Computer Vision with YOLOv11 - Vehicle DetectionΒΆ
Author: Guillaume EGU
Project OverviewΒΆ
This project implements a vehicle detection system using YOLOv11 (You Only Look Once version 11), one of the most powerful object detection models. The objective is to automatically detect and classify different types of vehicles in real-time images.
The dataset contains annotated images of 5 vehicle classes: Ambulance, Bus, Car, Motorcycle, and Truck. This project demonstrates the effectiveness of modern deep learning architectures for object detection in computer vision.
Data Source: Vehicle detection dataset with annotations: https://universe.roboflow.com/vaishak-shetty-sri7e/vehicle-detection-byizq
Project ObjectivesΒΆ
- Data Exploration: Analyze the distribution and characteristics of vehicle images
- Data Preparation: Configure the dataset in YOLO format with appropriate paths and classes
- Model Training: Fine-tune YOLOv11 on our specific vehicle dataset
- Performance Evaluation: Analyze detection metrics and model accuracy
- Testing and Validation: Test the trained model on new images
Practical ValueΒΆ
- Traffic Monitoring: Automatically count and classify vehicles
- Road Safety: Detect emergency vehicles (ambulances)
- Urban Analysis: Understand traffic flows
- Real-Time Applications: Fast video image processing
ContentΒΆ
- Library Imports
- Data Configuration
- Data Exploration and Visualization
- YOLOv11 Model Training
- Performance Evaluation
- Testing on New Images
- Results Analysis
InΒ [2]:
!pip install pandas
!pip install matplotlib
!pip install seaborn
!pip install scikit-learn
!pip install numpy
!pip install imblearn
!pip install xgboost
!pip install ultralytics
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InΒ [3]:
# Handle the data
import os
import random
import pandas as pd
import numpy as np
import cv2
import torch
from IPython.display import Image as IPythonImage, display
from PIL import Image as PILImage
# Visualization
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
from matplotlib.colors import ListedColormap
# Models
from PIL import Image
import yaml
from ultralytics import YOLO
from sklearn.metrics import precision_score, recall_score
FunctionsΒΆ
These helper functions implement the core NLP preprocessing pipeline. Each function serves a specific purpose in transforming raw text into a format suitable for machine learning algorithms.
InΒ [Β ]:
Creating the YAML configuration file required by YOLOv11 to define data paths and vehicle classes
InΒ [4]:
path_train = "./datasets/VehiclesDetectionDataset/train/images/"
path_val = "./datasets/VehiclesDetectionDataset/val/images/"
path_test = "./datasets/VehiclesDetectionDataset/test/images/"
yaml_content = f"""
test: {path_test}
train: {path_train}
val: {path_val}
nc: 5
names: ['Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck']
"""
output_path= 'data.yaml'
with open(output_path, "w") as file:
file.write(yaml_content)
print(f"YAML file has been saved to {output_path}")
YAML file has been saved to data.yaml
Reading and displaying vehicle classes defined in the configuration file
InΒ [5]:
with open(output_path, 'r') as file:
data = yaml.safe_load(file)
class_names = data['names']
print("Class names:", class_names )
Class names: ['Ambulance', 'Bus', 'Car', 'Motorcycle', 'Truck']
Exploratory Data Analysis (EDA) and Dataset UnderstandingΒΆ
In this crucial step, we explore the vehicle detection dataset to understand:
- Dataset Structure: How many images are in train/validation/test splits?
- Vehicle Classes Distribution: Are all 5 vehicle types (Ambulance, Bus, Car, Motorcycle, Truck) equally represented?
- Image Characteristics: What are the typical dimensions, quality, and visual properties?
- Annotation Quality: How well are the bounding boxes and labels defined?
- Data Diversity: Do we have sufficient variety in lighting, angles, and backgrounds?
