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aswintechguy
GitHub Repository: aswintechguy/Deep-Learning-Projects
 Path: blob/main/Custom Object Detection using YOLOv8/Custom Object Detection - YOLOv8.ipynb
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Kernel: Python 3 (ipykernel)

Install Modules

# install torch
# !pip install ultralytics

# use labelimg repo/tool to annotate the images

Import Modules

import os
import time
import random
import pandas as pd
import numpy as np
import cv2
import torch
from tqdm.auto import tqdm
from PIL import Image
import shutil
import matplotlib.pyplot as plt
%matplotlib inline

Load the Dataset

df = pd.read_csv('data/train_solution_bounding_boxes (1).csv')
df.head()

# get image_id
df['image_id'] = df['image'].apply(lambda x: x.split('.')[0])
df['classes'] = 0
df.head(2)

# initialize configuration
img_h, img_w, num_channels = (380, 676, 3)
images_folder = 'data/training_images/'

# convert the data points to YOLO format
df['x_center'] = (df['xmin'] + df['xmax']) / 2
df['y_center'] = (df['ymin'] + df['ymax']) / 2
df['w'] = df['xmax'] - df['xmin']
df['h'] = df['ymax'] - df['ymin']

# normalize the values
df['x_center'] = df['x_center'] / img_w
df['y_center'] = df['y_center'] / img_h
df['w'] = df['w'] / img_w
df['h'] = df['h'] / img_h

df.head(2)

Exploratory Data Analysis

image = random.choice(df['image'])
image_path = os.path.join(images_folder, image)
img = Image.open(image_path)
plt.axis('off')
plt.imshow(img)
plt.show()
Image in a Jupyter notebook
image = random.choice(df['image'])
image_path = os.path.join(images_folder, image)
img = Image.open(image_path)
plt.axis('off')
plt.imshow(img)
plt.show()
Image in a Jupyter notebook
def draw_bounding_box(idx):
    image = cv2.imread(os.path.join(images_folder, df['image'][idx]))
    x_min = int(df['xmin'][idx])
    y_min = int(df['ymin'][idx])
    x_max = int(df['xmax'][idx])
    y_max = int(df['ymax'][idx])

    # draw the rectangle
    cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
    # place the label above bounding boxes
    cv2.putText(image, 'car', (x_min, y_min-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    # display the image
    plt.axis('off')
    plt.imshow(image)
    plt.show()

idx = random.randrange(0, len(df))
draw_bounding_box(idx)
Image in a Jupyter notebook
idx = random.randrange(0, len(df))
draw_bounding_box(idx)
Image in a Jupyter notebook

Create Annotations for YOLO Format

annotations_folder = 'data/annotations/'

if not os.path.exists(annotations_folder):
    os.mkdir(annotations_folder)

annotations_dict = {}

# iterate through dataframe to consolidate bounding boxes for each image
for _, row in df.iterrows():
    image_file = row['image']
    class_label = int(row['classes'])
    x_center = row['x_center']
    y_center = row['y_center']
    w = row['w']
    h = row['h']

    # initialize list if image is not present in dicitionary
    if image_file not in annotations_dict:
        annotations_dict[image_file] = []

    # append annotations to the list 
    annotations_dict[image_file].append(f"{class_label} {x_center} {y_center} {w} {h}")

# write the annotations in text file
for image_file, annotations in annotations_dict.items():
    annotation_file = os.path.join(annotations_folder, os.path.splitext(image_file)[0] + '.txt')
    with open(annotation_file, 'w') as f:
        for annotation in annotations:
            f.write(annotation + '\n')

# split images for training and testing
from sklearn.model_selection import train_test_split
images = list(annotations_dict.keys())
train_images, test_images = train_test_split(images, test_size=0.2, random_state=42)

# create folders for train and test
train_images_folder = 'datasets/train_images'
test_images_folder = 'datasets/test_images'
train_annotations_folder = 'datasets/train_annotations'
test_annotations_folder = 'datasets/test_annotations'

# create directors for the above paths
os.mkdir('datasets')
os.mkdir(train_images_folder)
os.mkdir(test_images_folder)
os.mkdir(train_annotations_folder)
os.mkdir(test_annotations_folder)

# copy the files to the respective folder paths
def copy_files(images_list, image_src_folder, annotation_src_folder, image_dest_folder, annotation_dest_folder):
    for image_file in images_list:
        # path for source and destination
        src_image_path = os.path.join(image_src_folder, image_file)
        annotation_file = os.path.splitext(image_file)[0] + '.txt'
        src_annotations_path = os.path.join(annotation_src_folder, annotation_file)
        dest_image_path = os.path.join(image_dest_folder, image_file)
        dest_annotation_path = os.path.join(annotation_dest_folder, annotation_file)

