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End-to-End Object Detection with Transformers
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I am unable to reproduce the results in DETR model: No improvement in train_class_error or any other metric when training DETR on COCO17 without any changes to the original code #527

Closed RashoAli closed 2 years ago

RashoAli commented 2 years ago

If you do not know the root cause of the problem, and wish someone to help you, please post according to this template:

Instructions To Reproduce the Issue:

  1. what changes you made (git diff) or what code you wrote None (train on one GPU)

  2. what exact command you run: just run from Pycharm

  3. what you observed (including full logs):

    
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5. please simplify the steps as much as possible so they do not require additional resources to
     run, such as a private dataset.
- No private dataset was used

## Expected behavior:

If there are no obvious error in "what you observed" provided above,
please tell us the expected behavior.
- No improvement in the "train_class_error" or any other metric when using DETR out of the box

If you expect the model to converge / work better, note that we do not give suggestions
on how to train a new model.
Only in one of the two conditions we will help with it:
(1) You're unable to reproduce the results in DETR model zoo.
(2) It indicates a DETR bug.

## Environment:

Provide your environment information using the following command:

python -m torch.utils.collect_env



Python version: 3.8.13 (default, Mar 28 2022, 06:59:08) [MSC v.1916 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19044-SP0
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.22.3
[pip3] torch==1.12.0
[pip3] torchaudio==0.12.0
[pip3] torchvision==0.13.0
[conda] blas                      1.0                         mkl
[conda] cudatoolkit               11.3.1               h59b6b97_2
[conda] mkl                       2021.4.0           haa95532_640
[conda] mkl-service               2.4.0            py38h2bbff1b_0
[conda] mkl_fft                   1.3.1            py38h277e83a_0
[conda] mkl_random                1.2.2            py38hf11a4ad_0
[conda] numpy                     1.22.3           py38h7a0a035_0
[conda] numpy-base                1.22.3           py38hca35cd5_0
[conda] pytorch                   1.12.0          py3.8_cuda11.3_cudnn8_0    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                0.12.0               py38_cu113    pytorch
[conda] torchvision               0.13.0               py38_cu113    pytorch
alcinos commented 2 years ago

We trained DETR with a batch size of 64. If you are training on only one gpu, I doubt that you are able to have such a big batch size, which explains the difference in performance.