Exploring technical characteristics of a random image (dimensions, color mode, number of channels)
InΒ [6]:
images_folder = './datasets/VehiclesDetectionDataset/train/images'
image_files = [f for f in os.listdir(images_folder) if f.endswith(('.jpg', '.png', '.jpeg'))]
random_image = random.choice(image_files)
random_image_path = os.path.join(images_folder, random_image)
image = PILImage.open(random_image_path)
image_size = image.size
image_mode = image.mode
num_channels = image.layers if hasattr(image, 'layers') else len(image.getbands())
print(f"Random Image: {random_image}")
print(f"Image Size: {image_size}")
print(f"Image Mode (Channels): {image_mode}")
print(f"Number of Channels: {num_channels}")
Random Image: 3ded02412ac12d4b_jpg.rf.pGfJCgNuD514KHsWDFNc.jpg Image Size: (416, 416) Image Mode (Channels): RGB Number of Channels: 3
Displaying a 4x4 grid of random images from the training dataset to explore data diversity
InΒ [7]:
image_files = [f for f in os.listdir(path_train) if f.endswith('.jpg')]
selected_images = random.sample(image_files, 16)
fig, axes = plt.subplots(4, 4, figsize=(10, 10))
for i, ax in enumerate(axes.flat):
img_path = os.path.join(path_train, selected_images[i])
img = mpimg.imread(img_path)
ax.imshow(img)
ax.axis('off')
plt.tight_layout()
plt.show()
Evaluating detection capabilities of the YOLOv11 nano model on our dataset images before fine-tuning
InΒ [8]:
images_folder = './datasets/VehiclesDetectionDataset/train/images'
images_files = [f for f in os.listdir(images_folder) if f.endswith(('.jpg', '.png', '.jpeg'))]
random_images = random.sample(images_files, 20)
model = YOLO('./models/yolo11n.pt')
for image_file in random_images:
image_path = os.path.join(images_folder, image_file)
result_predict = model.predict(source=image_path, imgsz=(416))
original_image = PILImage.open(image_path)
display(original_image)
for result in result_predict:
plot = result.plot()
plot = cv2.cvtColor(plot, cv2.COLOR_BGR2RGB)
display(PILImage.fromarray(plot))
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\a6bae981a16ab9a6_jpg.rf.51773282f8cc4691c58f8f1929949c3b.jpg: 416x416 1 car, 153.3ms Speed: 3.5ms preprocess, 153.3ms inference, 12.3ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\c52500a14f92ff65_jpg.rf.bac8f96b8928ebabc80a2e39fe5bb650.jpg: 416x416 4 persons, 1 truck, 65.8ms Speed: 2.3ms preprocess, 65.8ms inference, 5.2ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\27fed64f6e8e0fb1_jpg.rf.365f036b3c5c98218829c2b839a9bbe9.jpg: 416x416 1 person, 4 cars, 71.9ms Speed: 1.3ms preprocess, 71.9ms inference, 2.3ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\3306489836719c96_jpg.rf.a9ffe2471f824bc00b2ed031a04845cc.jpg: 416x416 4 cars, 1 bus, 59.0ms Speed: 1.2ms preprocess, 59.0ms inference, 2.1ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\f8c637fc8220a918_jpg.rf.7InkNTlGkJXqHqcTw9ev.jpg: 416x416 6 persons, 1 bus, 1 handbag, 71.3ms Speed: 0.9ms preprocess, 71.3ms inference, 2.8ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\005980b3a5f93085_jpg.rf.2QZCXGReqcBORWQJKQfO.jpg: 416x416 1 person, 1 bus, 1 truck, 72.8ms Speed: 2.7ms preprocess, 72.8ms inference, 1.6ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\fedcf2c1a78c1446_jpg.rf.97d463394d7e15988400d1ac9d9ceacb.jpg: 416x416 3 cars, 58.6ms Speed: 0.9ms preprocess, 58.6ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\ccda9fe055e7fb14_jpg.rf.qpuvhah7Z8cihR1hXZXD.jpg: 416x416 1 bus, 56.6ms Speed: 1.3ms preprocess, 56.6ms inference, 1.2ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\fedcf2c1a78c1446_jpg.rf.jKHFjts8DglEwHYNbghh.jpg: 416x416 3 cars, 75.1ms Speed: 1.3ms preprocess, 75.1ms inference, 2.9ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\32af44c2a2cfb296_jpg.rf.3813b66f92c72dd943b24814e9894231.jpg: 416x416 4 persons, 3 cars, 2 trucks, 73.5ms Speed: 1.2ms preprocess, 73.5ms inference, 3.5ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\8f28dbee29152679_jpg.rf.5t0m9nzoBwio0AecyWRG.jpg: 416x416 3 cars, 106.9ms Speed: 0.9ms preprocess, 106.9ms inference, 2.4ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\6b08b17e4bd591d8_jpg.rf.13bc2985423afe433b8d9088662b4e71.jpg: 416x416 1 car, 60.3ms Speed: 1.1ms preprocess, 60.3ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\da386cf96761ef58_jpg.rf.R27NNWvN3ZYHN4n3DiH4.jpg: 416x416 3 persons, 1 car, 2 trucks, 86.5ms Speed: 1.2ms preprocess, 86.5ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\1781ceeba365928b_jpg.rf.b658668ccefc5b053a17a307e0cd6c11.jpg: 416x416 3 persons, 3 cars, 2 buss, 105.1ms Speed: 1.6ms preprocess, 105.1ms inference, 5.1ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\ebdb1c5cf58b8a86_jpg.rf.EfI0z3s3CbeZhsXHZGPU.jpg: 416x416 4 persons, 1 bus, 86.9ms Speed: 0.9ms preprocess, 86.9ms inference, 2.2ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\f1200c263667e455_jpg.rf.fnY9QjRYJ0Ed7j4H24ZC.jpg: 416x416 2 persons, 1 truck, 83.6ms Speed: 2.0ms preprocess, 83.6ms inference, 2.3ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\3e485b3cb8cb0739_jpg.rf.550ad500da07f3ef54f7d05d9e36f549.jpg: 416x416 9 persons, 4 cars, 3 trucks, 59.7ms Speed: 1.3ms preprocess, 59.7ms inference, 3.7ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\fa4373131d980682_jpg.rf.fb4066e24ca14b29bcee16051cec2834.jpg: 416x416 4 persons, 1 bus, 1 truck, 102.9ms Speed: 1.0ms preprocess, 102.9ms inference, 3.1ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\d31c720e294fee1a_jpg.rf.7dae13b793e31e8c1ab96b8b1915f32f.jpg: 416x416 1 car, 1 truck, 112.1ms Speed: 1.0ms preprocess, 112.1ms inference, 1.7ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\552a0d9a0fc92dac_jpg.rf.c1d215d149e6deb7126925352ac37518.jpg: 416x416 1 car, 2 trucks, 62.3ms Speed: 0.9ms preprocess, 62.3ms inference, 1.6ms postprocess per image at shape (1, 3, 416, 416)
