[OPGG] 인턴 연계 과정 - 미니맵 챔피언 인식
Updated:
오피지지 인턴 과정
오피지지 데이터 분석가 과정 교육을 수료하고 우수 교육생으로 한 달간 인턴 생활을 하게 되었습니다.
회사의 실무를 보면서 개인적으로 진행했던 과정에 대해 정리하고자 합니다.
본 내용들은 실제 인턴 생활에서 맡았던 업무와 관련 있다고 생각한 부분을 개인적으로 진행한 것 입니다.
실제 맡은 업무와는 상이할 수 있으니 참고 바랍니다.
미니맵 챔피언 인식
여기서는 yolov5 모델을 이용해서 리그 오브 레전드 게임 영상에서 미니맵의 챔피언을 인식해보겠습니다.
모델의 성능은 고려하지 않고 전체 과정을 간단하게 살펴보았습니다.
본 코드는 코랩으로 진행하였으며 혹시 직접 해보고 싶다면 경로 등에 유의하세요.
(특히나 저 같은 경우는 디렉토리를 변경하거나 절대경로를 많이 사용했습니다.)
1.환경 설정
# drive mount
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
- 드라이브 마운트
import os
import glob
import zipfile
import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import requests
import warnings
warnings.filterwarnings("ignore")
# change directory
%cd /content
# yolov5 git clone
!git clone https://github.com/ultralytics/yolov5.git
/content
Cloning into 'yolov5'...
remote: Enumerating objects: 10081, done.[K
remote: Total 10081 (delta 0), reused 0 (delta 0), pack-reused 10081[K
Receiving objects: 100% (10081/10081), 10.40 MiB | 24.54 MiB/s, done.
Resolving deltas: 100% (6990/6990), done.
# package for yolov5
%cd /content/yolov5/
!pip install -r requirements.txt
/content/yolov5
Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (3.2.2)
Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (1.19.5)
Requirement already satisfied: opencv-python>=4.1.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (4.1.2.30)
Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (7.1.2)
Collecting PyYAML>=5.3.1
Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)
[K |████████████████████████████████| 596 kB 5.4 MB/s
[?25hRequirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 9)) (2.23.0)
Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 10)) (1.4.1)
Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 11)) (1.10.0+cu111)
Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 12)) (0.11.1+cu111)
Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 13)) (4.62.3)
Requirement already satisfied: tensorboard>=2.4.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 16)) (2.7.0)
Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 20)) (1.1.5)
Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 21)) (0.11.2)
Collecting thop
Downloading thop-0.0.31.post2005241907-py3-none-any.whl (8.7 kB)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (3.0.6)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (0.11.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.3.2)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (1.24.3)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->-r requirements.txt (line 9)) (2021.10.8)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7.0->-r requirements.txt (line 11)) (3.10.0.2)
Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.42.0)
Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.17.3)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.0.1)
Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.12.0)
Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.35.0)
Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (57.4.0)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.3.6)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.37.0)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.6.1)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.4.6)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.1.4->-r requirements.txt (line 20)) (2018.9)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.4->tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.15.0)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (4.7.2)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.2.8)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (4.2.4)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.3.0)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 16)) (4.8.2)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.6.0)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 16)) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 16)) (3.1.1)
Installing collected packages: thop, PyYAML
Attempting uninstall: PyYAML
Found existing installation: PyYAML 3.13
Uninstalling PyYAML-3.13:
Successfully uninstalled PyYAML-3.13
Successfully installed PyYAML-6.0 thop-0.0.31.post2005241907
- yolov5 git을 클론하고 필요한 패키지 등을 설치합니다.
# create folder
%cd /content/yolov5/
folder_lst = ["lol/images", "lol/labels", "lol/origin/crop"]
for folder in folder_lst:
if not os.path.isdir(folder):
os.makedirs(folder)
del folder_lst
/content/yolov5
-
저는 yolov5 폴더 밑에 하위 폴더를 생성해두었습니다.
-
추후 학습에 사용할 이미지 파일을 넣을 것입니다.
