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mix-unmix
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# MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection (CVPR2022) This is the Pytorch implementation of our paper : <br> **MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection** <br> IEEE/CVF International Conference on Computer Vision (CVPR), 2022 <br> [[arXiv](https://arxiv.org/abs/2111.10958)] <p align="center"> <img src="teaser/mum_phase.png", width="85%"> </p> # Installtion & Setup We follow the installation precess of Unbiased Teacher official repo (https://github.com/facebookresearch/unbiased-teacher) ## Download the code - For your convenience, we provide the code and model weights in [zip](https://drive.google.com/file/d/18AS8by_YOpQwQURgQelIFl8WqHgteiJt/view?usp=sharing) ### Prerequisites - Linux or macOS with Python ≥ 3.6 - PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation. ### Build Detectron2 from Source - We find the latest(v0.6) package of Detectron2 occur the error with our code. - Therefore, please install the matched(v0.5) version of Detectron2 as follows: ```shell # get the Detectron2 v0.5 package wget https://github.com/facebookresearch/detectron2/archive/refs/tags/v0.5.zip # unzip unzip v0.5.zip # install python -m pip install -e detectron2-0.5 ``` ### Install other requirements ```shell pip install -r requirements.txt ``` ### Dataset download 1. Download COCO & VOC dataset 2. Organize the dataset as following: ```shell mix-unmix/ └── datasets/ ├── coco/ │ ├── train2017/ │ ├── val2017/ │ └── annotations/ │ ├── instances_train2017.json │ └── instances_val2017.json ├── VOC2007 │ ├── Annotations │ ├── ImageSets │ └── JPEGImages └── VOC2012 ├── Annotations ├── ImageSets └── JPEGImages ``` # Evaluation - Performance table and Model Weights (weight files are already included in zip file) | Backbone | Protocols | AP50 | AP50:95 | Model Weights | | :-----: | :---------: | :---: | :---: | :----------------------------------------------------------------------------------------: | | R50-FPN | COCO-Standard 1% | 40.06 | 21.89 | [link](https://drive.google.com/file/d/1NxHjtz4ioFnCfRJSxskqP_zkbnWVnIeu/view?usp=sharing) | | R50-FPN | COCO-Additional | 63.30 | 42.11 | [link](https://drive.google.com/file/d/1GhQlkurzdRAngdMp6Ut492TYD2AN20XB/view?usp=sharing) | | R50-FPN | VOC07 (VOC12) | 78.94 | 50.22 | [link](https://drive.google.com/file/d/1HVAMThGp9SR5BpmQEBFautuF_pQlkkQW/view?usp=sharing) | | R50-FPN | VOC07 (VOC12 / COCO20cls) | 80.45 | 52.31 | [link](https://drive.google.com/file/d/1Ywlnnxfi3fYwZK5jZKY7a8E7R0KP1SUs/view?usp=sharing) | | Swin | COCO-Standard 0.5% | 34.25 | 16.52 | [link](https://drive.google.com/file/d/1EsHK3FRCzBlt-pcaIf93agfxdQJE6Idz/view?usp=sharing) | - Run Evaluation w/ R50 in COCO ```shell python train_net.py \ --eval-only \ --num-gpus 1 \ --config configs/mum_configs/coco.yaml \ MODEL.WEIGHTS weights/<your weight>.pth ``` - Run Evaluation w/ R50 in VOC ```shell python train_net.py \ --eval-only \ --num-gpus 1 \ --config configs/mum_configs/voc.yaml \ MODEL.WEIGHTS weights/<your weight>.pth ``` # Train We use 4 GPUs (A6000 or V100 32GB) to achieve the paper results. - Train the MUM under 1% COCO-supervision (ResNet-50) ```shell python train_net.py \ --num-gpus 4 \ --config configs/mum_configs/coco.yaml \ ``` - Train the MUM under VOC07 as labeled set and VOC12 as unlabeled set ```shell python train_net.py \ --num-gpus 4 \ --config configs/mum_configs/voc.yaml \ ``` ## Swin - Download ImageNet pretrained weight of swin-t in [link](https://drive.google.com/file/d/1j95KPUoVl1PK49yxpQOvigKHcl2eTt5B/view?usp=sharing) - mv pretrained weight to weights folder ```shell mv swin_tiny_patch4_window7_224.pth weights/ ``` - Run Evaluation w/ Swin in COCO ```shell python train_net.py \ --eval-only \ --num-gpus 1 \ --config configs/mum_configs/coco_swin.yaml \ MODEL.WEIGHTS weights/<your weight>.pth ``` - Train under 0.5% COCO-supervision ```shell python train_net.py \ --num-gpus 4 \ --config configs/mum_configs/coco_swin.yaml \ ``` ## Mix/UnMix code block ### Mixing code block - Generate mix mask ```shell mask = torch.argsort(torch.rand(bs // ng, ng, nt, nt), dim=1).cuda() img_mask = mask.view(bs // ng, ng, 1, nt, nt) img_mask = img_mask.repeat_interleave(3, dim=2) img_mask = img_mask.repeat_interleave(h // nt, dim=3) img_mask = img_mask.repeat_interleave(w // nt, dim=4) ``` - Mixing image tiles ```shell img_tiled = images.tensor.view(bs // ng, ng, c, h, w) img_tiled = torch.gather(img_tiled, dim=1, index=img_mask) img_tiled = img_tiled.view(bs, c, h, w) ``` ### Unmixing code block - Generate inverse mask to unmix ```shell inv_mask = torch.argsort(mask, dim=1).cuda() feat_mask = inv_mask.view(bs//ng,ng,1,nt,nt) feat_mask = feat_mask.repeat_interleave(c,dim=2) feat_mask = feat_mask.repeat_interleave(h//nt, dim=3) feat_mask = feat_mask.repeat_interleave(w//nt, dim=4) ``` - Unmixing feature tiles ```shell feat_tiled = feat.view(bs//ng,ng,c,h,w) feat_tiled = torch.gather(feat_tiled, dim=1, index=feat_mask) feat_tiled = feat_tiled.view(bs,c,h,w) ``` # Acknowledgements We use Unbiased-teacher official code as our baseline. And also we use Timm repository to implement Swin Transformer easily. - [Unbiased-Teacher](https://github.com/facebookresearch/unbiased-teacher) - [Timm](https://github.com/rwightman/pytorch-image-models)