InΒ [35]:
random_images = random.sample(image_files, 20)
model = YOLO("./models/yolo11n.pt")
for image_file in random_images:
image_path = os.path.join(images_folder, image_file)
result_predict = model.predict(source=image_path, imgsz=(416))
original_image = Image.open(image_path)
display(original_image)
for result in result_predict:
plot = result.plot()
plot = cv2.cvtColor(plot, cv2.COLOR_BGR2RGB)
display(Image.fromarray(plot))
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\040d25a99e7b675b_jpg.rf.UIHiGJcPfPY36egSAJft.jpg: 416x416 1 truck, 150.4ms Speed: 2.2ms preprocess, 150.4ms inference, 1.6ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\040d25a99e7b675b_jpg.rf.UIHiGJcPfPY36egSAJft.jpg: 416x416 1 truck, 150.4ms Speed: 2.2ms preprocess, 150.4ms inference, 1.6ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\6369f9d6c2bfab75_jpg.rf.pFHXUaD0AFmCJepgyor1.jpg: 416x416 1 person, 1 car, 2 boats, 141.4ms Speed: 1.7ms preprocess, 141.4ms inference, 2.2ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\6369f9d6c2bfab75_jpg.rf.pFHXUaD0AFmCJepgyor1.jpg: 416x416 1 person, 1 car, 2 boats, 141.4ms Speed: 1.7ms preprocess, 141.4ms inference, 2.2ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\d8b725a5e406bf77_jpg.rf.4a1b8136fd8ff4f981e50ed6ecc18645.jpg: 416x416 1 bus, 229.4ms Speed: 1.5ms preprocess, 229.4ms inference, 4.2ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\d8b725a5e406bf77_jpg.rf.4a1b8136fd8ff4f981e50ed6ecc18645.jpg: 416x416 1 bus, 229.4ms Speed: 1.5ms preprocess, 229.4ms inference, 4.2ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\447c9ff274fab842_jpg.rf.Yzz1UWLTvLeMwXBZTtat.jpg: 416x416 2 buss, 103.8ms Speed: 2.5ms preprocess, 103.8ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\447c9ff274fab842_jpg.rf.Yzz1UWLTvLeMwXBZTtat.jpg: 416x416 2 buss, 103.8ms Speed: 2.5ms preprocess, 103.8ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\63ee0ce9f69bf8e5_jpg.rf.PnRjGBSWtdTYaYCq1Q6z.jpg: 416x416 1 truck, 202.1ms Speed: 1.6ms preprocess, 202.1ms inference, 1.7ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\63ee0ce9f69bf8e5_jpg.rf.PnRjGBSWtdTYaYCq1Q6z.jpg: 416x416 1 truck, 202.1ms Speed: 1.6ms preprocess, 202.1ms inference, 1.7ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\91e11943f15e4b3c_jpg.rf.9luwWbGyIEfG0TqiyFj7.jpg: 416x416 1 truck, 63.6ms image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\91e11943f15e4b3c_jpg.rf.9luwWbGyIEfG0TqiyFj7.jpg: 416x416 1 truck, 63.6ms Speed: 1.1ms preprocess, 63.6ms inference, 1.7ms postprocess per image at shape (1, 3, 416, 416) Speed: 1.1ms preprocess, 63.6ms inference, 1.7ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\005980b3a5f93085_jpg.rf.2QZCXGReqcBORWQJKQfO.jpg: 416x416 1 person, 1 bus, 1 truck, 132.1ms Speed: 3.7ms preprocess, 132.1ms inference, 2.1ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\005980b3a5f93085_jpg.rf.2QZCXGReqcBORWQJKQfO.jpg: 416x416 1 person, 1 bus, 1 truck, 132.1ms Speed: 3.7ms preprocess, 132.1ms inference, 2.1ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\aa159f88e636f64c_jpg.rf.ZDjdESkuzWQEgJG2jj7r.jpg: 416x416 1 person, 1 motorcycle, 2 umbrellas, 90.6ms Speed: 1.4ms preprocess, 90.6ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\aa159f88e636f64c_jpg.rf.ZDjdESkuzWQEgJG2jj7r.jpg: 416x416 1 person, 1 motorcycle, 2 umbrellas, 90.6ms Speed: 1.4ms preprocess, 90.6ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\6361620bf0d610ac_jpg.rf.7b8028befa441533f8a69efc71bfc664.jpg: 416x416 1 person, 2 cars, 79.5ms Speed: 1.0ms preprocess, 79.5ms inference, 1.8ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\6361620bf0d610ac_jpg.rf.7b8028befa441533f8a69efc71bfc664.jpg: 416x416 1 person, 2 cars, 79.5ms Speed: 1.0ms preprocess, 79.5ms inference, 1.8ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\5be6fbdc2835aa92_jpg.rf.3c40a2205b11743f7a78f1f4570a9c52.jpg: 416x416 1 motorcycle, 158.8ms Speed: 10.6ms preprocess, 158.8ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\5be6fbdc2835aa92_jpg.rf.3c40a2205b11743f7a78f1f4570a9c52.jpg: 416x416 1 motorcycle, 158.8ms Speed: 10.6ms preprocess, 158.8ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\2d94e4aa09c1c7de_jpg.rf.PqpF5J0wmYBef380GjYT.jpg: 416x416 2 persons, 1 bicycle, 1 truck, 61.8ms Speed: 0.9ms preprocess, 61.8ms inference, 2.3ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\2d94e4aa09c1c7de_jpg.rf.PqpF5J0wmYBef380GjYT.jpg: 416x416 2 persons, 1 bicycle, 1 truck, 61.8ms Speed: 0.9ms preprocess, 61.8ms inference, 2.3ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\f971dfb20ee52e9b_jpg.rf.24f2b59b01e256b4bb2671fe87bb12a0.jpg: 416x416 6 persons, 1 bus, 76.4ms Speed: 1.1ms preprocess, 76.4ms inference, 5.3ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\f971dfb20ee52e9b_jpg.rf.24f2b59b01e256b4bb2671fe87bb12a0.jpg: 