Stacked_Hourglass_Network_Keras

This is a Keras implementation for stacked hourglass network for single human pose estimation. The stacked hourglass network was proposed by [Stacked Hourglass Networks for Human Pose Estimation] (https://arxiv.org/abs/1603.06937). The official implementation built on top of torch is released under pose-hg-train, and pytorch version wrote by berapaw in repo pytorch-pose. Most of code for image processing and evaluation come from above repos.

Folder Structure

• data : data folder, mpii
• images : pictures for demo
• src : source code
  src/data_gen : data generator, augmentation and processnig code
  src/eval: evaluation code, eval callback
  src/net : net definition, hourglass network implementation
  src/tools: tool to draw accuracy curve and convert keras model to tf graph.
  top: top level entry to train/eval/demo network
• trained_models : folder to restore trained models.

Demo

• Download pre-trained model from shared drive and put them under trained_models
  BaiDu Pan: hg_s2_b1_mobile and hg_s2_b1
  Google Drive: hg_s2_b1_mobile and hg_s2_b1

• Run a quick demo to predict sample image

python demo.py --gpuID 0 --model_json ../../trained_models/hg_s2_b1/net_arch.json  --model_weights ../../trained_models/hg_s2_b1/weights_epoch89.h5  --conf_threshold 0.1 --input_image ../../images/sample.jpg

Train

MPII Data Preparation

• Download MPII Dataset and put its images under data/mpii/images
• The json mpii_annotations.json contains all of images' annotations including train and validation.

Train network

• Train from scratch, use python train.py --help to check all the valid arguments.

python train.py --gpuID 0 --epochs 100 --batch_size 24 --num_stack 2 --model_path ../../trained_models/hg_s2_b1_m

• Arguments:
  gpuID gpu id, epochs number of epoch to train, batch_size batch size of samples to train, num_stack number of hourglass stack, model_path path to store trained model snapshot

• Note: When mobile set as True, SeparableConv2D() is used instead of standard convolution, which is much smaller and faster.

• Continue training from previous checkpoint

python train.py --gpuID 0 --epochs 100 --batch_size 24 --num_stack 2 --model_path ../../trained_models/hg_s2_b1_m  --resume True --resume_model_json ../../trained_models/hg_s2_b1_m/net_arch.json --resume_model ../../trained_models/hg_s2_b1_m/weights_epoch15.h5 --init_epoch 16

Eval

Run evaluation on MPII validation dataset by using PCKh=0.5.

python eval.py --gpuID 1 --model_weights ../../trained_models/hg_s2_b1_mobile/weights_epoch70.h5  --model_json ../../trained_models/hg_s2_b1_mobile/net_arch.json --mat_file ../../trained_models/hg_s2_b1_mobile/preds.mat --num_stack 2

The validation score curve for hg_s2_b1 and hg_s2_b1_mobile

Issues

• Validation score drop significantly after 40 epochs. It is not stable as pytorch implementation. Did not root cause it yet.