[CV] Keras YOLO로 Raccoon Dataset을 이용한 Object Detection

Raccoon Dataset을 YOLO V3 Model로 학습시켜서 Image & Video에 Object Detection을 한번 수행해 보겠습니다.

• Dataset

GitHub - experiencor/keras-yolo3: Training and Detecting Objects with YOLO3

---

Library Download

Object Detection 학습을 위한 Library를 다운로드 하겠습니다.

!pwd
!rm -rf DLCV
!git clone https://github.com/chulminkw/DLCV.git
# DLCV 디렉토리가 Download되고 DLCV 밑에 Detection과 Segmentation 디렉토리가 있는 것을 확인
!ls -lia 
!ls -lia DLCV

!pip install tensorflow-gpu==1.15.2  
!pip install keras==2.3.0

• 주의사항: init.py는 반드시 import tensorflow, import keras 이전에 수행되어야 합니다. 만일 tensorflow, keras 설치한 뒤 아래의 import tensorflow, import keras를 먼저 수행하였으면 메뉴-> 런타임 -> 런타임 다시 시작을 누르신 뒤 init.py를 수정합니다.

import os

# keras backend 디렉토리 이동. 
os.chdir('/usr/local/lib/python3.6/dist-packages/keras/backend')

# 기존 __init__.py 삭제하고 새로운 __init__.py를 download 
!rm -rf __init__.py
!rm -rf __pycache__
!wget https://raw.githubusercontent.com/chulminkw/DLCV/master/colab_tf115_modify_files/__init__.py

# GPU가 세팅되어 있지 않으면 상단 메뉴에서 런타임->런타임 유형 변경에서 GPU를 선택한 후 런타임 다시 시작을 선택하고 처음 부터인 tensorflow, keras 설치 부터 다시 시작. 
import tensorflow as tf
import keras

print(tf.__version__)
print(keras.__version__)

# gpu가 세팅드어 있는지 확인. 
tf.test.gpu_device_name()

---

Raccoon Dataset Download

Object Detection 학습을 위한 Raccoon Dataset을 다운로드 하겠습니다.

 

• Racoon 데이터 세트를 git clone으로 복사합니다. git clone https://github.com/experiencor/raccoon_dataset.git

GitHub - experiencor/raccoon_dataset: The dataset is used to train my own raccoon detector and I blogged about it on Medium

# /content/DLCV/data 디렉토리에 raccoon_dataset을 다운로드함. 
%cd /content/DLCV/data/
!git clone https://github.com/experiencor/raccoon_dataset.git
# raccoon_dataset을 raccoon으로 디렉토리 이름 변경하고 확인 
!mv raccoon_dataset raccoon
!ls -lia

• VOC Annotation

# annotation과 image 디렉토리 설정. annotation디렉토리에 있는 파일 확인. 
import os
from pathlib import Path

#HOME_DIR = str(Path.home())
# 코랩 버전은 HOME_DIR을 /content 로 설정합니다.
HOME_DIR = '/content'

ANNO_DIR = os.path.join(HOME_DIR, 'DLCV/data/raccoon/annotations')
IMAGE_DIR = os.path.join(HOME_DIR, 'DLCV/data/raccoon/images')
print(ANNO_DIR)

files = os.listdir(ANNO_DIR)
print('파일 개수는:',len(files))
print(files)

/content/DLCV/data/raccoon/annotations
파일 개수는: 201

import glob  # 파일 경로 패턴 매칭을 위해 사용 (예: 특정 디렉토리 내의 모든 XML 파일을 찾을 때)
import xml.etree.ElementTree as ET  # XML 파일을 파싱하기 위해 사용
import os  # 파일 경로 처리를 위해 사용

# XML 파일을 CSV 형식의 텍스트 파일로 변환하는 함수
def xml_to_csv(path, output_filename):
    xml_list = []  # 파싱한 XML 데이터를 저장할 리스트
    # 출력 파일을 쓰기 모드로 열기
    with open(output_filename, "w") as train_csv_file:
        # 주어진 경로에서 모든 XML 파일을 순회
        for xml_file in glob.glob(path + '/*.xml'):
            # 각 XML 파일을 파싱하여 Element Tree 객체로 변환하고, 최상위(root) 요소를 추출
            tree = ET.parse(xml_file)
            root = tree.getroot()
            # XML 구조에서 이미지 파일명을 찾아 이미지의 전체 경로를 생성
            full_image_name = os.path.join(IMAGE_DIR, root.find('filename').text)
            value_str_list = ' '  # 각 객체의 바운딩 박스 정보를 저장할 문자열
            