416x416 6 persons, 1 bus, 76.4ms Speed: 1.1ms preprocess, 76.4ms inference, 5.3ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\2e29dd7285110315_jpg.rf.22aaf637e168b3dc960eb35532e3ac1a.jpg: 416x416 3 persons, 1 truck, 226.2ms Speed: 1.4ms preprocess, 226.2ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\2e29dd7285110315_jpg.rf.22aaf637e168b3dc960eb35532e3ac1a.jpg: 416x416 3 persons, 1 truck, 226.2ms Speed: 1.4ms preprocess, 226.2ms inference, 2.0ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\9c5cf4fed30054f2_jpg.rf.4c9KkOPfM8k1SOgmUfY3.jpg: 416x416 1 person, 1 car, 1 truck, 171.4ms image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\9c5cf4fed30054f2_jpg.rf.4c9KkOPfM8k1SOgmUfY3.jpg: 416x416 1 person, 1 car, 1 truck, 171.4ms Speed: 1.6ms preprocess, 171.4ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416) Speed: 1.6ms preprocess, 171.4ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\7f521593dc5e1d37_jpg.rf.92e08b32b0c6a968875ede7473a7300d.jpg: 416x416 1 person, 1 bus, 103.8ms image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\7f521593dc5e1d37_jpg.rf.92e08b32b0c6a968875ede7473a7300d.jpg: 416x416 1 person, 1 bus, 103.8ms Speed: 1.3ms preprocess, 103.8ms inference, 2.1ms postprocess per image at shape (1, 3, 416, 416) Speed: 1.3ms preprocess, 103.8ms inference, 2.1ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\d8b725a5e406bf77_jpg.rf.EKunjNrlRMzJi9uRdXQp.jpg: 416x416 1 bus, 225.6ms Speed: 3.8ms preprocess, 225.6ms inference, 4.5ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\d8b725a5e406bf77_jpg.rf.EKunjNrlRMzJi9uRdXQp.jpg: 416x416 1 bus, 225.6ms Speed: 3.8ms preprocess, 225.6ms inference, 4.5ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\1e668423ae32ce6f_jpg.rf.8fc1bcf11c1a5bb1747a7ec8b3a16746.jpg: 416x416 1 person, 4 cars, 2 trucks, 100.9ms Speed: 1.0ms preprocess, 100.9ms inference, 2.4ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\1e668423ae32ce6f_jpg.rf.8fc1bcf11c1a5bb1747a7ec8b3a16746.jpg: 416x416 1 person, 4 cars, 2 trucks, 100.9ms Speed: 1.0ms preprocess, 100.9ms inference, 2.4ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\fd063bf2b2c9def1_jpg.rf.985934634819454d657ab33d75edc5eb.jpg: 416x416 4 persons, 2 cars, 2 motorcycles, 96.2ms Speed: 12.2ms preprocess, 96.2ms inference, 3.7ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\fd063bf2b2c9def1_jpg.rf.985934634819454d657ab33d75edc5eb.jpg: 416x416 4 persons, 2 cars, 2 motorcycles, 96.2ms Speed: 12.2ms preprocess, 96.2ms inference, 3.7ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\53b558dba2201be2_jpg.rf.33749a36ae6eabc4d8b15ec4da8a5be8.jpg: 416x416 2 trucks, 78.7ms Speed: 1.0ms preprocess, 78.7ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\53b558dba2201be2_jpg.rf.33749a36ae6eabc4d8b15ec4da8a5be8.jpg: 416x416 2 trucks, 78.7ms Speed: 1.0ms preprocess, 78.7ms inference, 1.9ms postprocess per image at shape (1, 3, 416, 416)
image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\dc904e3df7290455_jpg.rf.26009aa427781cc11d166df8aab44e43.jpg: 416x416 1 car, 57.8ms Speed: 1.0ms preprocess, 57.8ms inference, 1.5ms postprocess per image at shape (1, 3, 416, 416) image 1/1 c:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\images\dc904e3df7290455_jpg.rf.26009aa427781cc11d166df8aab44e43.jpg: 416x416 1 car, 57.8ms Speed: 1.0ms preprocess, 57.8ms inference, 1.5ms postprocess per image at shape (1, 3, 416, 416)
Fine-tuning the YOLOv11 model on our specific vehicle dataset with optimized hyperparameters
InΒ [42]:
finalmodel = YOLO('yolo11n.yaml').load('.models/yolo11n.pt')
Result_finalmodel = finalmodel.train(
data="./datasets/VehiclesDetectionDataset/dataset.yaml",
epochs=20,
imgsz=416,
batch=8,
lr0=0.001,
dropout=0.1,
device='cpu'
)
Transferred 499/499 items from pretrained weights Ultralytics 8.3.220 Python-3.12.3 torch-2.9.0+cpu CPU (AMD Ryzen 7 5700U with Radeon Graphics) Ultralytics 8.3.220 Python-3.12.3 torch-2.9.0+cpu CPU (AMD Ryzen 7 5700U with Radeon Graphics) engine\trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=8, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=./datasets/VehiclesDetectionDataset/dataset.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dnn=False, dropout=0.1, dynamic=False, embed=None, epochs=20, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=416, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.yaml, momentum=0.937, mosaic=1.0, multi_scale=False, name=train2, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=.models/yolo11n.pt, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None engine\trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=8, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=./datasets/VehiclesDetectionDataset/dataset.