# image, labels 압축 해제
!unzip -uq '/content/drive/MyDrive/yolo_lol/images.zip' -d "/content/yolov5/lol/images"
!unzip -uq '/content/drive/MyDrive/yolo_lol/labels.zip' -d "/content/yolov5/lol/labels"
-
저는 미리 이미지, 라벨링 파일을 드라이브에 업로드 해두었습니다.
-
제 드라이브에 있는 zip파일을 앞서 만들 폴더에 압축해제 하였습니다.
-
이미지, 라벨링 파일 만드는 방법은 아래를 참고해주세요.
2.동영상 로드 및 편집
-
저는 저의 게임 리플레이 영상(약5초)를 my_replay로 저장해두었습니다.
-
오른쪽 아래에 미니맵에 챔피언 초상화들이 확인됩니다.
-
이 챔피언들을 인식해보겠습니다.
# 동영상에서 이미지 추출
vidcap = cv2.VideoCapture('/content/drive/MyDrive/yolo_lol/my_replay.mp4')
count = 0
while vidcap.isOpened():
ret, image = vidcap.read()
# 캡쳐된 이미지를 저장하는 함수
try:
cv2.imwrite(f"./lol/origin/frame_{str(count).zfill(3)}.jpg", image)
print(f'Saved frame_{str(count).zfill(3)}.jpg')
except:
print("End Save image")
break
if count == 59:
break
count += 1
# 메모리 해제
vidcap.release()
Saved frame_000.jpg
Saved frame_001.jpg
Saved frame_002.jpg
Saved frame_003.jpg
Saved frame_004.jpg
Saved frame_005.jpg
Saved frame_006.jpg
Saved frame_007.jpg
Saved frame_008.jpg
Saved frame_009.jpg
Saved frame_010.jpg
Saved frame_011.jpg
Saved frame_012.jpg
Saved frame_013.jpg
Saved frame_014.jpg
Saved frame_015.jpg
Saved frame_016.jpg
Saved frame_017.jpg
Saved frame_018.jpg
Saved frame_019.jpg
Saved frame_020.jpg
Saved frame_021.jpg
Saved frame_022.jpg
Saved frame_023.jpg
Saved frame_024.jpg
Saved frame_025.jpg
Saved frame_026.jpg
Saved frame_027.jpg
Saved frame_028.jpg
Saved frame_029.jpg
Saved frame_030.jpg
Saved frame_031.jpg
Saved frame_032.jpg
Saved frame_033.jpg
Saved frame_034.jpg
Saved frame_035.jpg
Saved frame_036.jpg
Saved frame_037.jpg
Saved frame_038.jpg
Saved frame_039.jpg
Saved frame_040.jpg
Saved frame_041.jpg
Saved frame_042.jpg
Saved frame_043.jpg
Saved frame_044.jpg
Saved frame_045.jpg
Saved frame_046.jpg
Saved frame_047.jpg
Saved frame_048.jpg
Saved frame_049.jpg
Saved frame_050.jpg
Saved frame_051.jpg
Saved frame_052.jpg
Saved frame_053.jpg
Saved frame_054.jpg
Saved frame_055.jpg
Saved frame_056.jpg
Saved frame_057.jpg
Saved frame_058.jpg
Saved frame_059.jpg
-
우선 영상을 프레임 단위의 이미지로 저장하여야 합니다.
-
60fps로 5 x 60 = 300 이미지 중 60장만 저장해두겠습니다.
-
저는 이미지 파일들을 따로 저장하고 추후 이 코드는 실행하지 않았습니다.
# 샘플로 1개 이미지 가져오기
img_basic = cv2.imread('./lol/origin/frame_000.jpg', cv2.IMREAD_COLOR)
plt.imshow(cv2.cvtColor(img_basic, cv2.COLOR_BGR2RGB))
plt.show()
-
프레임 단위로 저장된 이미지 중 1개를 불러왔습니다.
-
잘 불러와지는 것이 확인 되네요.