            # XML에서 'object' 요소를 모두 찾아 반복, 이는 이미지에 주석된 객체를 의미
            for obj in root.findall('object'):
                # 'bndbox' 요소를 찾아 바운딩 박스의 좌표 정보(xmin, ymin, xmax, ymax)를 가져옴
                xmlbox = obj.find('bndbox')
                x1 = int(xmlbox.find('xmin').text)  # 바운딩 박스의 좌측 상단 x 좌표
                y1 = int(xmlbox.find('ymin').text)  # 바운딩 박스의 좌측 상단 y 좌표
                x2 = int(xmlbox.find('xmax').text)  # 바운딩 박스의 우측 하단 x 좌표
                y2 = int(xmlbox.find('ymax').text)  # 바운딩 박스의 우측 하단 y 좌표
                # 객체의 클래스 ID, 여기서는 고정적으로 0을 사용 (예: 고정된 객체가 'raccoon'이라고 가정)
                class_id = 0
                # 바운딩 박스와 클래스 ID 정보를 문자열로 포맷팅하여 저장
                value_str = ('{0},{1},{2},{3},{4}').format(x1, y1, x2, y2, class_id)
                value_str_list = value_str_list + value_str + ' '  # 여러 객체 정보를 연결하여 저장
            # 이미지 경로와 객체 정보를 CSV 형식으로 출력 파일에 저장
            train_csv_file.write(full_image_name + ' ' + value_str_list + '\n')
        # XML 파일 순회를 마침

• glob.glob(path + '/*.xml'): 주어진 디렉토리에서 모든 XML 파일을 찾습니다.
• ET.parse(xml_file): XML 파일을 파싱하여 트리 구조를 생성합니다.
• root.findall('object'): XML 파일 내에서 모든 객체 정보를 찾습니다.
• 바운딩 박스 좌표(xmin, ymin, xmax, ymax)를 추출하여 객체의 위치를 파악합니다.
• 클래스 ID를 고정값(0)으로 설정하고, 이를 문자열로 포맷팅하여 CSV 형식으로 변환합니다.

xml_to_csv(ANNO_DIR, os.path.join(ANNO_DIR,'raccoon_anno.csv'))
print(os.path.join(ANNO_DIR,'raccoon_anno.csv'))

# /content/DLCV/data/raccoon/annotations/raccoon_anno.csv

import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping

 

Keras-yolo3 패키지를 다운로드 해서, /content/DLCV/Detection/yolo 밑에 설치

%cd /content/DLCV/Detection/yolo
!git clone https://github.com/qqwweee/keras-yolo3.git
!ls -lia /content/DLCV/Detection/yolo/keras-yolo3

!ls -lia /content/DLCV/Detection/yolo/keras-yolo3

import sys
import os

# 코랩 버전은 아래와 같이 절대 경로를 지정하여 Local Package 지정. 
default_dir = '/content/DLCV'
default_yolo_dir = os.path.join(default_dir, 'Detection/yolo')

LOCAL_PACKAGE_DIR = os.path.abspath(os.path.join(default_yolo_dir,'keras-yolo3'))
print(LOCAL_PACKAGE_DIR)
sys.path.append(LOCAL_PACKAGE_DIR)

from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data

 

Pretrained 모델 재성성 및 font directory 교체

• model_data 밑에 coco dataset로 pretrained 된 yolov3.weights 파일을 yolo.h5 파일로 변경해 줘야 합니다
• keras-yolo3의 font 디렉토리도 재 교체 합니다.

