task), a corresponding part will be added to our updated tech report, while the contributor will also be added to the author list.. Any user can open a PR to contribute to PYSKL. Presented at ICCV 17. 8-bit weights and activations are typically used. The code is a simplified version of the official code with the ease-of-use in mind.. Clothes Segmentation. The code is fully compatible with the official pre-trained weights and the results are the same of the [2022/07/31] Training code with predicted camera is released. Performance ImageNet pretrained models. Human Pose Estimation; Additional image tutorials. Poseur: Direct Human Pose Regression with Transformers. CropNet: Cassava Disease Detection; Super resolution; HRNet model inference for semantic segmentation; Audio Tutorials. - GitHub - PINTO0309/PINTO_model_zoo: A repository for storing models that have been inter-converted between various frameworks. 16. This is the official code of High-Resolution Representations for Facial Landmark Detection.We extend the high-resolution representation (HRNet) [1] by augmenting 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection Multi-person demo with pose-tracking is available. Thanks Google and UIUC researchers. Any contribution from the community to improve PYSKL is appreciated. Pose estimation plays a critical role in human-centered vision applications. PYSKL is an OpenSource Project under the Apache2 license. ; GFLOPs is for convolution and linear layers only. in case of Human Pose Estimation. higher-hrnet-w32-human-pose-estimation GitHub repo and licensed under Apache License Version 2.0. This is a preview for Poseur, which Assume that you have already installed mmdet. Pose Estimation is a general problem in Computer Vision where the goal is to detect the position and orientation of a person or an object. For details see SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation by Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang and Shu-Tao Xia. HRNetV2 ImageNet pretrained models are now available! This is the official code of High-Resolution Representations for Facial Landmark Detection.We extend the high-resolution representation (HRNet) [1] by augmenting All models are trained on COCO train2017 set and evaluated on COCO val2017 set. MoveNet is an ultra fast and accurate model that detects 17 keypoints of a body. HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation (CVPR 2020) News [2021/04/12] Welcome to check out our recent work on bottom-up pose estimation (CVPR 2021) HRNet-DEKR! FPS: 30 FONT_SCALE: We provide a demo script to run mmdet for hand detection, and mmpose for hand pose estimation. Simple Baselines for Human Pose Estimation and Tracking News. A modified HRNet combined with semantic and instance multi-scale context achieves SOTA panoptic segmentation result on the Mapillary Vista challenge. Abstract. We hope proposed SimDR will motivate the community to rethink the design of coordinate representation for 2D human pose estimation. HRNet + OCR + SegFix: Rank #1 (84.5) in Cityscapes leaderboard. Support for various datasets. HRNet is a stronger backbone, and acheives superior performance on human pose estimation, semantic segmentation, object detection, face alignment, and so on. Pose Estimation is a general problem in Computer Vision where the goal is to detect the position and orientation of a person or an object. Presented at ICCV 17. Thanks Google and UIUC researchers. High-resolution networks (HRNets) for facial landmark detection News [2020/03/13] Our paper is accepted by TPAMI: Deep High-Resolution Representation Learning for Visual Recognition. 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection Hand Box Model Preparation: The pre-trained hand box estimation model can be found in det model zoo. Person detector has person AP of 60.9 on COCO test-dev2017 dataset. See benchmark.md for more information. Note: Flip test is used. Poseur: Direct Human Pose Regression with Transformers, Weian Mao*, Yongtao Ge*, Chunhua Shen, Zhi Tian, Xinlong Wang, Zhibin Wang, Anton van den Hengel In: European Conference on Computer Vision (ECCV), 2022 arXiv preprint (arXiv 2201.07412) (* equal contribution). For details see SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation by Yanjie Li, Sen Yang, Peidong Liu, Shoukui Zhang, Yunxiao Wang, Zhicheng Wang, Wankou Yang and Shu-Tao Xia. More than 83 million people use GitHub to discover, fork, and contribute to over 200 million projects. Poseur: Direct Human Pose Regression with Transformers. lightweight-human-pose-estimation-3d: Real-time 3D multi-person pose estimation demo in PyTorch. Introduction. This task assigns a category label (including background label) to each pixel in an item.The evaluation metrics is the average precision including ,, computed over masks. OpenVINO backend can be used for fast inference on CPU. - GitHub - PINTO0309/PINTO_model_zoo: A repository for storing models that have been inter-converted between various frameworks. News! OCR: object contextual represenations pdf. 2019HRNetHigh-Resolution Net2DHuman Pose EstimationKeypoint Detection Lite Pose slides|paper|video. Human Pose Estimation; Additional image tutorials. MoveNet is an ultra fast and accurate model that detects 17 keypoints of a body. [2022/04/27] is ready to use. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). [2022/04/27] is ready to use. higher-hrnet-w32-human-pose-estimation GitHub repo and licensed under Apache License Version 2.0. In this work, we present an efficient high-resolution network, Lite-HRNet, for human pose estimation. OpenMMLab Pose Estimation Toolbox and Benchmark. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. Lightning is intended for latency-critical applications, while Thunder is intended for Contributing. [2020/07/05] A very nice blog from Towards Data Science introducing HRNet and HigherHRNet for human pose estimation. Note: Models are trained with the newly released code and higher-hrnet-w32-human-pose-estimation GitHub repo and licensed under Apache License Version 2.0. [][][][] BlueSky QQ : 352707983 Github . 15 High-resolution networks (HRNets) for facial landmark detection News [2020/03/13] Our paper is accepted by TPAMI: Deep High-Resolution Representation Learning for Visual Recognition. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML. Accepted by TPAMI. Code for pose estimation is available at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch - HRNet in case of Human Pose Estimation. --use-frames: . The model is offered on TF Hub with two variants, known as Lightning and Thunder. We hope proposed SimDR will motivate the community to rethink the design of coordinate representation for 2D human pose estimation. in case of Human Pose Estimation. pose_resnet_152 is our previous work of Simple Baselines for Human Pose Estimation and Tracking.
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