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0. Hardware Requirements

It is recommended to execute our code using V100 32GB or RTX A6000, as we have tested the following scripts and docker images on these devices.

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1. Environment Preparation (MVATeam1)

1.1 Cloning Our Repository

Please clone our GitHub repository and switch to the submit branch:

git clone https://github.com/Howardkhh/MVATeam1.git
cd MVATeam1
git switch submit

The default branch is called main, whereas the code we had prepared before 23:59 PST on April 21, 2023, is located in the submit branch.

However, please follow this main branch's tutorial since it contains the most up-to-date information.

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1.2 Preparing Private Test Data

Before launching the Docker container, ensure that the data folder is linked to our repository. The data folder should contain the private test folder named mva2023_sod4bird_private_test. Inside this folder, the annotation file should be named private_test_coco_empty_ann.json and placed under the annotations subfolder.

data
├── mva2023_sod4bird_private_test
│   ├── annotations
│   │   └── private_test_coco_empty_ann.json
│   └── images
│       ├── 00001.jpg
│       └── ...
└── ...

ln -s <absolute path to the data folder> ./data
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1.3 Launching Docker Container

The docker container can be launched by:

make start

After launching the Docker container, the working directory should be the MVATeam1 folder.

All of the commands below should be executed in MVATeam1 directory.

Next, please install the MMDetection development package inside the container by:

make post_install

The docker image is built with the Dockerfile within the root of our repository, and has been uploaded to the Dockerhub. Invoking make start will also download the image.

As inferencing using MMDetection requires a larger shared memory space (/dev/shm), we set the Docker container to have 32GB of shared memory space. The size could be changed by editing the SHMEM_SIZE variable in the Makefile.

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2. Model Weights

Our model weights are too large to be uploaded to GitHub. Please download the pre-trained weights from Google Drive:

gdown --folder https://drive.google.com/drive/folders/1zNZTGmlRVsPSpxrVwpik17I2w3XLbqDc?usp=share_link

A single folder named final should be downloaded to the root of our repository.

Important: Please ensure that all files are fully downloaded (the progress bars should be at 100%).

The size of the final folder should be 36 GB.

du -sh final
# output: 36G        final
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3. Folder Contents

Please make sure that the files are in the following structure (only the most important files are listed):

MVATeam1
├── configs
├── data
│   ├── mva2023_sod4bird_private_test
│   │   ├── annotations
│   │   │   └── private_test_coco_empty_ann.json
│   │   └── images
│   │       ├── 00001.jpg
│   │       └── ...
│   └── ...
├── ensemble
├── final
│   ├── baseline_centernet
│   │   ├── centernet_resnet18_140e_coco_inference.py
│   │   └── latest.pth
│   ├── cascade_mask_internimage_xl_fpn_20e_nwd_finetune_merged_train
│   │   └── latest.pth
│   ├── cascade_nwd_paste_howard
│   │   ├── cascade_nwd_paste_howard.zip
│   │   └── latest.pth
│   ├── cascade_rcnn_r50_fpn_40e_coco_finetune_sticker
│   │   └── latest.pth
│   ├── cascade_rcnn_r50_fpn_40e_coco_nwd_finetune
│   │   ├── cascade_rcnn_r50_fpn_40e_coco_finetune_original.py
│   │   └── latest.pth
│   ├── internimage_h_nwd
│   │   ├── intern_h_public.json
│   │   ├── intern_h_public_nosahi.json
│   │   ├── intern_h_public_nosahi_randflip.json
│   │   └── latest.pth
│   ├── internimage_xl_no_nwd
│   │   └── latest.pth
│   └── internimage_xl_nwd
│   	├── intern_xl_public_nosahi_randflip.json
│   	└── latest.pth
├── inference_private_parallel.sh
└── ...
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4. Inference

A bash script for inferencing has been prepared in the MVATeam1 folder.

Please change the variable <NUM_GPUS> depending on your system, and then run the script under MVATeam1 directory:

bash inference_private_parallel.sh <NUM_GPUS>
# E.g.:
bash inference_private_parallel.sh 4

Note: There is an alternative bash script called inference_private.sh, which is a sequential version that runs on a single GPU and produces the same output. However, we strongly recommend using the parallel version due to the extended inference time of our models.

This script automatically inferences with all of our models and runs the ensemble process. Upon completion, the final predictions are saved as results.json and zipped into results_team1.zip.

The final results to be evaluated is the file results_team1.zip under the MVATeam1 folder!

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Troubleshooting

1. Cannot Download Model Weights

In case of download quota exceeded, we provide another link. Please make sure the tarball is properly extracted.

gdown 'https://drive.google.com/u/3/uc?id=1fRW-3CVf3t5EQUjYfh6BQN27VvTRTn9a&export=download'
tar zxvf final.tar.gz

2. Internimage Custom Imports

If you encounter KeyError: "CenterNet: 'InternImage is not in the models registry'"
or KeyError: "CenterNet: 'CustomLayerDecayOptimizerConstructor is not in the models registry'",
please add

import mmdet_custom  # noqa: F401,F403
import mmcv_custom  # noqa: F401,F403

to the python script you are running.

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Auxiliary Training Data

Open sourced datasets are allowed in this contest. We use an auxiliary dataset (https://www.kaggle.com/datasets/nelyg8002000/birds-flying) to augment our data during training. We have uploaded it to the Google Drive. For detailed usage, please refer to the class: MVAPasteBirds in MVATeam1/mmdet/datasets/pipelines/transforms.py. It can be downloaded with the following commands:

cd data
gdown --fuzzy https://drive.google.com/file/d/1dcPNszz3h7Ntq0G_OfjEzGMwc9OAv7uQ/view?usp=share_link
unzip birds.zip
rm birds.zip
cd ..