%cd /content/DLCV/Detection/yolo/keras-yolo3
!ls

/content/DLCV/Detection/yolo/keras-yolo3
coco_annotation.py  LICENSE	 train_bottleneck.py  yolov3.cfg
convert.py	    model_data	 train.py	      yolov3-tiny.cfg
darknet53.cfg	    __pycache__  voc_annotation.py    yolov3.weights
font		    README.md	 yolo3		      yolo_video.py
kmeans.py	    snapshots	 yolo.py

# yolov3.weights 파일을 download 받고, convert.py 를 수행하여 model_data 밑에 yolo.h5 파일 생성 수행. 
%cd /content/DLCV/Detection/yolo/keras-yolo3 
# yolo 공식 사이트에서 download시 download 속도가 약 25분 정도 소요됨. github에서 다운로드 요망. 
#!wget  https://pjreddie.com/media/files/yolov3.weights
# github에서 다운로드
!wget https://github.com/chulminkw/DLCV/releases/download/1.0/yolov3.weights

# yolov3.weights를 keras-yolo3에서 사용할 수 있도록 yolo.h5 로 변환
!python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
# model_data 밑에 yolo.h5 파일이 생성되었는지 확인. 
!ls /content/DLCV/Detection/yolo/keras-yolo3/model_data
# yolo.detect_image() 메소드는 PIL package를 이용하여 image 작업 수행. keras-yolo3/font 디렉토리를 상위 디렉토리로 복사 해야함.  
%cd /content/DLCV/Detection/yolo
!cp -rf keras-yolo3/font ./font

Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, None, None, 3 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, None, 3 128         conv2d_1[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_1 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, None, None, 3 0           leaky_re_lu_1[0][0]              
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, None, None, 6 18432       zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, None, 6 256         conv2d_2[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_2 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_2[0][0]      
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, None, None, 3 2048        leaky_re_lu_2[0][0]              
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, None, 3 128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_3 (LeakyReLU)       (None, None, None, 3 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, None, None, 6 18432       leaky_re_lu_3[0][0]              
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, None, 6 256         conv2d_4[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_4 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
add_1 (Add)                     (None, None, None, 6 0           leaky_re_lu_2[0][0]              
                                                                 leaky_re_lu_4[0][0]              
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, None, None, 6 0           add_1[0][0]                      
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, None, None, 1 73728       zero_padding2d_2[0][0]           
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, None, 1 512         conv2d_5[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_5 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, None, None, 6 8192        leaky_re_lu_5[0][0]              
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, None, None, 6 256         conv2d_6[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_6 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_6[0][0]              
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, None, None, 1 512         conv2d_7[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_7 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
add_2 (Add)                     (None, None, None, 1 0           leaky_re_lu_5[0][0]              
                                                                 leaky_re_lu_7[0][0]              
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, None, None, 6 8192        add_2[0][0]                      
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, None, None, 6 256         conv2d_8[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_8 (LeakyReLU)       (None, None, None, 6 0           batch_normalization_8[0][0]      
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, None, None, 1 73728       leaky_re_lu_8[0][0]              
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, None, None, 1 512         conv2d_9[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_9 (LeakyReLU)       (None, None, None, 1 0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
add_3 (Add)                     (None, None, None, 1 0           add_2[0][0]                      
                                                                 leaky_re_lu_9[0][0]              
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, None, None, 1 0           add_3[0][0]                      
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, None, None, 2 294912      zero_padding2d_3[0][0]           
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, None, None, 2 1024        conv2d_10[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_10 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, None, None, 1 32768       leaky_re_lu_10[0][0]             
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, None, None, 1 512         conv2d_11[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_11 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_11[0][0]     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_11[0][0]             
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, None, None, 2 1024        conv2d_12[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_12 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
add_4 (Add)                     (None, None, None, 2 0           leaky_re_lu_10[0][0]             
                                                                 leaky_re_lu_12[0][0]             
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, None, None, 1 32768       add_4[0][0]                      
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, None, None, 1 512         conv2d_13[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_13 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_13[0][0]             
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, None, None, 2 1024        conv2d_14[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_14 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_14[0][0]     
__________________________________________________________________________________________________
add_5 (Add)                     (None, None, None, 2 0           add_4[0][0]                      
                                                                 leaky_re_lu_14[0][0]             
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, None, None, 1 32768       add_5[0][0]                      
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, None, None, 1 512         conv2d_15[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_15 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_15[0][0]             
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, None, None, 2 1024        conv2d_16[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_16 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
add_6 (Add)                     (None, None, None, 2 0           add_5[0][0]                      
                                                                 leaky_re_lu_16[0][0]             
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, None, None, 1 32768       add_6[0][0]                      
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, None, None, 1 512         conv2d_17[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_17 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_17[0][0]             
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, None, None, 2 1024        conv2d_18[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_18 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
add_7 (Add)                     (None, None, None, 2 0           add_6[0][0]                      
                                                                 leaky_re_lu_18[0][0]             
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, None, None, 1 32768       add_7[0][0]                      
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, None, None, 1 512         conv2d_19[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_19 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_19[0][0]             
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, None, None, 2 1024        conv2d_20[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_20 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_20[0][0]     
__________________________________________________________________________________________________
add_8 (Add)                     (None, None, None, 2 0           add_7[0][0]                      
                                                                 leaky_re_lu_20[0][0]             
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, None, None, 1 32768       add_8[0][0]                      
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, None, None, 1 512         conv2d_21[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_21 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_21[0][0]     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_21[0][0]             
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, None, None, 2 1024        conv2d_22[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_22 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_22[0][0]     
__________________________________________________________________________________________________
add_9 (Add)                     (None, None, None, 2 0           add_8[0][0]                      
                                                                 leaky_re_lu_22[0][0]             
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, None, None, 1 32768       add_9[0][0]                      
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, None, None, 1 512         conv2d_23[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_23 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_23[0][0]     
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_23[0][0]             
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, None, None, 2 1024        conv2d_24[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_24 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_24[0][0]     
__________________________________________________________________________________________________
add_10 (Add)                    (None, None, None, 2 0           add_9[0][0]                      
                                                                 leaky_re_lu_24[0][0]             
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, None, None, 1 32768       add_10[0][0]                     
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, None, None, 1 512         conv2d_25[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_25 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_25[0][0]     
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, None, None, 2 294912      leaky_re_lu_25[0][0]             
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, None, None, 2 1024        conv2d_26[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_26 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_26[0][0]     
__________________________________________________________________________________________________
add_11 (Add)                    (None, None, None, 2 0           add_10[0][0]                     
                                                                 leaky_re_lu_26[0][0]             
__________________________________________________________________________________________________
zero_padding2d_4 (ZeroPadding2D (None, None, None, 2 0           add_11[0][0]                     
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, None, None, 5 1179648     zero_padding2d_4[0][0]           
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, None, None, 5 2048        conv2d_27[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_27 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_27[0][0]     
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_27[0][0]             
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, None, None, 2 1024        conv2d_28[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_28 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_28[0][0]     
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_28[0][0]             
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, None, None, 5 2048        conv2d_29[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_29 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_29[0][0]     
__________________________________________________________________________________________________
add_12 (Add)                    (None, None, None, 5 0           leaky_re_lu_27[0][0]             
                                                                 leaky_re_lu_29[0][0]             
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, None, None, 2 131072      add_12[0][0]                     
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, None, None, 2 1024        conv2d_30[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_30 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_30[0][0]     
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_30[0][0]             
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, None, None, 5 2048        conv2d_31[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_31 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_31[0][0]     
__________________________________________________________________________________________________
add_13 (Add)                    (None, None, None, 5 0           add_12[0][0]                     
                                                                 leaky_re_lu_31[0][0]             
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, None, None, 2 131072      add_13[0][0]                     
