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Lite-HRNet

The Lite-HRNet(Lite-HighResolutionNetwork) is a light weight backbone for high-resolution dependent computer vision tasks like Semantic Segmentation and Pose Estimation. It is a lightweight variant of HRNet(HighResolutionalNetwork), in which the authors replaces 1*1 convolutional blocks in HRNet with their innovative Cross Channel Weighting(CCW) to reduce both the time and spatial complexity. In this case, we implement the Lite-HRNet using Mindspore for the task of human pose estimation.

Performance and Pretrained Model

On COCO2017:

Arch Input Size #Params FLOPs AP AP50 AP75 AR AR50 ckpt
Naive Lite-HRNet-18 256x192 0.7M 194.8M 0.608 0.873 0.683 0.645 0.886 Mindspore Download
Wider Naive Lite-HRNet-18 256x192 1.3M 311.1M 0.643 0.893 0.725 0.680 0.901 Mindspore Download
Lite-HRNet-18 256x192 1.1M 205.2M 0.624 0.884 0.703 0.663 0.895 Mindspore Download
Lite-HRNet-18 384x288 1.1M 461.6M 0.668 0.895 0.739 0.702 0.909 Mindspore Download
Lite-HRNet-30 256x192 1.8M 319.2M 0.655 0.895 0.737 0.694 0.908 Mindspore Download
Lite-HRNet-30 384x288 1.8M 717.8M 0.698 0.905 0.781 0.731 0.918 Mindspore Download

On MPII:

Arch Input Size #Params FLOPs Mean [email protected] ckpt
Naive Lite-HRNet-18 256x192 0.7M 194.8M 0.842 0.270 Mindspore Download
Wider Naive Lite-HRNet-18 256x256 1.3M 311.1M 0.859 0.288 Mindspore Download
Lite-HRNet-18 256x256 1.1M 205.2M 0.842 0.262 Mindspore Download
Lite-HRNet-30 256x256 1.8M 319.2M 0.861 0.280 Mindspore Download

Prepare Dataset

You have to download COCO2017 dataset and annotation in COCODataset and make sure that the your path is organized as following:

Lite-HRNet/
    ├── imgs
    ├── src
    ├── annotations
        ├──person_keypoints_train2017.json
        └──person_keypoints_train2017.json
    ├── train2017
    └── val2017

Requirements

Train

python src/train.py
Optional Args Default Value Explanation
--model_type lite_18 The type of model
--target_res 256x192 Resolution of resized input images
--checkpoint_path ./ckpts Where to save or load checkpoints
--train_batch 32 Training batch size
--save_checkpoint_steps 500 The interval of saving checkpoints
--load_ckpt False Load a checkpoint and continue training

Evaluate

python src/eval.py
Optional Args Default Value Explanation
--model_type lite_18 The type of model
--target_res 256x192 Resolution of resized input images
--checkpoint_path ./ckpts Where to load checkpoints
--output_path ./eval_result Where to save the evaluate result json file

Infer

python src/eval.py
Optional Args Default Value Explanation
--model_type lite_18 The type of model
--target_res 256x192 Resolution of resized input images
--checkpoint_path ./ckpts Where to load checkpoints
--infer_data_root ./infer_data Where to load the input data
--out_data_root ./out_data Where to save the output data

Result

图片