# 미니맵 자르기
img = img_basic[1010:,1874:,:]
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.show()
-
여기선 수기로 잘라보면서 미니맵을 추출하였습니다.
-
실제로는 사용자별로 미니맵 크기가 다르므로 설정이 필요합니다.
-
cv2.matchTemplate()등을 사용해서 자동화 해야합니다. -
저는 체험의 목적이 강하기 때문에 그냥 진행하겠습니다.
# 원본 사진을 미니맵만 잘라 저장하기
origin_jpg_len = len(glob.glob('./lol/origin/*jpg'))
for i in range(origin_jpg_len-1):
# 원본 사진 불러오기
img_origin = cv2.imread(f'./lol/origin/frame_{str(i).zfill(3)}.jpg', cv2.IMREAD_COLOR)
# 미니맵 자르기
img = img_origin[1010:,1874:,:]
# 이미지 크기 변형
expand = cv2.resize(img, None, fx=512/430, fy=512/430, interpolation=cv2.INTER_CUBIC)
cv2.imwrite(f"./lol/origin/crop/crop_frame_{str(i).zfill(3)}.jpg", expand)
-
앞서 미니맵을 자른 과정을 모든 사진에 대해 반복합니다.
-
자른 미니맵은 crop_frame이란 이름으로 저장하였습니다.
-
이 이미지들은 라벨링에 사용할 것이며 실제 학습에 사용될 이미지입니다.
-
저는 미리 저장을 해두어 앞서 1.환경 설정에서 처럼 다른 경로에 풀어두었습니다.
# 이미지로 영상 만들기
paths = sorted(glob.glob('./lol/origin/crop/*.jpg'))
fps = 60
frame_array = []
for idx , path in enumerate(paths) :
img = cv2.imread(path)
height, width, layers = img.shape
size = (width, height)
frame_array.append(img)
out = cv2.VideoWriter('my_replay2.mp4', cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
for i in range(len(frame_array)):
# writing to a image array
out.write(frame_array[i])
out.release()
-
이제 자른 이미지들을 다시 영상으로 만들어 줍니다.
-
만들어진 영상은 detection에 사용할 것입니다.
-
위와 같이 미니맵 부분만 영상으로 잘 만들어졌습니다.
-
사실 이 방법 말고도
ffmpeg등을 사용하면 더 편리할 수도 있습니다. -
궁금하신분은 구글에 crop video를 검색하세요.
3.라벨링
-
라벨링은
labelImg를 설치하여 진행하였습니다. -
저는 라벨링 소개가 아니므로 자세한 방법은 구글링을 부탁드립니다.
-
모든 이미지에서 10개의 챔피언에 대해 바운딩 박스를 정하고 라벨링을 진행합니다.
-
생각보다 정말 오래 걸립니다..
4.Yolov5 실행
# yaml 만들기
%cd /content/yolov5/data
with open("lol.yaml", "w", encoding="utf8") as yaml:
# 현재 train, val 구분하지 않으므로 모두 같은 경로를 설정하였음
yaml.write("train: ./lol/images/")
yaml.write("\nval: ./lol/images/")
# number of classes
yaml.write("\n\nnc: 10")
# class names
yaml.write("\nnames: ['Syndra','Thresh','Jhin','Graves','Irellia','Xayah','Taliyah','Yone','Pyke','Poppy']")
/content/yolov5/data
-
모델을 실행하기 앞서 yaml 파일을 생성하여야 합니다.
-
yaml 파일은 train, val 이미지 경로, 인식할 클래스 수, 클래스 명을 입력합니다.
-
저는 직접 yaml 파일을 작성하고 수정하는 방식을 위해 코드로 작성했습니다.
-
그리고 학습시킬 이미지가 적어 train, val 모두 같은 경로를 설정했습니다.
# 학습시킬 이미지 파일 경로
%cd /content/yolov5/
# train
img_lst = sorted(glob.glob('/content/yolov5/lol/images/*.jpg'))
with open("train_list.txt", "w", encoding="utf8") as txt:
for i, title in enumerate(img_lst):
txt.write(title+"\n")
# val (현재 train과 동일)
img_lst = sorted(glob.glob('/content/yolov5/lol/images/*.jpg'))
with open("val_list.txt", "w", encoding="utf8") as txt:
for i, title in enumerate(img_lst):
txt.write(title+"\n")
/content/yolov5
- yolov5폴더 내에 학습시킬 이미지들의 경로를 모두 적은 txt 파일을 생성합니다.
# 학습 시작
!python train.py --img 512 --batch 10 --epochs 10 --data ./data/lol.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt --name lol_yolov5s_results
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
[34m[1mtrain: [0mweights=yolov5s.pt, cfg=./models/yolov5s.yaml, data=./data/lol.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=10, batch_size=10, imgsz=512, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=lol_yolov5s_results, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
[34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v6.0-113-g5ca5dd4 torch 1.10.0+cu111 CUDA:0 (Tesla K80, 11441MiB)
[34m[1mhyperparameters: [0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt to yolov5s.pt...
100% 14.0M/14.0M [00:00<00:00, 98.6MB/s]
Overriding model.yaml nc=80 with nc=10
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 40455 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 270 layers, 7046599 parameters, 7046599 gradients, 15.9 GFLOPs
Transferred 342/349 items from yolov5s.pt
Scaled weight_decay = 0.00046875
[34m[1moptimizer:[0m SGD with parameter groups 57 weight, 60 weight (no decay), 60 bias
[34m[1malbumentations: [0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed
[34m[1mtrain: [0mScanning 'lol/labels' images and labels...60 found, 0 missing, 0 empty, 0 corrupted: 100% 60/60 [00:00<00:00, 1026.48it/s]
[34m[1mtrain: [0mNew cache created: lol/labels.cache
[34m[1mval: [0mScanning 'lol/labels.cache' images and labels... 60 found, 0 missing, 0 empty, 0 corrupted: 100% 60/60 [00:00<?, ?it/s]
Plotting labels to runs/train/lol_yolov5s_results/labels.jpg...
[34m[1mAutoAnchor: [0m6.67 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Image sizes 512 train, 512 val
Using 2 dataloader workers
Logging results to [1mruns/train/lol_yolov5s_results[0m
Starting training for 10 epochs...
Epoch gpu_mem box obj cls labels img_size
0/9 1.38G 0.1274 0.06466 0.06789 140 512: 100% 6/6 [00:06<00:00, 1.02s/it]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 1.99it/s]
all 60 600 0.0087 0.06 0.00505 0.00135
Epoch gpu_mem box obj cls labels img_size
1/9 1.57G 0.1265 0.0677 0.06738 113 512: 100% 6/6 [00:03<00:00, 1.73it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.07it/s]
all 60 600 0.0209 0.0667 0.0129 0.00338
Epoch gpu_mem box obj cls labels img_size
2/9 1.57G 0.1247 0.07307 0.06695 151 512: 100% 6/6 [00:03<00:00, 1.73it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.08it/s]
all 60 600 0.0235 0.09 0.0173 0.00492
Epoch gpu_mem box obj cls labels img_size
3/9 1.57G 0.1218 0.07233 0.06695 100 512: 100% 6/6 [00:03<00:00, 1.72it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.12it/s]
all 60 600 0.0266 0.0932 0.0202 0.00721
Epoch gpu_mem box obj cls labels img_size
4/9 1.57G 0.1198 0.07748 0.06692 184 512: 100% 6/6 [00:03<00:00, 1.73it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.22it/s]
all 60 600 0.0189 0.0983 0.0201 0.00775
Epoch gpu_mem box obj cls labels img_size
5/9 1.57G 0.1197 0.08971 0.06654 199 512: 100% 6/6 [00:03<00:00, 1.73it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.22it/s]
all 60 600 0.0237 0.103 0.0212 0.00807
Epoch gpu_mem box obj cls labels img_size
6/9 1.57G 0.1167 0.08073 0.06652 156 512: 100% 6/6 [00:03<00:00, 1.74it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.17it/s]
all 60 600 0.0209 0.112 0.0193 0.00695
Epoch gpu_mem box obj cls labels img_size
7/9 1.57G 0.1151 0.08292 0.06587 176 512: 100% 6/6 [00:03<00:00, 1.76it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.11it/s]
all 60 600 0.0161 0.203 0.0201 0.00684
Epoch gpu_mem box obj cls labels img_size
8/9 1.57G 0.1135 0.08108 0.06632 159 512: 100% 6/6 [00:03<00:00, 1.75it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.13it/s]
all 60 600 0.0174 0.158 0.0191 0.00634
Epoch gpu_mem box obj cls labels img_size
9/9 1.57G 0.1122 0.08377 0.06593 128 512: 100% 6/6 [00:03<00:00, 1.75it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:01<00:00, 2.12it/s]
all 60 600 0.0173 0.163 0.0184 0.00598
10 epochs completed in 0.016 hours.