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, None, None, 2 1024        conv2d_32[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_32 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_32[0][0]     
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_32[0][0]             
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, None, None, 5 2048        conv2d_33[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_33 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_33[0][0]     
__________________________________________________________________________________________________
add_14 (Add)                    (None, None, None, 5 0           add_13[0][0]                     
                                                                 leaky_re_lu_33[0][0]             
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, None, None, 2 131072      add_14[0][0]                     
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, None, None, 2 1024        conv2d_34[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_34 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_34[0][0]     
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_34[0][0]             
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, None, None, 5 2048        conv2d_35[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_35 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_35[0][0]     
__________________________________________________________________________________________________
add_15 (Add)                    (None, None, None, 5 0           add_14[0][0]                     
                                                                 leaky_re_lu_35[0][0]             
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, None, None, 2 131072      add_15[0][0]                     
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, None, None, 2 1024        conv2d_36[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_36 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_36[0][0]     
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_36[0][0]             
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, None, None, 5 2048        conv2d_37[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_37 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_37[0][0]     
__________________________________________________________________________________________________
add_16 (Add)                    (None, None, None, 5 0           add_15[0][0]                     
                                                                 leaky_re_lu_37[0][0]             
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, None, None, 2 131072      add_16[0][0]                     
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, None, None, 2 1024        conv2d_38[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_38 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_38[0][0]     
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_38[0][0]             
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, None, None, 5 2048        conv2d_39[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_39 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_39[0][0]     
__________________________________________________________________________________________________
add_17 (Add)                    (None, None, None, 5 0           add_16[0][0]                     
                                                                 leaky_re_lu_39[0][0]             
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, None, None, 2 131072      add_17[0][0]                     
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, None, None, 2 1024        conv2d_40[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_40 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_40[0][0]     
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_40[0][0]             
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, None, None, 5 2048        conv2d_41[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_41 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_41[0][0]     
__________________________________________________________________________________________________
add_18 (Add)                    (None, None, None, 5 0           add_17[0][0]                     
                                                                 leaky_re_lu_41[0][0]             
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, None, None, 2 131072      add_18[0][0]                     
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, None, None, 2 1024        conv2d_42[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_42 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_42[0][0]     
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_42[0][0]             
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, None, None, 5 2048        conv2d_43[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_43 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_43[0][0]     
__________________________________________________________________________________________________
add_19 (Add)                    (None, None, None, 5 0           add_18[0][0]                     
                                                                 leaky_re_lu_43[0][0]             
__________________________________________________________________________________________________
zero_padding2d_5 (ZeroPadding2D (None, None, None, 5 0           add_19[0][0]                     
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, None, None, 1 4718592     zero_padding2d_5[0][0]           
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, None, None, 1 4096        conv2d_44[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_44 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_44[0][0]     
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_44[0][0]             
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, None, None, 5 2048        conv2d_45[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_45 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_45[0][0]     
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_45[0][0]             
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, None, None, 1 4096        conv2d_46[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_46 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_46[0][0]     
__________________________________________________________________________________________________
add_20 (Add)                    (None, None, None, 1 0           leaky_re_lu_44[0][0]             
                                                                 leaky_re_lu_46[0][0]             
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, None, None, 5 524288      add_20[0][0]                     
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, None, None, 5 2048        conv2d_47[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_47 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_47[0][0]     
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_47[0][0]             
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, None, None, 1 4096        conv2d_48[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_48 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_48[0][0]     
__________________________________________________________________________________________________
add_21 (Add)                    (None, None, None, 1 0           add_20[0][0]                     
                                                                 leaky_re_lu_48[0][0]             
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, None, None, 5 524288      add_21[0][0]                     
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, None, None, 5 2048        conv2d_49[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_49 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_49[0][0]     
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_49[0][0]             
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, None, None, 1 4096        conv2d_50[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_50 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_50[0][0]     
__________________________________________________________________________________________________
add_22 (Add)                    (None, None, None, 1 0           add_21[0][0]                     
                                                                 leaky_re_lu_50[0][0]             
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, None, None, 5 524288      add_22[0][0]                     
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, None, None, 5 2048        conv2d_51[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_51 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_51[0][0]     
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_51[0][0]             
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, None, None, 1 4096        conv2d_52[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_52 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_52[0][0]     
__________________________________________________________________________________________________
add_23 (Add)                    (None, None, None, 1 0           add_22[0][0]                     
                                                                 leaky_re_lu_52[0][0]             
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, None, None, 5 524288      add_23[0][0]                     
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, None, None, 5 2048        conv2d_53[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_53 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_53[0][0]     
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_53[0][0]             
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, None, None, 1 4096        conv2d_54[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_54 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_54[0][0]     
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_54[0][0]             
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, None, None, 5 2048        conv2d_55[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_55 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_55[0][0]     
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, None, None, 1 4718592     leaky_re_lu_55[0][0]             
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, None, None, 1 4096        conv2d_56[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_56 (LeakyReLU)      (None, None, None, 1 0           batch_normalization_56[0][0]     
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, None, None, 5 524288      leaky_re_lu_56[0][0]             
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, None, None, 5 2048        conv2d_57[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_57 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_57[0][0]     
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_57[0][0]             
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, None, None, 2 1024        conv2d_60[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_59 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_59[0][0]     
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D)  (None, None, None, 2 0           leaky_re_lu_59[0][0]             
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, None, None, 7 0           up_sampling2d_1[0][0]            
                                                                 add_19[0][0]                     
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, None, None, 2 196608      concatenate_1[0][0]              
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, None, None, 2 1024        conv2d_61[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_60 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_60[0][0]     
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_60[0][0]             
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, None, None, 5 2048        conv2d_62[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_61 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_61[0][0]     
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, None, None, 2 131072      leaky_re_lu_61[0][0]             
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, None, None, 2 1024        conv2d_63[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_62 (LeakyReLU)      (None, None, None, 2 0           batch_normalization_62[0][0]     
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, None, None, 5 1179648     leaky_re_lu_62[0][0]             
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, None, None, 5 2048        conv2d_64[0][0]                  
__________________________________________________________________________________________________
leaky_re_lu_63 (LeakyReLU)      (None, None, None, 5 0           batch_normalization_63[0][0]     
___