        # copy images
        shutil.copy2(src_image_path,  dest_image_path)
        # copy annotations
        shutil.copy2(src_annotations_path, dest_annotation_path)


copy_files(train_images, images_folder, annotations_folder, train_images_folder, train_annotations_folder)
copy_files(test_images, images_folder, annotations_folder, test_images_folder, test_annotations_folder)

Model Training

from ultralytics import YOLO
# load pretrained model
model = YOLO('yolov8n.yaml')

# define training parameters
model.train(data='data.yaml', epochs=50, batch=16, imgsz=676, workers=2)
New https://pypi.org/project/ultralytics/8.2.68 available Update with 'pip install -U ultralytics' Ultralytics YOLOv8.2.66 Python-3.12.3 torch-2.4.0 CUDA:0 (NVIDIA GeForce GTX 1050 Ti, 4096MiB) engine\trainer: task=detect, mode=train, model=yolov8n.yaml, data=data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=676, save=True, save_period=-1, cache=False, device=None, workers=2, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]] YOLOv8n summary: 225 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n... AMP: checks passed WARNING imgsz=[676] must be multiple of max stride 32, updating to [704]
C:\Users\Aswin\anaconda3\Lib\site-packages\ultralytics\engine\trainer.py:268: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead. self.scaler = torch.cuda.amp.GradScaler(enabled=self.amp) train: Scanning D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\train_im
train: New cache created: D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\train_images.cache
val: Scanning D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\test_image
val: New cache created: D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\test_images.cache
Plotting labels to runs\detect\train\labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 704 train, 704 val Using 2 dataloader workers Logging results to runs\detect\train Starting training for 50 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 2.58G 4.802 5.365 4.228 25 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 2.58G 3.708 4.318 3.925 44 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 2.58G 2.99 3.268 3.365 30 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 2.58G 2.648 2.612 2.806 33 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 2.58G 2.421 2.361 2.515 31 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 2.58G 2.27 2.136 2.329 43 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 2.58G 2.201 2.009 2.296 39 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.000552 0.0504 0.000351 0.000139
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 2.58G 2.104 1.779 2.18 25 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.637 0.0588 0.137 0.0481
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 2.58G 2.066 1.64 2.125 29 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.611 0.41 0.45 0.221
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 2.58G 2.054 1.615 2.116 52 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.817 0.655 0.767 0.402
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 2.58G 1.934 1.443 2.019 30 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.738 0.779 0.765 0.368
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 2.58G 1.862 1.345 1.945 27 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.862 0.84 0.902 0.462
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 2.58G 1.855 1.358 1.97 21 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.86 0.778 0.876 0.476
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 2.58G 1.748 1.237 1.845 35 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.953 0.856 0.924 0.493
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 2.58G 1.736 1.205 1.811 43 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.876 0.891 0.915 0.505
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 2.58G 1.662 1.109 1.78 28 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.924 0.84 0.921 0.542
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 2.58G 1.656 1.12 1.771 39 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.96 0.807 0.916 0.515
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 2.58G 1.643 1.066 1.763 37 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.953 0.84 0.94 0.524
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 2.58G 1.609 1.025 1.709 21 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.936 0.908 0.944 0.548
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 2.58G 1.603 1.017 1.722 22 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.953 0.846 0.91 0.48
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 2.58G 1.576 1.009 1.701 27 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.981 0.848 0.941 0.54
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 2.58G 1.565 0.9774 1.698 34 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.963 0.866 0.956 0.559
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 2.58G 1.619 0.9488 1.726 41 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.921 0.885 0.95 0.56
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 2.58G 1.543 0.9321 1.639 38 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.959 0.899 0.958 0.517
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 2.58G 1.554 0.9106 1.636 33 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.92 0.916 0.955 0.555
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 2.58G 1.504 0.8987 1.595 29 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.987 0.891 0.953 0.586
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 2.58G 1.522 0.9251 1.643 30 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.974 0.891 0.946 0.579
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 2.58G 1.49 0.8887 1.596 42 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.931 0.907 0.945 0.573
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 2.58G 1.527 0.916 1.613 51 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.931 0.916 0.952 0.537
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 2.58G 1.481 0.8685 1.614 32 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.925 0.932 0.949 0.586
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 2.58G 1.469 0.8318 1.571 34 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.977 0.924 0.952 0.577
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 2.58G 1.422 0.8208 1.579 28 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.958 0.899 0.95 0.579
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 2.58G 1.408 0.8027 1.523 28 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.947 0.907 0.954 0.596
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 2.58G 1.439 0.8083 1.606 34 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.927 0.899 0.95 0.58
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 2.58G 1.398 0.7983 1.51 41 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.982 0.903 0.96 0.595
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 2.58G 1.401 0.7645 1.526 46 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.98 0.933 0.956 0.615
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 2.58G 1.382 0.7625 1.507 30 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.972 0.924 0.956 0.599
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 2.58G 1.358 0.7435 1.494 36 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.97 0.916 0.954 0.61
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 2.58G 1.376 0.7575 1.494 49 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.985 0.916 0.964 0.597
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 2.58G 1.362 0.7527 1.485 25 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.985 0.916 0.968 0.63
Closing dataloader mosaic Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 2.58G 1.317 0.8025 1.537 15 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.982 0.907 0.965 0.618
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 2.58G 1.296 0.7658 1.492 15 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.972 0.908 0.959 0.597
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 2.58G 1.294 0.7724 1.522 18 704: 100%|██████████| 18/18 [00:11<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.956 0.916 0.955 0.602
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 2.58G 1.254 0.7474 1.501 25 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.982 0.93 0.962 0.61
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 2.58G 1.233 0.7139 1.479 15 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.983 0.924 0.963 0.615
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 2.58G 1.268 0.7314 1.49 11 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.99 0.924 0.963 0.601
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 2.58G 1.238 0.717 1.464 19 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.989 0.924 0.965 0.62
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 2.58G 1.21 0.698 1.447 19 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.989 0.916 0.963 0.611
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 2.58G 1.24 0.7071 1.498 22 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.991 0.922 0.966 0.608
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 2.58G 1.209 0.7075 1.449 12 704: 100%|██████████| 18/18 [00:12<00:00, 1. Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.988 0.924 0.965 0.623
50 epochs completed in 0.180 hours. Optimizer stripped from runs\detect\train\weights\last.pt, 6.2MB Optimizer stripped from runs\detect\train\weights\best.pt, 6.2MB Validating runs\detect\train\weights\best.pt... Ultralytics YOLOv8.2.66 Python-3.12.3 torch-2.4.0 CUDA:0 (NVIDIA GeForce GTX 1050 Ti, 4096MiB) YOLOv8n summary (fused): 168 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<0
all 71 119 0.985 0.916 0.968 0.63 Speed: 0.3ms preprocess, 6.4ms inference, 0.0ms loss, 0.7ms postprocess per image Results saved to runs\detect\train
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metrics = model.val()
Ultralytics YOLOv8.2.66 Python-3.12.3 torch-2.4.0 CUDA:0 (NVIDIA GeForce GTX 1050 Ti, 4096MiB) YOLOv8n summary (fused): 168 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
val: Scanning D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\test_image Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 5/5 [00:03<0
all 71 119 0.985 0.916 0.968 0.633 Speed: 0.3ms preprocess, 7.0ms inference, 0.0ms loss, 0.9ms postprocess per image Results saved to runs\detect\train2 
# # load the weights
# best_model = YOLO('yolov8n.yaml')
# best_model.load('runs/detect/train/weights/best.pt')

