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Full pipeline for TianChi FashionAI clothes keypoints detection compitetion in TensorFlow
| Date | Stars |
|---|---|
| 2026-07-24 | 379 |
| 2026-07-25 | 379 |
| 2026-07-28 | 379 |
| 2026-07-30 | 379 |
| 2026-08-06 | 379 |
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# Hourglass, DHN and CPN model in TensorFlow for 2018-FashionAI Key Points Detection of Apparel at TianChi This repository contains codes of the re-implementent of [Stacked Hourglass Networks for Human Pose Estimation](https://arxiv.org/abs/1603.06937), [Simple Baselines for Human Pose Estimation and Tracking (Deconvolution Head Network)](https://arxiv.org/abs/1804.06208) and [Cascaded Pyramid Network for Multi-Person Pose Estimation](https://arxiv.org/abs/1711.07319) in TensorFlow for [FashionAI Global Challenge 2018 - Key Points Detection of Apparel](https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.11409106.5678.1.95b62e48Im9JVH&raceId=231648). Both the CPN(Cascaded Pyramid Network) and DHN (Deconvolution Head Network) here has several different backbones: ResNet50, SE-ResNet50, SE-ResNeXt50, [DetNet](https://arxiv.org/abs/1804.06215) or DetResNeXt50. I have also tried [Averaging Weights Leads to Wider Optima and Better Generalization](https://arxiv.org/abs/1803.05407) to ensemble models on the fly, although limited improvement was achieved. The pre-trained models of backbone networks can be found here: - [ResNet50](https://github.com/tensorflow/models/tree/master/official/resnet) - [SE-ResNet50](https://github.com/HiKapok/TF_Se_ResNe_t) - [SE-ResNeXt50](https://github.com/HiKapok/TF_Se_ResNe_t) ## Introduction The main goal of this competition is to detect the keypoints of the clothes' image colleted from Alibaba's e-commerce platforms. There are tens of thousands images in total five categories: blouse, outwear, trousers, skirt, dress. The keypoints for each category is defined as follows.  Almost all the codes was writen by myself and tested under TensorFlow 1.6, Python 3.5, Ubuntu 16.04. I tried to use the latest possible TensorFlow's best practice paradigm, like [tf.estimator](https://www.tensorflow.org/api_docs/python/tf/estimator) and [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers). Almost none py_func was used in my codes to maximize the performance. Augumentations like flip, rotate, random crop, color distort were used to reduce overfitting. The current performance of the model is ~0.4% in Normalized Error and got to ~20th-place in the second stage of the competition. About the model: - DetNet is better, perform almost the same as SEResNeXt, while SEResNet showed little improvement than ResNet - DHN has at least the same performance as CPN, but lack of thorough testing due to the limited time - Enforce the loss of invisible keypoints to zero gave better performance - OHKM is useful - It's bad to do gaussian blur on the predicted heatmap, but it's better to do gaussian blur on the target heatmaps for lower-level prediction - Ensemble of the heatmaps for fliped images is worser than emsemble of the predictions of fliped images, and do one quarter correction is also useful - Do cascaded prediction on whole network can eliminate the using of clothes detection network as well as larger input image - The native hourglass model was the worst but still have great potential, see the top solution of [here](http://human-pose.mpi-inf.mpg.de/#results) There are still other ways to further improve the performance but I didn't try those in this competition because of their limitations in applications, for example: - More larger input image size - More deeper backbone networks - Locate clothes first by detection networks - Multi-scale supervision for Stacked Hourglass Models - Extra-regressor to refine the location of keypoints - Multi-crop or multi-scale ensemble for single image predictions - It's maybe better to put all catgories into one model rather than training separate ones (the codes supports both mode) - It was also reported that replacing the bilinear-upsample of CPN to deconvolution did much better If you find it's useful to your research or competitions, any contribution or star to this repo is welcomed. ## Usage
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matched fp:fd00119baee1d3ed, topic:pose-estimation, readme:pose estimation
matched fp:fd00119baee1d3ed, topic:tensorflow