# colab 버전은 아래 명령어로 raccoon_class.txt 를 수정합니다. 
BASE_DIR = os.path.join(HOME_DIR, 'DLCV/Detection/yolo/keras-yolo3')
classes_path = os.path.join(BASE_DIR, 'model_data/raccoon_class.txt')
with open(classes_path, "w") as f:
    f.write("raccoon")

# colab 버전은 raccoon_class.txt에 제대로 기재되었나 확인. 
!cat /content/DLCV/Detection/yolo/keras-yolo3/model_data/raccoon_class.txt

from train import get_classes, get_anchors  # 클래스와 앵커 정보를 불러오는 함수
from train import create_model, data_generator, data_generator_wrapper  # 모델 생성 및 데이터 제너레이터 관련 함수

BASE_DIR = os.path.join(HOME_DIR, 'DLCV/Detection/yolo/keras-yolo3')

## 학습을 위한 기본 환경 설정. annotation 파일, 저장된 모델 파일 경로, 클래스 파일, 앵커 파일 경로를 설정.
annotation_path = os.path.join(ANNO_DIR, 'raccoon_anno.csv')  # 주석 파일 경로
log_dir = os.path.join(BASE_DIR, 'snapshots/000/')  # 모델 학습 중간 결과를 저장할 폴더
classes_path = os.path.join(BASE_DIR, 'model_data/raccoon_class.txt')  # 클래스 정보 파일 경로
anchors_path = os.path.join(BASE_DIR, 'model_data/yolo_anchors.txt')  # 앵커 박스 정보 파일 경로

# 클래스 이름과 앵커 박스 정보를 불러옴
class_names = get_classes(classes_path)  # 클래스 이름을 가져옴
num_classes = len(class_names)  # 클래스 개수를 계산
anchors = get_anchors(anchors_path)  # 앵커 박스를 가져옴

# 최초 가중치 파일은 COCO 데이터셋으로 학습된 YOLO 모델을 사용.
model_weights_path = os.path.join(BASE_DIR, 'model_data/yolo.h5')

input_shape = (416, 416)  # 입력 이미지 크기 (416x416), YOLO 모델에서 32의 배수여야 함

# 앵커 박스 개수가 6개면 Tiny YOLO 모델 사용
is_tiny_version = len(anchors) == 6

# Tiny YOLO 모델이냐 일반 YOLO 모델이냐에 따라 모델 생성 방식을 선택
if is_tiny_version:
    model = create_tiny_model(input_shape, anchors, num_classes,
                              freeze_body=2, weights_path=model_weights_path)
else:
    model = create_model(input_shape, anchors, num_classes,
                         freeze_body=2, weights_path=model_weights_path)  # 레이어 일부 고정

# 모델 학습 시 사용할 콜백 설정 (TensorBoard 로그, 체크포인트 저장, 학습률 조정 등)
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
                             monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)

val_split = 0.1  # 학습 데이터 중 10%를 검증 데이터로 사용

# 주석 파일을 열어 이미지와 객체 정보를 읽어옴
with open(annotation_path) as f:
    lines = f.readlines()

# 데이터를 셔플하여 훈련 데이터와 검증 데이터로 나눔
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines) * val_split)  # 검증 데이터 개수
num_train = len(lines) - num_val  # 학습 데이터 개수

