centernet_tensorflow_wilderface_voc

1. Introduction

• This is the unofficial implementation of the "CenterNet:Objects as Points".In my experiment, it was not based on the DLA34, Hourglass and other networks in the original paper. I simply modified shufflenetv2_1.0x and yolov3, and kept their feature extraction part, then connected to centernet_detect_head, and did not use dcn convolution.
• This is just a simple attempt to the effect of the algorithm.I only have one 1080ti,I did not use any data augmentation and any other tricks during training，so the model is not very good,still need more work to get good results.If it helps you, please give me a star.You can read my Chinese notes.https://zhuanlan.zhihu.com/p/68383078
• Official implementation:https://github.com/xingyizhou/CenterNet
• CenterNet:Objects as Points:https://arxiv.org/pdf/1904.07850.pdf
• Shufflenetv2 is modified from:https://github.com/timctho/shufflenet-v2-tensorflow
• Shufflenetv2:https://arxiv.org/abs/1807.11164
• Yolov3 is is modified from:https://github.com/wizyoung/YOLOv3_TensorFlow
• Yolov3:https://pjreddie.com/media/files/papers/YOLOv3.pdf

2. My experimental environment

• anaconda3、pycharm-community、python3.6、numpy1.14
• tensorflow1.12、slim
• cuda9.0、cudnn7.3
• opencv-python4.1
• gtx1080ti*1

3. datasets

• For single-target detection, trained on wilderface dataset with 12876 training images.
• For multi-target detection, trained on pascal-voc2012 dataset with 17125 training images.

4. Experimental result

4.1 Face detection

input_size:512x512
downsample_ratio:4.0
batch_size:14
global_steps:14800
epochs≈16
train_time≈3.7 hours

4.1.1 Network

4.1.2 result

4.2 Multi-target detection

input_size:512x512
downsample_ratio:8.0
batch_size:8
global_steps:70000
epochs≈32
train_time≈9.7 hours

4.2.1 Network

4.2.2 result(on training set,not very good on the test set)

4.3 inference time

environment：python3.6 gtx1080ti*1 intel-i7-8700k
model_name   			avg_time(ms)    input_size	 model_size(.pb)	
shufflenet-face			21.37		512x512		 20.5MB
yolo3_centernet_voc		25.23		512x512		 230MB

5. Run test demo(still need more work to get good results)

download ckpt filehttps://pan.baidu.com/s/1VrHv5U1wF1UP_r7JICbeZAcode:qqwx,and put them to ./shufflenet_face/ and ./yolo3_centernet_voc/,then run test_on_images.py

6.Create tfrecords to train

• The function about how to create and parse tfrecords is under folder img2tfrecords_detection.
• You only need to modify the following variables：img_path, txt_path, tfrecords.
• Then run img2tfrecords_pad.py to create tfrecords and parse it by parse-tfrecords.py.
• For detailed implementation, please see the relevant code under folder img2tfrecords_detection.