5/22/25, 10:17 PM image Processing 3 - Colab

!pip install -q ultralytics

!pip install ultralytics==8.0.191

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Collecting thop>=0.1.1 (from ultralytics==8.0.191)
Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)
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Downloading ultralytics-8.0.191-py3-none-any.whl (615 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 615.8/615.8 kB 16.7 MB/s eta 0:00:00
Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)
Installing collected packages: thop, ultralytics
Attempting uninstall: ultralytics
Found existing installation: ultralytics 8.3.142
Uninstalling ultralytics-8.3.142:
Successfully uninstalled ultralytics-8.3.142
Successfully installed thop-0.1.1.post2209072238 ultralytics-8.0.191

 

import zipfile
import os

# Ensure the path is correct and the file exists.

zip_path = '/content/cardetection.zip' # Update if necessary
extract_to = 'car_dataset'

os.makedirs(extract_to, exist_ok=True)

# Check if the file exists before attempting to open it.

if os.path.exists(zip_path):
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(extract_to)
print("Dataset extracted successfully!")
else:
print(f"Error: File not found at {zip_path}")

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 1/19
5/22/25, 10:17 PM image Processing 3 - Colab

Dataset extracted successfully!

!ls /content

car_dataset cardetection.zip sample_data

import os

data_path = 'car_dataset'
for root, dirs, files in os.walk(data_path):
print(f"Folder: {root}")
print(f"Subfolders: {dirs}")
print(f"Files sample: {files[:5]}")
print('-' * 40)

Folder: car_dataset
Subfolders: ['car']
Files sample: ['video.mp4']
----------------------------------------
Folder: car_dataset/car
Subfolders: ['test', 'train', 'valid']
Files sample: ['data.yaml', 'README.roboflow.txt', 'README.dataset.txt']
----------------------------------------
Folder: car_dataset/car/test
Subfolders: ['labels', 'images']
Files sample: []
----------------------------------------
Folder: car_dataset/car/test/labels
Subfolders: []
Files sample: ['001693_JPG_jpg.rf.9c74f3077d05257d04e7a58a48642fe0.txt', '001996_jpg.rf.e04928f5210adad4b15f5a851c83b514.txt', '001289_j
----------------------------------------
Folder: car_dataset/car/test/images
Subfolders: []
Files sample: ['000500_jpg.rf.b742528d52c611a0eb6f2d0453a3c81a.jpg', '000166_jpg.rf.f37d1c743a28243f0e0f2482f2a67290.jpg', '00003_00044_
----------------------------------------
Folder: car_dataset/car/train
Subfolders: ['labels', 'images']
Files sample: []
----------------------------------------
Folder: car_dataset/car/train/labels
Subfolders: []
Files sample: ['FisheyeCamera_1_01055_png.rf.bf7c9d64a08a26f03ae9d5480af21b4f.txt', '00004_00033_00001_png.rf.de63398c66037392921471cfc3
----------------------------------------
Folder: car_dataset/car/train/images
Subfolders: []
Files sample: ['FisheyeCamera_1_00029_png.rf.385403b0b6cdac4328ddbd14e877b56d.jpg', '00001_00027_00019_png.rf.5fb9f4384898315b802072a0ec
----------------------------------------
Folder: car_dataset/car/valid
Subfolders: ['labels', 'images']
Files sample: []
----------------------------------------
Folder: car_dataset/car/valid/labels
Subfolders: []
Files sample: ['FisheyeCamera_1_00573_png.rf.536dbba2a930dc75169d0868b51c285e.txt', '001805_JPG_jpg.rf.6be207f36b3528388ef38d83a0ecafcb.
----------------------------------------
Folder: car_dataset/car/valid/images
Subfolders: []
Files sample: ['00008_00017_00021_png.rf.e3539348f10ebb2a43c22c2ff8ead631.jpg', 'road107_png.rf.9470544fddc172eb51c93f29188ec99c.jpg', '
----------------------------------------

 

data_path = 'car_dataset/car/train' # <- update to correct folder

image_folder = os.path.join(data_path, 'images')
label_folder = os.path.join(data_path, 'labels')

image_files = sorted([f for f in os.listdir(image_folder) if f.endswith(('.jpg', '.png'))])

label_files = sorted([f for f in os.listdir(label_folder) if f.endswith('.txt')])

print(f"Number of images: {len(image_files)}")

print(f"Number of label files: {len(label_files)}")
print("Sample image files:", image_files[:3])
print("Sample label files:", label_files[:3])

