# clothes_classify **Repository Path**: hzy46/clothes_classify ## Basic Information - **Project Name**: clothes_classify - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2018-06-01 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Deep Fashion Analysis with Feature Map Upsampling and Landmark-driven Attention This repository is the code for [*Deep Fashion Analysis with Feature Map Upsampling and Landmark-driven Attention*](https://drive.google.com/file/d/1Dyj0JIziIrTRWMWDfPOapksnJM5iPzEi/view) in the First Workshop on Computer Vision for Fashion, Art and Design (Fashion) of ECCV 2018. ![network](https://images.gitee.com/uploads/images/2018/1127/221036_e55b13a1_725860.png "network.png") ### Requirements Python 3, PyTorch >= 0.4.0, and make sure you have installed TensorboardX: ``` pip install tensorboardX ``` ### Quick Start __1\. Prepare the Dataset__ Download the "Category and Attribute Prediction Benchmark" of the DeepFashion dataset from http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/AttributePrediction.html . Extract all the files to a folder and put all the images in a folder named "img". For example, if you choose to put the dataset to /home/user/datasets/benchmark1/, the structure of this folder will be: ``` benchmark1/ Anno/ Eval/ img/ README.txt ``` Please modify the variable "base_path" in src/const.py correspondingly: ``` # in src/const.py base_path = "/home/user/datasets/benchmark1/" ``` __2\. Create info.csv__ ``` python -m src.create_info ``` Please make sure you have modified the variable "base_path" in src/const.py, otherwise you may encounter a FileNotFound error. After the script finishes, you will find a file named "info.csv" in your "base_path". __3. Train the model__ To train the landmark branch solely, run: ``` python -m src.train --conf src.conf.lm ``` To train the landmark branch and the category/attribute prediction network jointly, run: ``` python -m src.train --conf src.conf.whole ``` ### Monitor your training You can monitor all the training losses and evaluation metrics via tensorboard. Please run: ``` tensorboard --logdir runs/ ``` Then visit localhost:6006 for detailed information. ### Results The following table shows the landmark localization results on the DeepFashion dataset. Numbers stands for normalized distances between prediction and the ground truth. Best results are marked in bold. | Methods | L.Collar | R.Collar | L.Sleeve | R.Sleeve | L.Waistline | R.Waistline | L.Hem | R.Hem | Avg. | |-------------|------------|------------|----------|----------|-------------|-------------|------------|------------|--------| | [FashionNet](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf) | 0.0854 | 0.0902 | 0.0973 | 0.0935 | 0.0854 | 0.0845 | 0.0812 | 0.0823 | 0.0872 | | [DFA](https://arxiv.org/pdf/1608.03049) | 0.0628 | 0.0637 | 0.0658 | 0.0621 | 0.0726 | 0.0702 | 0.0658 | 0.0663 | 0.0660 | | [DLAN](https://arxiv.org/pdf/1708.02044) | 0.0570 | 0.0611 | 0.0672 | 0.0647 | 0.0703 | 0.0694 | 0.0624 | 0.0627 | 0.0643 | | [Wang et al.](http://web.cs.ucla.edu/~yuanluxu/publications/fashion_grammar_cvpr18.pdf) | 0.0415 | 0.0404 | 0.0496 | **0.0449** | 0.0502 | 0.0523 | **0.0537** | **0.0551** | 0.0484 | | Ours | **0.0332** | **0.0346** | **0.0487** | 0.0519 | **0.0422** | **0.0429** | 0.0620 | 0.0639 | **0.0474** | The following table shows the category classification and attribute prediction results on the DeepFashion dataset. The two numbers in each cell stands for top-3 and top-5 accuracy. Best results are marked in bold. | Methods | Category | Texture | Fabric | Shape | Part | Style | All | |:---------------:|:----------------------:|:----------------------:|:--------------:|:----------------------:|:-----------------------:|:------------------:|:------------------:| | [WTBI](https://pdfs.semanticscholar.org/b185/f0a39384ceb3c4923196aeed6d68830a069f.pdf) | 43.73 \| 66.25 | 24.21 \| 32.65 | 25.38 \| 36.06 | 23.39 \| 31.26 | 26.31 \| 33.24 | 49.85 \| 58.68 | 27.46 \| 35.37 | | [DARN](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Huang_Cross-Domain_Image_Retrieval_ICCV_2015_paper.pdf) | 59.48 \| 79.58 | 36.15 \| 48.15 | 36.64 \| 48.52 | 35.89 \| 46.93 | 39.17 \| 50.14 | 66.11 \| 71.36 | 42.35 \| 51.95 | | [FashionNet](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf) | 82.58 \| 90.17 | 37.46 \| 49.52 | 39/30 \| 49.84 | 39.47 \| 48.59 | 44.13 \| 54.02 | 66.43 \| 73.16 | 45.52 \| 54.61 | | [Lu et al.](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lu_Fully-Adaptive_Feature_Sharing_CVPR_2017_paper.pdf) | 86.72 \| 92.51 | - | - | - | - | - | - | | [Corbiere et al.](https://arxiv.org/pdf/1709.09426) | 86.30 \| 92.80 | 53.60 \| 63.20 | 39.10 \| 48.80 | 50.10 \| 59.50 | 38.80 \| 48.90 | 30.50 \| 38.30 | 23.10 \| 30.40 | | [Wang et al.](http://web.cs.ucla.edu/~yuanluxu/publications/fashion_grammar_cvpr18.pdf) | 90.99 \| 95.78 | 50.31 \| 65.48 | 40.31 \| 48.23 | 53.32 \| 61.05 | 40.65 \| 56.32 | 68.70 \| **74.25** | 51.53 \| 60.95 | | Ours | **91.16** \| **96.12** | **56.17** \| **65.83** | **43.20** \| **53.52** | **58.28** \| **67.80** | **46.97** \| **57.42** | **68.82** \| 74.13 | **54.69** \| **63.74** | ### Citation The paper are going to be published soon. You can find the full text [here](https://drive.google.com/file/d/1Dyj0JIziIrTRWMWDfPOapksnJM5iPzEi/view).