A TensorFlow implementation of HRNet-32.The dataset used to train the model is the AI Challenger dataset.

Just for fun! A famous actor CXK in China and the keypoints estimated using the HRNet-32.

For more details, please refer to the paper and the dataset.

• python 3.6 or higher
• TensorFlow 1.11 or higher
• PyCharm

• Download the AI Challenger dataset.
• Convert the images in the AI Challenger dataset (train_images folder) to TFRecords by running the dataset.py. Please make sure that the dataset_root_path you used in the extract_people_from_dataset() function is the path of the AI Challenger dataset you saved in the previous step.
• Run the train.py!

Please note that the structure of the HRNet is complicated. I trained the HRNet-32 network using 2 Nvidia Titan V graphics cards. As the limited of the graphics memory（16 GB）, the max batch size I used was 2, and it took around 30 hours to finish 1 epoch (189176 steps). The model files were not uploaded. Please email me if you need them.

• Finish the 4 steps in the training.
• Make sure the dataset name, mode file name are corrected.
• Run the test.py!

The result images will be saved in the test_img folder. It will also generate the distances.npy and the classes.npy file, which will be used to calculate the AP50 and AP75 later.

• Run the evaluate.py.

It will print the AP50 and AP75 information in the command line.

• The loss information.
• The examples of images predicted by the network will be saved into the ./demo_img/ folder.

Epoch Number	example image 1	example image 2	example image 3	example image 4
epoch 0
epoch 1
epoch 2
epoch 3

• The result of testing images will be saved into the ./test_img/ floder.

Contact me: [email protected]