# 첫 번째 학습 단계: 고정된 레이어로 학습을 시작하여 기본적인 손실을 안정화
if True:
    model.compile(optimizer=Adam(lr=1e-3), loss={
        # yolo_loss는 사용자 정의 손실 함수 (Lambda 레이어 사용)
        'yolo_loss': lambda y_true, y_pred: y_pred})

    batch_size = 4  # 배치 크기 설정
    print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
    # 데이터 제너레이터를 사용하여 모델을 학습
    model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                        steps_per_epoch=max(1, num_train // batch_size),
                        validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                        validation_steps=max(1, num_val // batch_size),
                        epochs=50,  # 50 에포크 동안 학습
                        initial_epoch=0,
                        callbacks=[logging, checkpoint])
    model.save_weights(log_dir + 'trained_weights_stage_1.h5')  # 첫 번째 학습 단계의 가중치를 저장

# 두 번째 학습 단계: 고정된 레이어를 풀고(fine-tune) 추가 학습 진행
if True:
    for i in range(len(model.layers)):
        model.layers[i].trainable = True  # 모든 레이어를 학습 가능 상태로 설정
    model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred})  # 변경 사항을 적용하기 위해 모델 재컴파일
    print('Unfreeze all of the layers.')

    batch_size = 4  # 고정된 레이어를 푼 후에는 더 많은 GPU 메모리가 필요할 수 있음
    print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
    # 동일한 방식으로 학습, 그러나 모든 레이어가 학습되도록 설정
    model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
                        steps_per_epoch=max(1, num_train // batch_size),
                        validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
                        validation_steps=max(1, num_val // batch_size),
                        epochs=100,  # 100 에포크 동안 학습
                        initial_epoch=50,
                        callbacks=[logging, checkpoint, reduce_lr, early_stopping])
    model.save_weights(log_dir + 'trained_weights_final.h5')  # 최종 가중치 저장

• 모델 로딩: COCO 데이터셋으로 미리 학습된 YOLO 모델을 로딩하고, 이를 기반으로 학습을 시작합니다.
• 모델 학습 과정:
  • 고정된 레이어로 먼저 학습을 진행하여 기본적인 손실을 안정화합니다.
  • 그 후, 고정된 레이어를 풀어(fine-tuning), 모델을 더욱 세부적으로 학습합니다.
• 콜백 함수: 학습 중간중간에 손실 값에 따라 학습률을 조정하거나 학습을 조기 종료하는 등 다양한 콜백 함수들이 사용됩니다.
• 데이터 분할: 주석 데이터를 셔플한 후, 90%는 학습에, 10%는 검증에 사용됩니다.

# snapshots/000 디렉토리에 학습된 weight 파일이 생성되었는지 확인. 
!ls /content/DLCV/Detection/yolo/keras-yolo3/snapshots/000

---

학습된 model을 이용하여 Inference 수행

# YOLO 객체 생성. 
import sys
import argparse
from yolo import YOLO, detect_video
#keras-yolo에서 image처리를 주요 PIL로 수행. 
from PIL import Image

#LOCAL_PACKAGE_DIR = os.path.abspath("./keras-yolo3")
#sys.path.append(LOCAL_PACKAGE_DIR)

# 코랩 버전은 절대 경로를 설정. 
HOME_DIR='/content'
raccoon_yolo = YOLO(model_path=os.path.join(HOME_DIR,'DLCV/Detection/yolo/keras-yolo3/snapshots/000/trained_weights_final.h5'),
            anchors_path=os.path.join(HOME_DIR,'DLCV/Detection/yolo/keras-yolo3/model_data/yolo_anchors.txt'),
            classes_path=os.path.join(HOME_DIR, 'DLCV/Detection/yolo/keras-yolo3/model_data/raccoon_class.txt'))
            
# /content/DLCV/Detection/yolo/keras-yolo3/snapshots/000/trained_weights_final.h5 model, anchors, and classes loaded.

import matplotlib
import matplotlib.pyplot as plt

img = Image.open(os.path.join(IMAGE_DIR, 'raccoon-171.jpg'))

plt.figure(figsize=(12, 12))
plt.imshow(img)

 

<matplotlib.image.AxesImage at 0x7fdcd7226588>

detected_img = raccoon_yolo.detect_image(img)

plt.figure(figsize=(12, 12))
plt.imshow(detected_img)

(416, 416, 3)
Found 1 boxes for img
raccoon 0.99 (105, 29) (185, 106)
2.3317793840005834
<matplotlib.image.AxesImage at 0x7fdcda716b70>