Test with Real Image

# process the results
image_path = 'datasets/test_images/vid_4_10020.jpg'
results = model(image_path)
image = cv2.imread(image_path)
for result in results:
    # loop through the detected objects
    for detection in result.boxes:
        x_min, y_min, x_max, y_max = detection.xyxy[0]
        confidence = round(float(detection.conf[0]), 2)
        class_id = int(detection.cls[0])

        # draw bouding box
        cv2.rectangle(image, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 2)

        # write label
        label = f"Car {confidence}"
        cv2.putText(image, label, (int(x_min), int(y_min)-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

# convert the image to rgb
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

plt.imshow(image_rgb)
plt.axis('off')
plt.show()
image 1/1 D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\test_images\vid_4_10020.jpg: 416x704 1 car, 103.3ms Speed: 2.5ms preprocess, 103.3ms inference, 1.0ms postprocess per image at shape (1, 3, 416, 704)
Image in a Jupyter notebook
# process the results
image_path = 'datasets/test_images/vid_4_26360.jpg'
results = model(image_path)
image = cv2.imread(image_path)
for result in results:
    # loop through the detected objects
    for detection in result.boxes:
        x_min, y_min, x_max, y_max = detection.xyxy[0]
        confidence = round(float(detection.conf[0]), 2)
        class_id = int(detection.cls[0])

        # draw bouding box
        cv2.rectangle(image, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 2)

        # write label
        label = f"Car {confidence}"
        cv2.putText(image, label, (int(x_min), int(y_min)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# convert the image to rgb
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

plt.imshow(image_rgb)
plt.axis('off')
plt.show()
image 1/1 D:\notebooks\Data Science Projects\Deep Learning\Custom Object Detection using YOLOv8\datasets\test_images\vid_4_26360.jpg: 416x704 4 cars, 112.7ms Speed: 4.5ms preprocess, 112.7ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 704)
Image in a Jupyter notebook