Number of images: 3530

Number of label files: 3530
Sample image files: ['000000_jpg.rf.b11f308f16626f9f795a148029c46d10.jpg', '000005_jpg.rf.d730849ae93a7c211a7c8f57ed851028.jpg', '000007

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 2/19
5/22/25, 10:17 PM image Processing 3 - Colab
Sample label files: ['000000_jpg.rf.b11f308f16626f9f795a148029c46d10.txt', '000005_jpg.rf.d730849ae93a7c211a7c8f57ed851028.txt', '000007

import cv2
import matplotlib.pyplot as plt
 

def plot_image_with_boxes(image_path, label_path):

img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h, w, _ = img.shape

with open(label_path, 'r') as f:

boxes = f.readlines()

for box in boxes:

cls, x_center, y_center, bw, bh = map(float, box.strip().split())

# Convert normalized coordinates to pixels

x1 = int((x_center - bw / 2) * w)
y1 = int((y_center - bh / 2) * h)
x2 = int((x_center + bw / 2) * w)
y2 = int((y_center + bh / 2) * h)

cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,0), 2)

cv2.putText(img, str(int(cls)), (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255,0,0), 2)

plt.figure(figsize=(10, 8))
plt.imshow(img)
plt.axis('off')
plt.show()

# Example usage with first image and label

plot_image_with_boxes(os.path.join(image_folder, image_files[0]), os.path.join(label_folder, label_files[0]))

 

import os

# Paths to your label folders

train_labels_dir = 'car_dataset/car/train/labels'
valid_labels_dir = 'car_dataset/car/valid/labels'
test_labels_dir = 'car_dataset/car/test/labels'

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 3/19
5/22/25, 10:17 PM image Processing 3 - Colab

def get_classes_from_labels(label_dir):
class_ids = set()
for label_file in os.listdir(label_dir):
if label_file.endswith('.txt'):
with open(os.path.join(label_dir, label_file), 'r') as f:
for line in f:
class_id = int(line.split()[0])
class_ids.add(class_id)
return class_ids

# Gather all class IDs from train, valid, and test label folders
classes_train = get_classes_from_labels(train_labels_dir)
classes_valid = get_classes_from_labels(valid_labels_dir)
classes_test = get_classes_from_labels(test_labels_dir)

all_classes = classes_train.union(classes_valid).union(classes_test)
num_classes = len(all_classes)

print(f"Detected class IDs: {sorted(all_classes)}")

print(f"Number of classes: {num_classes}")

# Manually define class names here, or set generic names if unknown

# For example, if 3 classes detected:
class_names = [f'class{i}' for i in range(num_classes)] # change to real names if you have them

# Prepare YAML content

yaml_content = f"""
train: car_dataset/car/train/images
val: car_dataset/car/valid/images
test: car_dataset/car/test/images

nc: {num_classes}
names: {class_names}
"""

# Write to data.yaml
with open('data.yaml', 'w') as f:
f.write(yaml_content.strip())

print("data.yaml created successfully!")

Detected class IDs: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

Number of classes: 15
data.yaml created successfully!

pip install -U ultralytics

Using cached ultralytics-8.3.142-py3-none-any.whl.metadata (37 kB)

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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 4/19
5/22/25, 10:17 PM image Processing 3 - Colab
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Using cached ultralytics-8.3.142-py3-none-any.whl (1.0 MB)
Installing collected packages: ultralytics
Attempting uninstall: ultralytics
Found existing installation: ultralytics 8.0.191
Uninstalling ultralytics-8.0.191:
Successfully uninstalled ultralytics-8.0.191
S f ll i t ll d lt l ti 8 3 142

from ultralytics import YOLO

# Load the pretrained YOLOv8n model by model name string only — let ultralytics handle the download & load
model = YOLO('yolov8n.pt')

# Train with your dataset yaml file

model.train(data='/content/data.yaml', epochs=10, imgsz=640)


 

