MMDetection from dacon

Published by onesixx on 21-07-1121-07-11

https://yskim0.github.io/computer%20vision/2020/11/03/mmdetection-사용하기/
https://dacon.io/competitions/official/235672/codeshare : K-Fashion AI 경진대회 (CV관련)
https://youtu.be/VOn7T6NR1tc

MMdectection

open source object detection toolbox based on PyTorch.

K-Fashion AI 경진대회 baseline github을 참조하여 작성,
대회의 목적은 주어진 이미지자료를 학습하여, 각 클래스와 클래스별 세그멘테이션 마스크에 대해 예측하는 것 .

Installation

mmcv 설치후 , clone후 setup.

mmdetection github에서 v2.3.0 branch로 이동 후 Install.md 기본으로.
아래는 요약본

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
conda install -c pytorch pytorch torchvision -y

# install the latest mmcv
pip install mmcv-full

# install mmdetection
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -r requirements/build.txt
pip install -v -e .

Requirements

Linux or macOS (Windows is not currently officially supported)
Python 3.6+
PyTorch 1.3+
CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
GCC 5+
mmcv

$ cat /etc/issue
Ubuntu 18.04.5 LTS \n \l

$ python --version
Python 3.6.9

$ gcc --version
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0

$ python
Python 3.6.9 (default, Jul 17 2020, 12:50:27) 
[GCC 8.4.0] on linux
>>> import torch
>>> print(torch.__version__)
1.7.1+cu110
>>> quit()

$ nvidia-smi
Thu Jun 10 08:59:32 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  A100-SXM4-40GB      On   | 00000000:07:00.0 Off |                    0 |
| N/A   23C    P0    59W / 400W |      0MiB / 40537MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
....
+-------------------------------+----------------------+----------------------+
|   7  A100-SXM4-40GB      On   | 00000000:BD:00.0 Off |                    0 |
| N/A   27C    P0    59W / 400W |      0MiB / 40537MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

$dpkg -l | grep mmcv
$pip install mmcv-full
$pip install mmcv

가상환경 생성

Minicoda 설치후, 가상환경명이 sixxMM 일 경우, 해당 가상환경 생성 / 활성화

$ conda create -n sixxMM python=3.7 -y
$ conda activate sixxMM

dacon_utils 폴더 확인

pytorch, torchvision 설치 (버전 주의)

버전주의: mmdetection version==2.3.0일때, torch version == 1.5.0
pytorch official instructions를 참조하여 원하는 버전 설치

현재 1.7.1+cu110

$ conda install pytorch==1.5.0 torchvision==0.6.0 -c pytorch

MMdetection repo. 클론

$ mkdir  ~/dacon
$ cd ~/dacon

# CLONE mmdetection repository 
~/dacon/$ git clone --branch v2.3.0 https://github.com/open-mmlab/mmdetection.git

해당버전으로 clone함. branch == v2.3.0

참고

~/dacon/$ git clone https://github.com/dacon-ai/K-fashion-baseline.git

DOTA 데이터셋을 COCO 데이터셋으로 변환

아리랑 위성영상 AI 객체 검출 경진대회

MMDetection from dacon

https://yskim0.github.io/computer%20vision/2020/11/03/mmdetection-사용하기/
https://dacon.io/competitions/official/235672/codeshare : K-Fashion AI 경진대회 (CV관련)
https://youtu.be/VOn7T6NR1tc

MMdectection

Installation

Requirements

가상환경 생성

pytorch, torchvision 설치 (버전 주의)

MMdetection repo. 클론

참고

관련파일 설치

Requirements 설치 : cython , numpy 설치

Set up

DOTA 데이터셋을 COCO 데이터셋으로 변환

onesixx

vision

Nuclio 모델배포 : mmdetect

vision

Nuclio 모델배포 : custom model – Mask

vision

Nuclio 모델배포 : custom model

MMDetection from dacon

https://yskim0.github.io/computer%20vision/2020/11/03/mmdetection-사용하기/https://dacon.io/competitions/official/235672/codeshare : K-Fashion AI 경진대회 (CV관련)https://youtu.be/VOn7T6NR1tc

MMdectection

Installation

Requirements

가상환경 생성

pytorch, torchvision 설치 (버전 주의)

MMdetection repo. 클론

참고

관련파일 설치

Requirements 설치 : cython , numpy 설치

Set up

DOTA 데이터셋을 COCO 데이터셋으로 변환

onesixx

Related Posts

vision

Nuclio 모델배포 : mmdetect

vision

Nuclio 모델배포 : custom model – Mask

vision

Nuclio 모델배포 : custom model

https://yskim0.github.io/computer%20vision/2020/11/03/mmdetection-사용하기/
https://dacon.io/competitions/official/235672/codeshare : K-Fashion AI 경진대회 (CV관련)
https://youtu.be/VOn7T6NR1tc