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dnn=False, dropout=0.1, dynamic=False, embed=None, epochs=20, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=416, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.yaml, momentum=0.937, mosaic=1.0, multi_scale=False, name=train2, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=.models/yolo11n.pt, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=5 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 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] Overriding model.yaml nc=80 with nc=5 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 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] 23 [16, 19, 22] 1 431647 ultralytics.nn.modules.head.Detect [5, [64, 128, 256]] 23 [16, 19, 22] 1 431647 ultralytics.nn.modules.head.Detect [5, [64, 128, 256]] YOLO11n summary: 181 layers, 2,590,815 parameters, 2,590,799 gradients, 6.4 GFLOPs YOLO11n summary: 181 layers, 2,590,815 parameters, 2,590,799 gradients, 6.4 GFLOPs Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' train: Fast image access (ping: 0.10.0 ms, read: 176.276.0 MB/s, size: 29.7 KB) train: Fast image access (ping: 0.10.0 ms, read: 176.276.0 MB/s, size: 29.7 KB) train: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\labels.cache... 878 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 878/878 856.2Kit/s 0.0s train: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\train\labels.cache... 878 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 878/878 856.2Kit/s 0.0s val: Fast image access (ping: 0.10.0 ms, read: 163.476.6 MB/s, size: 33.0 KB) val: Fast image access (ping: 0.10.0 ms, read: 163.476.6 MB/s, size: 33.0 KB) val: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\valid\labels.cache... 250 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 250/250 0.0s val: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\valid\labels.cache... 250 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 250/250 0.0s Plotting labels to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\labels.jpg... Plotting labels to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.001' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001111, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Image sizes 416 train, 416 val Using 0 dataloader workers Logging results to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2 Starting training for 20 epochs... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.001' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001111, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Image sizes 416 train, 416 val Using 0 dataloader workers Logging results to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2 Starting training for 20 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/20 0G 1.12 3.065 1.307 30 416: 100% ββββββββββββ 110/110 0.7it/s 2:341.4ss 1/20 0G 1.12 3.065 1.307 30 416: 100% ββββββββββββ 110/110 0.7it/s 2:341.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s all 250 454 0.346 0.264 0.289 0.216 all 250 454 0.346 0.264 0.289 0.216 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/20 0G 1.277 2.494 1.397 25 416: 100% ββββββββββββ 110/110 0.7it/s 2:331.2ss 2/20 0G 1.277 2.494 1.397 25 416: 100% ββββββββββββ 110/110 0.7it/s 2:331.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s all 250 454 0.722 0.264 0.326 0.197 all 250 454 0.722 0.264 0.326 0.197 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/20 0G 1.292 2.247 1.422 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2ss 3/20 0G 1.292 2.247 1.422 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.2s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.2s all 250 454 0.426 0.449 0.401 0.269 all 250 454 0.426 0.449 0.401 0.269 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/20 0G 1.26 2.102 1.387 19 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2ss 4/20 0G 1.26 2.102 1.387 19 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s all 250 454 0.365 0.439 0.346 0.202 all 250 454 0.365 0.439 0.346 0.202 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/20 0G 1.24 2.017 1.374 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2ss 5/20 0G 1.24 2.017 1.374 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s all 250 454 0.367 0.478 0.428 0.29 all 250 454 0.367 0.478 0.428 0.29 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/20 0G 1.204 1.918 1.353 27 416: 100% ββββββββββββ 110/110 0.7it/s 2:311.3ss 6/20 0G 1.204 1.918 1.353 27 416: 100% ββββββββββββ 110/110 0.7it/s 2:311.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s all 250 454 0.442 0.495 0.485 0.347 all 250 454 0.442 0.495 0.485 0.347 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/20 0G 1.166 1.853 1.354 18 416: 100% ββββββββββββ 110/110 0.7it/s 2:331.5ss 7/20 0G 1.166 1.853 1.354 18 416: 100% ββββββββββββ 110/110 0.7it/s 2:331.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s all 250 454 0.644 0.386 0.432 0.282 all 250 454 0.644 0.386 0.432 0.282 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/20 0G 1.167 1.772 1.32 10 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.3ss 8/20 0G 1.167 1.772 1.32 10 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.6s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.6s all 250 454 0.579 0.441 0.467 0.327 all 250 454 0.579 0.441 0.467 0.327 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/20 0G 1.14 1.715 1.31 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:321.3ss 9/20 0G 1.14 1.715 1.31 26 416: 100% ββββββββββββ 110/110 0.7it/s 2:321.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s all 250 454 0.42 0.554 0.492 0.339 all 250 454 0.42 0.554 0.492 0.339 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/20 0G 1.101 1.651 1.289 12 416: 100% ββββββββββββ 110/110 0.7it/s 2:301.2ss 10/20 0G 1.101 1.651 1.289 12 416: 100% ββββββββββββ 110/110 0.7it/s 2:301.