0.61962, 0.62062, 0.62162, 0.62262, 0.62362,

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 5/19
5/22/25, 10:17 PM image Processing 3 - Colab
0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 
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0.71572, 0.71672, 0.71772, 0.71872, 0.71972,

from ultralytics import YOLO

import matplotlib.pyplot as plt
from PIL import Image
import os

# Load the trained model

model = YOLO('runs/detect/train/weights/best.pt') # Adjust path if needed

# Step 1: Evaluate on the validation set

metrics = model.val(data='data.yaml') # Or your exact path
print("Validation Results:", metrics)

# Step 2: Predict on new images

results = model.predict(source='/content/car_dataset/car/test/images', save=True, conf=0.25)

# Optional: Display one of the predictions

predicted_img_path = os.path.join('runs/detect/predict', os.listdir('runs/detect/predict')[0])
img = Image.open(predicted_img_path)
plt.imshow(img)
plt.axis('off')
plt.title("Sample Prediction")
plt.show()

# Step 3: Export the model for deployment

model.export(format='onnx') # Options: 'torchscript', 'onnx', 'openvino', 'engine', etc.


 

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 6/19
5/22/25, 10:17 PM image Processing 3 - Colab

Ultralytics 8.3.142 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)

Model summary (fused): 72 layers, 3,008,573 parameters, 0 gradients, 8.1 GFLOPs
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 778.2±419.2 MB/s, size: 19.7 KB)
val: Scanning /content/car_dataset/car/valid/labels.cache... 801 images, 0 backgrounds, 0 corrupt: 100%|██████████| 801/801 [00:00<?, ?i
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 51/51 [00:13<00:00, 3.73it/s
all 801 944 0.924 0.827 0.916 0.786
class0 87 122 0.814 0.644 0.745 0.419
class1 74 108 0.839 0.704 0.792 0.46
class3 52 52 0.904 0.908 0.958 0.856
class4 17 17 0.872 0.706 0.895 0.816
class5 60 60 0.946 0.876 0.967 0.883
class6 56 56 0.984 0.893 0.977 0.848
class7 71 74 0.972 0.932 0.965 0.895
class8 53 55 0.935 0.909 0.974 0.85
class9 68 71 0.986 0.789 0.935 0.844
class10 76 76 0.951 0.882 0.942 0.854
class11 78 78 0.983 0.936 0.966 0.883
class12 56 56 0.957 0.804 0.913 0.803
class13 38 38 0.799 0.658 0.799 0.688
class14 81 81 0.991 0.938 0.994 0.913
Speed: 0.9ms preprocess, 4.5ms inference, 0.0ms loss, 2.6ms postprocess per image
Results saved to runs/detect/val
Validation Results: ultralytics.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([ 0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])

box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7d4f37a4fb50>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.00
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 7/19
5/22/25, 10:17 PM image Processing 3 - Colab
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 8/19
5/22/25, 10:17 PM image Processing 3 - Colab
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fitness: np.float64(0.799405115099259)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.41882, 0.46001, 0.78647, 0.85635, 0.81591, 0.88295, 0.84795, 0.89489, 0.84976, 0.
names: {0: 'class0', 1: 'class1', 2: 'class2', 3: 'class3', 4: 'class4', 5: 'class5', 6: 'class6', 7: 'class7', 8: 'class8', 9: 'class9'
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.9237832867920784), 'metrics/recall(B)': np.float64(0.8270031328037614), 'metrics/mAP
save_dir: PosixPath('runs/detect/val')
speed: {'preprocess': 0.9035659950075413, 'inference': 4.4998729300881495, 'loss': 0.000900782770962935, 'postprocess': 2.55558162421913
task: 'detect'

