RON is an efficient object detection system as descibed in This CVPR paper.
This repository contains code of the re-implement of RON following the above paper. Now almost all of the implementation details matches the open-source version by the the author of RON.
The code is modified from SSD-Tensorflow. You can use the code to train/evaluate your network for object detection task.
For more details (including dataset prepare), please refer to README of SSD-Tensorflow.
Update: Recently, I have found some details of the detection pipeline maybe sub-optimal in this implementation. So if you would like use codes here for further research, I recommend you to refer to this repo which includes many of my latest insights about detection.
Update:
- Add SSD preprocesing method using Tensorflow ops [zero ground truth fixed]
- Modify details of the network to match the original Caffe code
- Add NMS using Tensorflow ops to support two mode
- Fix most part of the matching strategy between ground truth and anchors
- Replica GPU training support (If you are using Tensorflow 1.5.0+, then rename the replicate_model_fn.py)
- Add voc eval with debug
- Add realtime eval, using class-wise bboxes-select and nms
- Add support for training use vgg16_reducedfc model converted from pytorch, you can get from here
- Other important fixes [2018.03.21]
- Make all anchors on different layers be matched together, to avoid some suboptimal matching results
- Refactor anchors matching pipeline
- Fix attribute 'difficult' missing problem in the TFRecords dataset
- Model-320 (reduced version) trained on VOC07+12 dataset now is available at here, the heavier one needs to be trained by yourself(may I will also update a trained model later)
Last Update:
- Maybe the code here is one of the few Tensorflow detection pipelines in open-source that could be trained to get 0.7+mAP(not using weights converted from other framework), except the official object-detection API.
- You can use these code snippets for your own project
- Model trained with heavier VGG-16 backbone got to 0.715mAP(0.734mAP using VOC12 evaluation alogorithm) at 120k steps on my side without other tricks.
- You can try other matching threshold, different learning rate policy, more augumentation, other loss weights to get more improvement. I didn't try these but picked one simple setting.
- If you would like to train using reduced_vgg from here, please make sure that you swaped image channals from the default RGB order into BGR order, and the pixel value should in [-128, 128].
Note: Model trained (07+12 VOC-train and test on VOC07-test) using the initial version of this code can only get to 0.45~0.55mAP, clone the latest version will give you much better performance at 0.7+mAP(needs ~120k steps, training with ron_net.py and evaluation with eval_ron_network.py). Futher improvement is still going on.
Here are some demo result images of reduced-version RON-320 detector(with a heavier vgg16-backbone 0.74mAP is reported in paper) trained using this code:
