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Codestates collaboration project with NEXTLab to detect, classify, and track car in cameras using EfficientNetv2

Python 100.00%

cnn yolov4 efficientnet efficientnet-keras

project-nextlab-cnn-efficientnet's Introduction

NEXTLab Car Detection Project

Example

Main Contributers

Jeongtae Shin(@Klassikcat): Project Management, Model build, Engineering
Jaewoong Lee(@colin19950703): Augmentation, model build, Enigeering
Hongkyun Kim(@ghdrbs0302): Data Inspection, Test data Labeling, Engineering

Installation

!pip install -U git+https://github.com/Klassikcat/project-NEXTLab-CNN-EfficientNet

pre-trained Weights

https://drive.google.com/file/d/1UyVnR5Pi7NvzD3LPnkllGEyn8afMi5jF/view?usp=sharing

Structure

project-NEXTLab
┖ core
  ┖ config.py               # Tensorflow Configuration
  ┖ utils.py
┖ model
  ┖ EfficientNet.py         # Model block and builder
  ┖ initializers.py         # from EfficientNetv2 (https://arxiv.org/abs/2104.00298)
  ┖ layer.py                # from EfficietnNetv2
  ┖ model.py                # from EfficientNetv2
  ┖ params.py               # from EfficientNetv2
┖ utils
  ┖ augmentation            # Data augmentation tools
    ┖ augmentation.py
    ┖ launcher_augment.py
    ┖ launcher_classname.py
  ┖ from_folder             
    ┖ loader.py             # get train/valid/test dataset from directory
  ┖ tfrecord                 
    ┖ tfrecordMaker         # Make TFRecord
    ┖ tfrecordViewer        # Visualize, Load TFRecord 
  ┖ unicodeUtil.py          # Korean utilities assemble jamo
  ┖ visuialization.py       # Plots(data distribution, heatmap)
setup.py                    # installer
readme.md

Stack

Objective

Making EfficientNetv2 application that distinguish 322 cars by using photos captured by traffic camera
Traffic enforcement, Parking lot management.

Data

Train/Validatation Data

Uses modified "자동차 차종/연식/번호판 인식용 영상" from AIHub: https://aihub.or.kr/aidata/27727
Uses 522 classes in "세단", "SUB", "해치백", "승합"

Test Data

Road Camera dataset from NEXTLab: https://www.nextlab.ai/
5016 images belonging to 80 classes

Data Distribution

approximately 82% of data is korean cars(hyundai, KIA)

approximately 75% of data is korean cars(sonata, etc...)

51% sedan, 28.8% SUV, 10% Hatchback, 9% van

mainly consists with 2010~2018 cars

Data Augmentation

use these augmentations taking the possible road situations into account.
Drop the class with less than 30 pieces of data in order to avoid data leakage
randomly pick 300 data. if the class has less than 30 data, proceed Data Augmentation for equal distribution of data

Model Structure

EfficientNetB0(224, 224) with 30 layer freeze

Train_validation accuracy/loss

top 1 accuracy/loss

train: 0.9051/0.2755
valid: 0.8960/0.3769

top 3 accuracy

train: 0.9880
valid: 0.9717

Further Work

[ ] Increase valid/test accuracy by making Ensemble model

[ ] Label Smoothing

[ ] Fix all false label

project-nextlab-cnn-efficientnet's People

Contributors

Watchers

project-nextlab-cnn-efficientnet's Issues

Yolo_v4 Colab에서 실행 시 detect_image(img_path) 실행 에러

아직 욜로를 실행을 못하는 중입니다...

재웅님이 공유해준 yolo.zip 파일을 코랩에 압축을 풀어서 코드를 실행하고 아래 이미지 코드를 실행했습니다. (경로만 수정해줬습니다)

img_path = '/content/data/road.mp4'
detect_video(img_path)
print("Done!!")

실행 시 발생한 에러는 다음과 같습니다.

[에러코드]
DisabledFunctionError: cv2.imshow() is disabled in Colab, because it causes Jupyter sessions
to crash; see jupyter/notebook#3935.
As a substitution, consider using
from google.colab.patches import cv2_imshow

from google.colab.patches import cv2_imshow <-- 코드를 추가하여서 런타임 다시 실행하여 코드를 실행해봤을 때도 똑같은 에러가 발생했습니다.

from google.colab.patches import cv2_imshow
img_path = '/content/data/road.mp4'
detect_video(img_path)
print("Done!!")

혹시나 detect_video를 실행하지 않고 이미지만 불러오려고 아래 코드를 실행했습니다.

img_path = '/content/data/road_kor.jpg'
cv2_imshow(img_path)

[에러코드]
AttributeError Traceback (most recent call last)
in ()
1 from google.colab.patches import cv2_imshow
2 img_path = '/content/data/road_kor.jpg'
----> 3 cv2_imshow(img_path)

/usr/local/lib/python3.7/dist-packages/google/colab/patches/init.py in cv2_imshow(a)
20 image.
21 """
---> 22 a = a.clip(0, 255).astype('uint8')
23 # cv2 stores colors as BGR; convert to RGB
24 if a.ndim == 3:

AttributeError: 'str' object has no attribute 'clip'

위의 에러코드 23번을 보면 'cv2 stores colors as BGR; convert to RGB' 라는걸 확인해서 혹시나 하는 생각으로 detect_video 함수에 있는 40번줄에 있는 코드를
result = cv2.cvtColor(np.array(result), cv2.COLOR_RGB2BGR) 에서 컬러 수정해주는거를
result = cv2.cvtColor(np.array(result), cv2.COLOR_BGR2RGB) 로 수정해봤지만 이 또한 4번에 적어둔 에러코드와 똑같은게 뜹니다.

cv2 버전을 확인했는데
'4.1.2' 버전입니다

마지막 첨부사진은 현재 제 경로입니다.

[Yolo v4 issue :: 모델이 1회용이 된 거 같습니다]

https://drive.google.com/file/d/1PEMV7wxIcqM3uGw3wbX8FUOQLrIOluxQ/view?usp=sharing

1.위의 링크로 들어가 용량문제로 첨부되지 못한 데이터, pre-train weight, 모델을 다운받아주세요.

2.압축을 풀고 YOLO-v4 폴더에 넣어주세요
아래와 같이 구성되어야합니다.
YOLO-v4
└checkpoints
| core
| data
| yolov4-416
| Yolo_v4.ipynb

[ issue]

주피터 노트북으로 main문(object detection부분)을 실행후 또 다시 main문을 호출하게 되면 objectdetection이 실행되지 않습니다.
이때 load model 블럭을 호출하고 다시 main문을 실행하게 되면 정상적으로 작동합니다.
무슨 모델이 1회만 사용하고 동작하지 않는 것 같습니다.