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g / / / _ / / / g / _ _ _p g jpg ,
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 10/19
5/22/25, 10:17 PM image Processing 3 - Colab
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5/22/25, 10:17 PM image Processing 3 - Colab
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 12/19
5/22/25, 10:17 PM image Processing 3 - Colab
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 13/19
5/22/25, 10:17 PM image Processing 3 - Colab
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 14/19
5/22/25, 10:17 PM image Processing 3 - Colab
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image 452/638 /content/car_dataset/car/test/images/2257b962-595c-43ae-8f0d-575f1b55beb8_jpg.rf.d8ba84a72e00584f12f54d3cf03c8dc0.jpg: 640
image 453/638 /content/car_dataset/car/test/images/24085f02-5dde-4b59-ad8e-887935bd08e1_jpg.rf.d216c61af0d1adf927b98f242a4fe85f.jpg: 640
image 454/638 /content/car_dataset/car/test/images/27beeb65-e6ea-4c4a-9dea-597500f26e7f_jpg.rf.29ca4c588d77178889b70d59993eb0ff.jpg: 640
image 455/638 /content/car_dataset/car/test/images/643854b9-b9d9-4b74-99fb-830fde4a0b74_jpg.rf.4d252deec56db095b1acab7c1c0f1bdc.jpg: 640
image 456/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00000_png.rf.51e7c2d131818f5ac51613cb3d152380.jpg: 640x640 (no detect
image 457/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00005_png.rf.2c3a5ef517c817ce7872439ac76a7c08.jpg: 640x640 (no detect
image 458/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00012_png.rf.72d6e678caad674f4fe8c948471cf743.jpg: 640x640 1 class12,
image 459/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00018_png.rf.765c48e66c9a0a7d70f6e4b0af00494c.jpg: 640x640 (no detect
image 460/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00021_png.rf.703e3c27438f8fe990ef8ffbd48f2b51.jpg: 640x640 (no detect
image 461/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00022_png.rf.783465f75a2393c7aaa0ec3c3cd3ed4d.jpg: 640x640 (no detect
image 462/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00023_png.rf.cd1aa333b9e78ad2f7b2de3c154e7429.jpg: 640x640 (no detect
image 463/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00027_png.rf.dfb965799faeb1514f927e69c5ec0fd2.jpg: 640x640 (no detect
image 464/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00028_png.rf.f6f0a58dd52d1cf0fe75c8496a37b067.jpg: 640x640 1 class4,
image 465/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00036_png.rf.a4fcf0fd896e50b389c7a93fae4d3a07.jpg: 640x640 (no detect
image 466/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00041_png.rf.402d963ad90df5846463e66a301debd3.jpg: 640x640 (no detect
image 467/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00089_png.rf.3a437fe3f7f2a546183b5ef50bf92eab.jpg: 640x640 1 class0,
image 468/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00095_png.rf.7339640275f9c4483536f89fb2c815e0.jpg: 640x640 (no detect
image 469/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00097_png.rf.fb72097a4038b905024bea71b14c1434.jpg: 640x640 (no detect
image 470/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00098_png.rf.937d582ee38fb9c9007c37c872df3176.jpg: 640x640 (no detect
image 471/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00100_png.rf.683a106328c4647fb8dcd00c1c2af90f.jpg: 640x640 (no detect
image 472/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00104_png.rf.4b7fdfa8ad94152180492a204dfd629d.jpg: 640x640 (no detect
image 473/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00142_png.rf.def6aeee07cff082ea33cd3d659e7024.jpg: 640x640 (no detect
image 474/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00150_png.rf.59de5a3cfa738990cf21ba7d18b79444.jpg: 640x640 (no detect
image 475/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00151_png.rf.ff1355b3e842019c0228380552ea0c2c.jpg: 640x640 (no detect
image 476/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00153_png.rf.19444df592470cf02432f62dfd784f12.jpg: 640x640 (no detect
image 477/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00154_png.rf.be661e0bba8aead34dbcded1d4e5b260.jpg: 640x640 (no detect
image 478/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00194_png.rf.21c8dbe947c18776eaab4e5caec1585b.jpg: 640x640 (no detect
image 479/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00195_png.rf.c958e21bb68e76adac7817623d7c1f75.jpg: 640x640 1 class0,
image 480/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00202_png.rf.ee30ccd577d5cfc9c2f6c16edaeacdf2.jpg: 640x640 2 class0s,
image 481/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00204_png.rf.19edfa32d48b66e0365f54c7325dcbd4.jpg: 640x640 2 class0s,
image 482/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00211_png.rf.4d937346689834589353be2d154dc924.jpg: 640x640 2 class0s,
image 483/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00230_png.rf.0abfb32b639a38aefee3263df7d3733e.jpg: 640x640 2 class1s,
image 484/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00230_png.rf.de641278e85b0675e5c4ec40b2fdf74f.jpg: 640x640 2 class1s,
image 485/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00239_png.rf.d6a3e883bbb92412f2642432771f2d09.jpg: 640x640 1 class0,
image 486/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00257_png.rf.fd48c32db227dac063bcd71340f4f334.jpg: 640x640 1 class0,
image 487/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00282_png.rf.8432bcb7705139cd0bcd1421987aaff7.jpg: 640x640 1 class0,
image 488/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00283_png.rf.e88cf39eb00a52dfcceb2fcfddf89260.jpg: 640x640 1 class0,
image 489/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00285_png.rf.cbc33d5d21aad4b5d0d195479cb4f9ef.jpg: 640x640 (no detect
image 490/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00298_png.rf.a4bc4257abfe5a5f97a7ab4671d5a682.jpg: 640x640 1 class0,
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image 492/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00314_png.rf.f10aa9e570183b2892515007c415b9cf.jpg: 640x640 1 class1,
image 493/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00315_png.rf.14c625eca5ff0f371a2459cb470b6dd5.jpg: 640x640 1 class1,
image 494/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00351_png.rf.0c9f77a62c69eb9b4b91da5b1454eda5.jpg: 640x640 (no detect
image 495/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00364_png.rf.a9a2ae260182758dd32759d27a3f08ae.jpg: 640x640 1 class4,
image 496/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00382_png.rf.da9f23f9bb25ab0a204cb881a5ccf798.jpg: 640x640 (no detect
image 497/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00454_png.rf.69544c267e97e6de7d8ff36d70f8a8f6.jpg: 640x640 (no detect
image 498/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00471_png.rf.eb0a04fb4ec3ae41bf4b96e27bd5d26d.jpg: 640x640 2 class0s,
image 499/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00476_png.rf.9039468fbc1dab58476b697e479807f8.jpg: 640x640 2 class0s,
image 500/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00479_png.rf.f7e77b725ba1e70b5dc8b0697e0b1f92.jpg: 640x640 2 class0s,
image 501/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00480_png.rf.ff94615495e0c4eb7cd3a1cfce3cf2cd.jpg: 640x640 2 class0s,
image 502/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00509_png.rf.a4fdcf5053bc422a3b30b4cef69e5ea6.jpg: 640x640 1 class1,
image 503/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00587_png.rf.8f637c2480c931d014f8cc2877541abd.jpg: 640x640 (no detect
image 504/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00594_png.rf.dc0e60f4633360af03461b14c31a1454.jpg: 640x640 (no detect
image 505/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00600_png.rf.99bc40dcb86cb45b4425a2604dd3cd8d.jpg: 640x640 (no detect
image 506/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00604_png.rf.fd483074d74e8bee0d71bb87e77ab3ef.jpg: 640x640 1 class1,
image 507/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00615_png.rf.6b8b9a4df61e83d15119053ea613f434.jpg: 640x640 1 class1,
image 508/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00631_png.rf.ef7fde7123c71d075628496a468eb1df.jpg: 640x640 1 class1,
image 509/638 /content/car_dataset/car/test/images/FisheyeCamera_1_00922_png.rf.408835a278b9c700eb1a2f358ca58d07.jpg: 640x640 (no detect