Optimizer stripped from runs/train/lol_yolov5s_results/weights/last.pt, 14.4MB
Optimizer stripped from runs/train/lol_yolov5s_results/weights/best.pt, 14.4MB
Validating runs/train/lol_yolov5s_results/weights/best.pt...
Fusing layers...
Model Summary: 213 layers, 7037095 parameters, 0 gradients, 15.9 GFLOPs
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 3/3 [00:03<00:00, 1.20s/it]
all 60 600 0.0237 0.103 0.0213 0.00813
Syndra 60 60 0 0 0 0
Thresh 60 60 0 0 0 0
Jhin 60 60 0 0 0 0
Graves 60 60 0.00894 0.05 0.00964 0.00502
Irellia 60 60 0.141 0.117 0.0597 0.0162
Xayah 60 60 0 0 0 0
Taliyah 60 60 0.0867 0.867 0.143 0.0601
Yone 60 60 0 0 0 0
Pyke 60 60 0 0 0 0
Poppy 60 60 0 0 0 0
Results saved to [1mruns/train/lol_yolov5s_results[0m
- 저는 빠른 실행을 위해 epoch도 적게 설정하고 모델도 s를 사용했습니다.
# detection
!python detect.py --source /content/drive/MyDrive/yolo_lol/my_replay2.mp4 --weights ./runs/train/lol_yolov5s_results/weights/best.pt
[34m[1mdetect: [0mweights=['./runs/train/lol_yolov5s_results/weights/best.pt'], source=/content/drive/MyDrive/yolo_lol/my_replay2.mp4, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False
YOLOv5 🚀 v6.0-113-g5ca5dd4 torch 1.10.0+cu111 CUDA:0 (Tesla K80, 11441MiB)
Fusing layers...
Model Summary: 213 layers, 7037095 parameters, 0 gradients, 15.9 GFLOPs
video 1/1 (1/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.031s)
video 1/1 (2/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.031s)
video 1/1 (3/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.030s)
video 1/1 (4/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.031s)
video 1/1 (5/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.030s)
video 1/1 (6/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.030s)
video 1/1 (7/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.030s)
video 1/1 (8/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.028s)
video 1/1 (9/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.028s)
video 1/1 (10/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.028s)
video 1/1 (11/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.028s)
video 1/1 (12/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.027s)
video 1/1 (13/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.026s)
video 1/1 (14/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.025s)
video 1/1 (15/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.025s)
video 1/1 (16/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.025s)
video 1/1 (17/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.025s)
video 1/1 (18/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (19/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (20/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (21/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (22/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (23/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (24/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (25/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (26/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (27/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (28/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (29/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (30/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (31/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (32/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (33/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (34/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (35/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (36/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (37/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (38/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (39/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (40/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (41/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (42/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (43/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (44/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (45/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (46/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (47/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (48/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (49/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (50/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (51/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (52/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (53/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (54/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (55/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (56/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (57/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (58/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (59/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (60/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (61/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (62/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (63/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (64/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (65/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (66/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (67/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (68/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (69/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (70/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (71/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (72/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (73/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (74/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (75/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (76/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (77/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (78/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (79/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (80/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (81/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (82/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (83/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (84/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (85/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (86/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (87/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (88/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (89/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (90/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (91/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (92/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (93/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (94/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (95/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (96/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (97/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (98/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (99/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (100/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (101/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (102/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (103/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (104/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (105/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (106/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (107/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (108/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (109/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (110/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (111/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (112/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (113/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (114/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (115/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (116/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (117/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (118/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (119/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (120/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (121/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (122/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (123/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (124/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (125/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (126/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (127/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (128/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (129/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (130/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (131/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (132/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (133/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (134/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (135/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (136/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (137/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (138/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (139/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (140/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (141/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (142/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (143/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (144/