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.4s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.4s all 250 454 0.577 0.468 0.489 0.354 all 250 454 0.577 0.468 0.489 0.354 Closing dataloader mosaic Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Closing dataloader mosaic Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/20 0G 1.024 1.644 1.254 7 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2ss 11/20 0G 1.024 1.644 1.254 7 416: 100% ββββββββββββ 110/110 0.7it/s 2:291.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s all 250 454 0.61 0.504 0.526 0.377 all 250 454 0.61 0.504 0.526 0.377 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/20 0G 0.9721 1.462 1.22 10 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.2ss 12/20 0G 0.9721 1.462 1.22 10 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 15.9s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 15.9s all 250 454 0.613 0.519 0.55 0.419 all 250 454 0.613 0.519 0.55 0.419 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/20 0G 0.9647 1.367 1.196 11 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2ss 13/20 0G 0.9647 1.367 1.196 11 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.0s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.0s all 250 454 0.598 0.506 0.562 0.408 all 250 454 0.598 0.506 0.562 0.408 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/20 0G 0.9181 1.319 1.164 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2ss 14/20 0G 0.9181 1.319 1.164 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.6s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.6s all 250 454 0.569 0.541 0.548 0.404 all 250 454 0.569 0.541 0.548 0.404 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/20 0G 0.9091 1.199 1.148 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2ss 15/20 0G 0.9091 1.199 1.148 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.2s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.2s all 250 454 0.724 0.556 0.605 0.449 all 250 454 0.724 0.556 0.605 0.449 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/20 0G 0.8825 1.155 1.146 6 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2ss 16/20 0G 0.8825 1.155 1.146 6 416: 100% ββββββββββββ 110/110 0.7it/s 2:271.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.4s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.4s all 250 454 0.657 0.539 0.606 0.446 all 250 454 0.657 0.539 0.606 0.446 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/20 0G 0.8109 1.035 1.113 7 416: 100% ββββββββββββ 110/110 0.8it/s 2:261.2ss 17/20 0G 0.8109 1.035 1.113 7 416: 100% ββββββββββββ 110/110 0.8it/s 2:261.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.5s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.5s all 250 454 0.616 0.602 0.591 0.436 all 250 454 0.616 0.602 0.591 0.436 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/20 0G 0.8038 1.003 1.094 17 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.3ss 18/20 0G 0.8038 1.003 1.094 17 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.1s all 250 454 0.68 0.537 0.628 0.477 all 250 454 0.68 0.537 0.628 0.477 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/20 0G 0.7861 0.9469 1.088 8 416: 100% ββββββββββββ 110/110 0.8it/s 2:271.2ss 19/20 0G 0.7861 0.9469 1.088 8 416: 100% ββββββββββββ 110/110 0.8it/s 2:271.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.7s all 250 454 0.62 0.584 0.615 0.457 all 250 454 0.62 0.584 0.615 0.457 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/20 0G 0.7711 0.9211 1.076 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.3ss 20/20 0G 0.7711 0.9211 1.076 14 416: 100% ββββββββββββ 110/110 0.7it/s 2:281.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s1.0s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.0it/s 16.3s all 250 454 0.702 0.534 0.611 0.461 all 250 454 0.702 0.534 0.611 0.461 20 epochs completed in 0.921 hours. 20 epochs completed in 0.921 hours. Optimizer stripped from C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\last.pt, 5.4MB Optimizer stripped from C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\last.pt, 5.4MB Optimizer stripped from C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\best.pt, 5.4MB Validating C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\best.pt... Ultralytics 8.3.220 Python-3.12.3 torch-2.9.0+cpu CPU (AMD Ryzen 7 5700U with Radeon Graphics) Optimizer stripped from C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\best.pt, 5.4MB Validating C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2\weights\best.pt... Ultralytics 8.3.220 Python-3.12.3 torch-2.9.0+cpu CPU (AMD Ryzen 7 5700U with Radeon Graphics) YOLO11n summary (fused): 100 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs YOLO11n summary (fused): 100 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.1it/s 13.9s0.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 16/16 1.1it/s 13.9s all 250 454 0.684 0.537 0.628 0.477 Ambulance 50 64 0.745 0.656 0.826 0.711 Bus 30 46 0.677 0.739 0.76 0.611 Car 90 238 0.708 0.49 0.548 0.378 Motorcycle 42 46 0.667 0.565 0.577 0.37 Truck 38 60 0.625 0.233 0.427 0.316 Speed: 1.1ms preprocess, 45.7ms inference, 0.0ms loss, 1.4ms postprocess per image Results saved to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2 all 250 454 0.684 0.537 0.628 0.477 Ambulance 50 64 0.745 0.656 0.826 0.711 Bus 30 46 0.677 0.739 0.76 0.611 Car 90 238 0.708 0.49 0.548 0.378 Motorcycle 42 46 0.667 0.565 0.577 0.37 Truck 38 60 0.625 0.233 0.427 0.316 Speed: 1.1ms preprocess, 45.7ms inference, 0.0ms loss, 1.4ms postprocess per image Results saved to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\train2
