Michio JP

Michio JP


Conditional DETR | Encode and Decode Transformer for Object Detection

Conditional DETR

This repository is an official implementation of the ICCV 2021 paper "Conditional DETR for Fast Training Convergence".


The DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings and that the spatial embeddings make minor contributions, increasing the need for high-quality content embeddings and thus increasing the training difficulty.


Our conditional DETR learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box (Figure 1). This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for stronger backbones DC5-R50 and DC5-R101.

Model Zoo

We provide conditional DETR and conditional DETR-DC5 models. AP is computed on COCO 2017 val.

Conditional DETR-R5050449041.020.644.359.3model 
Conditional DETR-DC5-R50504419543.723.947.660.1model 
Conditional DETR-R101506315642.821.746.660.9model 
Conditional DETR-DC5-R101506326245.026.148.962.8model 


  1. The numbers in the table are slightly differently from the numbers in the paper. We re-ran some experiments when releasing the codes.
  2. "DC5" means removing the stride in C5 stage of ResNet and add a dilation of 2 instead.



  • Python >= 3.7, CUDA >= 10.1
  • PyTorch >= 1.7.0, torchvision >= 0.6.1
  • Cython, COCOAPI, scipy, termcolor

The code is developed using Python 3.8 with PyTorch 1.7.0. First, clone the repository locally:

git clone https://github.com/Atten4Vis/ConditionalDETR.git

Then, install PyTorch and torchvision:

conda install pytorch=1.7.0 torchvision=0.6.1 cudatoolkit=10.1 -c pytorch

Install other requirements:

cd ConditionalDETR
pip install -r requirements.txt


Data preparation

Download and extract COCO 2017 train and val images with annotations from http://cocodataset.org. We expect the directory structure to be the following:

├── annotations/  # annotation json files
└── images/
    ├── train2017/    # train images
    ├── val2017/      # val images
    └── test2017/     # test images


To train conditional DETR-R50 on a single node with 8 gpus for 50 epochs run:

bash scripts/conddetr_r50_epoch50.sh


python -m torch.distributed.launch \
    --nproc_per_node=8 \
    --use_env \
    main.py \
    --resume auto \
    --coco_path /path/to/coco \
    --output_dir output/conddetr_r50_epoch50

The training process takes around 30 hours on a single machine with 8 V100 cards.

Same as DETR training setting, we train conditional DETR with AdamW setting learning rate in the transformer to 1e-4 and 1e-5 in the backbone. Horizontal flips, scales and crops are used for augmentation. Images are rescaled to have min size 800 and max size 1333. The transformer is trained with dropout of 0.1, and the whole model is trained with grad clip of 0.1.


To evaluate conditional DETR-R50 on COCO val with 8 GPUs run:

python -m torch.distributed.launch \
    --nproc_per_node=8 \
    --use_env \
    main.py \
    --batch_size 2 \
    --eval \
    --resume <checkpoint.pth> \
    --coco_path /path/to/coco \
    --output_dir output/<output_path>

Note that numbers vary depending on batch size (number of images) per GPU. Non-DC5 models were trained with batch size 2, and DC5 with 1, so DC5 models show a significant drop in AP if evaluated with more than 1 image per GPU.


Conditional DETR is released under the Apache 2.0 license. Please see the LICENSE file for more information.


  title       = {Conditional DETR for Fast Training Convergence},
  author      = {Meng, Depu and Chen, Xiaokang and Fan, Zejia and Zeng, Gang and Li, Houqiang and Yuan, Yuhui and Sun, Lei and Wang, Jingdong},
  booktitle   = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year        = {2021}

Download Details: 

Author: Atten4Vis

Download The Source Code : https://github.com/Atten4Vis/ConditionalDETR/archive/refs/heads/main.zip 

GITHUB: https://github.com/Atten4Vis/ConditionalDETR 

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Docker Applikationen mit Visual Studio Code debuggen

Mit dem integrierten Debugger von Visual Studio Code lassen sich ASP.NET Core bzw. .NET Core Applikationen einfach und problemlos debuggen. Der Debugger unterstützt auch Remote Debugging, somit lassen sich zum Beispiel .NET Core Programme, die in einem Docker-Container laufen, debuggen.

Als Beispiel Applikation reicht das Default-Template für MVC Applikationen dotnet new mvc

$ md docker-core-debugger
$ cd docker-core-debugger
$ dotnet new mvc

Mit dotnet run prüfen wir kurz, ob die Applikation läuft und unter der Adresse http://localhost:5000 erreichbar ist.

$ dotnet run
$ Hosting environment: Production
$ Content root path: D:\Temp\docker-aspnetcore
$ Now listening on: http://localhost:5000

Die .NET Core Applikation builden wir mit dotnet build und publishen alles mit Hilfe von dotnet publish

$ dotnet build
$ dotnet publish -c Debug -o out --runtime linux-x64

Dabei gilt es zu beachten, dass die Build Configuration mit -c Debug gesetzt ist und das Output Directory auf -o out. Sonst findet Docker die nötigen Binaries nicht. Für den Docker Container brauchen wir nun ein Dockerfile, dass beim Start vorgängig den .NET Core command line debugger (VSDBG) installiert. Das Installations-Script für VSDBG ist unter https://aka.ms/getvsdbgsh abfrufbar.

FROM microsoft/aspnetcore:latest

RUN apt-get update \
    && apt-get install -y --no-install-recommends \
       unzip procps \
    && rm -rf /var/lib/apt/lists/* \
    && curl -sSL https://aka.ms/getvsdbgsh | bash /dev/stdin -v latest -l /vsdbg

COPY ./out .
ENTRYPOINT ["dotnet", "docker-core-debugger.dll"]

Den Docker Container erstellen wir mit dem docker build Kommando

$ docker build -t coreapp .

und starten die Applikation mit docker run.

$ docker run -d -p 8080:80 --name coreapp coreapp

Jetzt muss Visual Studio Code nur noch wissen, wo unsere Applikation läuft. Dazu definieren wir eine launch.json vom Typ attach und konfigurieren die nötigen Parameter für den Debugger.

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    "configurations": [
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            "type": "coreclr",
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                "pipeCwd": "${workspaceRoot}"

            "logging": {
                "engineLogging": true,
                "exceptions": true,
                "moduleLoad": true,
                "programOutput": true

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Nun muss der dotnet Prozess ausgewählt werden. Der Visual Studio Code Debugger verbindet sich darauf mit VSDBG und wir können wie gewohnt unseren Code debuggen. Dazu setzen wir einen Breakpoint in der Index-Action des HomeControllers und rufen mit dem Browser die URL http://localhost:8080/ auf.


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