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (145/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (146/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (147/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (148/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (149/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (150/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (151/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (152/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (153/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (154/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (155/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (156/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (157/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (158/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (159/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (160/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (161/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (162/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (163/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (164/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (165/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (166/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (167/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (168/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (169/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (170/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (171/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (172/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (173/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (174/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (175/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (176/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (177/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (178/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (179/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (180/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (181/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (182/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (183/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (184/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (185/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (186/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (187/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (188/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (189/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (190/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (191/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (192/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (193/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (194/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (195/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (196/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (197/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (198/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (199/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (200/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (201/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (202/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (203/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (204/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (205/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (206/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (207/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (208/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (209/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (210/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (211/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (212/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (213/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (214/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (215/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (216/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (217/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (218/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (219/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (220/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (221/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (222/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (223/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (224/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (225/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (226/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (227/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (228/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (229/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (230/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (231/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (232/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (233/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (234/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (235/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (236/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (237/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (238/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (239/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (240/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (241/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (242/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (243/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (244/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (245/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (246/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (247/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (248/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (249/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (250/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (251/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (252/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (253/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (254/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (255/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (256/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (257/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (258/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (259/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (260/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (261/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (262/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (263/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (264/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (265/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (266/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (267/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (268/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (269/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (270/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (271/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (272/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (273/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (274/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (275/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (276/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (277/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (278/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (279/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (280/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (281/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (282/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (283/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (284/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (285/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (286/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (287/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (288/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (289/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (290/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (291/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (292/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (293/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (294/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (295/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.024s)
video 1/1 (296/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (297/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (298/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (299/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
video 1/1 (300/300) /content/drive/MyDrive/yolo_lol/my_replay2.mp4: 640x640 Done. (0.023s)
Speed: 0.5ms pre-process, 23.7ms inference, 0.3ms NMS per image at shape (1, 3, 640, 640)
Results saved to [1mruns/detect/exp[0m
-
마지막으로 detction 과정입니다.
-
앞서 저장한 my_replay2를 사용하였습니다.
-
runs/detect/exp 폴더에 detection이 된 영상이 저장됩니다.
-
학습 결과 등은 runs/train 폴더에 저장됩니다.
-
detection을 진행한 영상 캡처본입니다.
-
사실 이 영상은 위 학습에서 epoch를 200으로 설정했을 때 입니다.
-
정리하면서 다시 실행한다고 시간을 줄이려고 epoch을 10으로 해두었습니다.
-
챔피언 초상화를 어느정도 인식한 것으로 보이네요.
5.EOD
yolov5 모델을 이용해서 롤 게임영상에서 미니맵 초상화를 인식해보았습니다.
라벨링이나 yolov5 사용법에 대한 자세한 설명이 없기에 꼭 직접 찾아보시길 바랍니다.
여기선 모델 성능을 아예 고려하지 않았기 때문에 이런 식으로 작업이 가능하구나라고 봐주시면 감사하겠습니다.
그리고 여기선 detection으로 끝을 냈지만 옵션 설정에 따라 좌표도 얻을 수 있습니다.
이를 활용해서 다양한 분석이 가능할 것 같네요.
Leave a comment