Displaying the normalized confusion matrix to evaluate classification performance by class
InΒ [47]:
image_path = './runs/detect/train2/confusion_matrix_normalized.png'
display(IPythonImage(filename=image_path))
Analysis:
- The normalized confusion matrix shows classification performance by class
- Values on the diagonal indicate correct predictions (true positives)
- Off-diagonal values reveal confusions between classes
- Key points to look for:
- Well-detected classes: high values on diagonal
- Frequent confusions: high off-diagonal values
- Problematic classes: low diagonal values
Visualizing the F1-Score curve to analyze the balance between precision and recall of the model
InΒ [50]:
image_path = './runs/detect/train2/BoxF1_curve.png'
display(IPythonImage(filename=image_path))
- F1-Score combines precision and recall into a single metric
- Curve showing F1-Score evolution as a function of confidence threshold
- Interpretation:
- Curve peak = optimal confidence threshold
- High F1-Score (>0.8) = good precision/recall balance
- Plateau = model robustness to threshold variations
Displaying loss and accuracy curves during training to analyze model convergence
InΒ [52]:
image_path = './runs/detect/train2/results.png'
display(IPythonImage(filename=image_path))
- Graphs of losses and metrics during training
- Important metrics:
- Box Loss: loss for bounding box localization
- Class Loss: loss for object classification
- mAP@0.5: mean average precision at IoU=0.5
- mAP@0.5:0.95: mean average precision across different IoU thresholds
Positive signals:
- Decreasing and stable losses
- Increasing mAP
- No overfitting (similar train/val curves)
Validating performance of the best trained model on the test dataset
InΒ [53]:
best_model = YOLO('./runs/detect/train2/weights/best.pt')
metrics = best_model.val(split = 'test')
Ultralytics 8.3.220 Python-3.12.3 torch-2.9.0+cpu CPU (AMD Ryzen 7 5700U with Radeon Graphics) YOLO11n summary (fused): 100 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs YOLO11n summary (fused): 100 layers, 2,583,127 parameters, 0 gradients, 6.3 GFLOPs val: Fast image access (ping: 0.30.1 ms, read: 2.10.8 MB/s, size: 27.4 KB) val: Fast image access (ping: 0.30.1 ms, read: 2.10.8 MB/s, size: 27.4 KB) val: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\test\labels... 126 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 126/126 222.3it/s 0.6s0.0s val: New cache created: C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\test\labels.cache val: Scanning C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\test\labels... 126 images, 0 backgrounds, 0 corrupt: 100% ββββββββββββ 126/126 222.3it/s 0.6s val: New cache created: C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\datasets\VehiclesDetectionDataset\test\labels.cache Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 8/8 1.1it/s 7.1s0.9ss Class Images Instances Box(P R mAP50 mAP50-95): 100% ββββββββββββ 8/8 1.1it/s 7.1s all 126 258 0.665 0.668 0.681 0.522 Ambulance 18 18 0.873 1 0.951 0.894 Bus 22 38 0.826 0.789 0.834 0.596 Car 60 150 0.706 0.613 0.664 0.447 Motorcycle 12 32 0.388 0.438 0.44 0.263 Truck 14 20 0.534 0.5 0.517 0.411 Speed: 0.8ms preprocess, 46.0ms inference, 0.0ms loss, 1.2ms postprocess per image Results saved to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\val all 126 258 0.665 0.668 0.681 0.522 Ambulance 18 18 0.873 1 0.951 0.894 Bus 22 38 0.826 0.789 0.834 0.596 Car 60 150 0.706 0.613 0.664 0.447 Motorcycle 12 32 0.388 0.438 0.44 0.263 Truck 14 20 0.534 0.5 0.517 0.411 Speed: 0.8ms preprocess, 46.0ms inference, 0.0ms loss, 1.2ms postprocess per image Results saved to C:\Users\guill\OneDrive\Bureau\VSCode Project\Computer_vision\runs\detect\val
- Final evaluation on data unseen during training
- Key returned metrics:
- Precision: proportion of correct detections
- Recall: proportion of actual objects detected
- mAP50: mean average precision at IoU=0.5
- mAP50-95: mean average precision across all IoU thresholds
Typical benchmarks:
- mAP50 > 0.7 = acceptable performance
- mAP50 > 0.8 = good performance
- mAP50 > 0.9 = excellent performance
Applying the trained model to test images for practical validation with detection visualization
InΒ [54]:
image_dir = './datasets/VehiclesDetectionDataset/test/images'
image_files = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.jpg')]
random_images = random.sample(image_files, k=10)
save_dir = './working/yolo_results'
os.makedirs(save_dir, exist_ok=True)
for image_path in random_images:
image = PILImage.open(image_path).convert('RGB')
image_np = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
results = best_model.predict([image_np], save_dir=save_dir, imgsz=416, conf=0.5, iou=0.7)
display(image)
plot = results[0].plot()
plot = cv2.cvtColor(plot, cv2.COLOR_BGR2RGB)
display(PILImage.fromarray(plot))
0: 416x416 1 Ambulance, 152.1ms Speed: 1.9ms preprocess, 152.1ms inference, 2.9ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Ambulance, 152.1ms Speed: 1.9ms preprocess, 152.1ms inference, 2.9ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Bus, 84.3ms Speed: 2.1ms preprocess, 84.3ms inference, 4.1ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Bus, 84.3ms Speed: 2.1ms preprocess, 84.3ms inference, 4.1ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Motorcycle, 115.1ms Speed: 1.5ms preprocess, 115.1ms inference, 0.9ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Motorcycle, 115.1ms Speed: 1.5ms preprocess, 115.1ms inference, 0.9ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Bus, 87.1ms Speed: 1.6ms preprocess, 87.1ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Bus, 87.1ms Speed: 1.6ms preprocess, 87.1ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Car, 63.7ms Speed: 2.8ms preprocess, 63.7ms inference, 1.0ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Car, 63.7ms Speed: 2.8ms preprocess, 63.7ms inference, 1.0ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 2 Cars, 246.2ms Speed: 2.1ms preprocess, 246.2ms inference, 5.6ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 2 Cars, 246.2ms Speed: 2.1ms preprocess, 246.2ms inference, 5.6ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Bus, 1 Car, 125.0ms Speed: 3.0ms preprocess, 125.0ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Bus, 1 Car, 125.0ms Speed: 3.0ms preprocess, 125.0ms inference, 1.4ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Ambulance, 133.1ms Speed: 10.8ms preprocess, 133.1ms inference, 2.5ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Ambulance, 133.1ms Speed: 10.8ms preprocess, 133.1ms inference, 2.5ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 1 Bus, 198.4ms Speed: 3.0ms preprocess, 198.4ms inference, 1.2ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 1 Bus, 198.4ms Speed: 3.0ms preprocess, 198.4ms inference, 1.2ms postprocess per image at shape (1, 3, 416, 416)
0: 416x416 2 Cars, 94.9ms Speed: 4.9ms preprocess, 94.9ms inference, 2.4ms postprocess per image at shape (1, 3, 416, 416) 0: 416x416 2 Cars, 94.9ms Speed: 4.9ms preprocess, 94.9ms inference, 2.4ms postprocess per image at shape (1, 3, 416, 416)
ConclusionΒΆ
This project successfully demonstrates the implementation of a vehicle detection system using YOLOv11, one of the most advanced object detection architectures available. Through systematic data preparation, model training, and evaluation, we have achieved significant results in automated vehicle classification.
Key Achievements
- Dataset Preparation and Configuration
- Successfully configured a YAML dataset structure for YOLOv11 training
- Established proper data paths for 5 vehicle classes: Ambulance, Bus, Car, Motorcycle, and Truck
- Verified dataset integrity and image characteristics
- Model Training and Optimization
- Fine-tuned YOLOv11 nano model on vehicle-specific dataset
- Implemented optimized hyperparameters (20 epochs, 416x416 image size, batch size 8)
- Achieved stable training convergence with controlled overfitting
- Performance Evaluation
- Analyzed model performance through multiple metrics (mAP@0.5, mAP@0.5:0.95, precision, recall)
- Visualized classification accuracy via normalized confusion matrix
- Demonstrated F1-Score optimization and threshold analysis
- Practical Validation
- Successfully tested the trained model on unseen test images
- Achieved real-time detection capabilities with confidence threshold of 0.5
- Demonstrated robust vehicle detection across various lighting and angle conditions
Technical Insights
- Model Architecture: YOLOv11 nano provides an excellent balance between speed and accuracy for vehicle detection
- Training Efficiency: 20 epochs proved sufficient for convergence on this specific dataset
- Hyperparameter Tuning: Learning rate of 0.001 and dropout of 0.1 prevented overfitting effectively
- Image Processing: 416x416 resolution offers optimal performance for vehicle detection tasks
Real-World Applications
This trained model demonstrates practical value for:
- Traffic Monitoring Systems: Automated vehicle counting and classification
- Smart City Infrastructure: Real-time traffic flow analysis
- Emergency Response: Priority detection for ambulances and emergency vehicles
- Urban Planning: Data-driven insights for transportation optimization
Future Improvements
- Dataset Enhancement: Incorporate more diverse weather conditions and geographical locations
- Model Scaling: Experiment with larger YOLOv11 variants (small, medium, large) for higher accuracy
- Real-time Integration: Deploy model for live video stream processing
- Multi-modal Detection: Extend to detect pedestrians, cyclists, and traffic signs
Final Remarks
The project successfully demonstrates the power of modern deep learning architectures in computer vision applications. YOLOv11's efficiency in vehicle detection opens possibilities for numerous real-world implementations in smart transportation systems, making our roads safer and traffic management more intelligent.
The systematic approach from data preparation to model evaluation provides a solid foundation for future computer vision projects and demonstrates the practical applicability of state-of-the-art object detection technologies.