image 510/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01013_png.rf.9464970b75fa02363baae3c682e99f39.jpg: 640x640 (no detect
image 511/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01026_png.rf.dfb591570ded3c052e6641cb4f2d80c1.jpg: 640x640 2 class0s,
image 512/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01037_png.rf.870a2a09f0f8cb2a0b5f4a4a3b3ccadc.jpg: 640x640 1 class0,
image 513/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01082_png.rf.da2e5bebfb023e66aacd368902a130ef.jpg: 640x640 1 class1,
image 514/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01086_png.rf.dd1b44b6d6e2e2da3f3f6ab8f823bc01.jpg: 640x640 1 class1,
image 515/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01090_png.rf.663e34306a969d1e2d3f1ca290e1ddd7.jpg: 640x640 1 class1,
image 516/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01098_png.rf.e141b30ac0a11cc0c857435f655656fc.jpg: 640x640 1 class1,
image 517/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01099_png.rf.d3e2606bc19452ecaec7548723f05a0b.jpg: 640x640 1 class1,
image 518/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01104_png.rf.1c0a4fa66929930288c5ff6e293ca501.jpg: 640x640 1 class1,
image 519/638 /content/car_dataset/car/test/images/FisheyeCamera_1_01147_png.rf.4b377ccbafe830dd921c6b606ab4f94a.jpg: 640x640 1 class0,
image 520/638 /content/car_dataset/car/test/images/b413caec-028d-48e6-922b-fc63db1b7ebb_jpg.rf.259a6b31980f35168c8ec00a5e80c263.jpg: 640
image 521/638 /content/car_dataset/car/test/images/road104_png.rf.4468adcd7c2dcfc04377146de785c0a6.jpg: 640x640 1 class11, 7.5ms
image 522/638 /content/car_dataset/car/test/images/road108_png.rf.9655ebb7a27af56ab1d0360d10685ec8.jpg: 640x640 (no detections), 6.4ms
image 523/638 /content/car_dataset/car/test/images/road109_png.rf.c7fd39b9ac59c325ec73dc6b5ece99fd.jpg: 640x640 1 class7, 6.3ms
image 524/638 /content/car_dataset/car/test/images/road112_png.rf.b765c2b5d3a2145d317b0ceadfb08464.jpg: 640x640 1 class5, 7.1ms
image 525/638 /content/car_dataset/car/test/images/road11_png.rf.40b76142a629faa238c042bcb209cc27.jpg: 640x640 2 class0s, 8.0ms
image 526/638 /content/car_dataset/car/test/images/road157_png.rf.e48c88408b366592b950018ee5d6426d.jpg: 640x640 1 class8, 7.0ms
image 527/638 /content/car_dataset/car/test/images/road16_png.rf.9aba07c7ac70aab405028e7e8a750fb7.jpg: 640x640 1 class1, 7.3ms
image 528/638 /content/car_dataset/car/test/images/road196_png.rf.6aa5da1b76ad6487d317d27963cc00d1.jpg: 640x640 1 class8, 6.9ms
image 529/638 /content/car_dataset/car/test/images/road19_png.rf.05e1657af2361aab3c4d5deb43b29087.jpg: 640x640 1 class0, 7.9ms
image 530/638 /content/car_dataset/car/test/images/road243_png.rf.555053220adbac3849def1e4105008fd.jpg: 640x640 2 class3s, 6.5ms
image 531/638 /content/car_dataset/car/test/images/road254_png.rf.5ed92d428851755882973a8e28944076.jpg: 640x640 1 class8, 6.3ms
image 532/638 /content/car_dataset/car/test/images/road260_png.rf.47ad1e33078da07b4bf3b0e5712c926d.jpg: 640x640 1 class8, 7.9ms
image 533/638 /content/car_dataset/car/test/images/road277_png.rf.3f53c2534447d823337f731b63c65c75.jpg: 640x640 1 class8, 6.5ms
image 534/638 /content/car_dataset/car/test/images/road286_png.rf.702db0cc9906e626d838a77a4e79244d.jpg: 640x640 1 class8, 6.5ms
image 535/638 /content/car_dataset/car/test/images/road288_png.rf.db64fffa70c02e58aabcbb10e3a19798.jpg: 640x640 1 class8, 6.1ms
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https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 15/19
5/22/25, 10:17 PM image Processing 3 - Colab
image 536/638 /content/car_dataset/car/test/images/road289_png.rf.ba6012fe8a27d9f53d8d66bb29db5ad8.jpg: 640x640 1 class1, 1 class14, 6.4
image 537/638 /content/car_dataset/car/test/images/road28_png.rf.d0de3563c76ae8b60ecdaf41757d472b.jpg: 640x640 2 class1s, 8.4ms
image 538/638 /content/car_dataset/car/test/images/road305_png.rf.f807df671677244a7387d2ffbe200f58.jpg: 640x640 1 class8, 6.4ms
image 539/638 /content/car_dataset/car/test/images/road312_png.rf.ad2229fb2f7e136d7747b9568f121af4.jpg: 640x640 1 class8, 6.8ms
image 540/638 /content/car_dataset/car/test/images/road314_png.rf.697a2d138347ee6ee2da288b2ffe8e9d.jpg: 640x640 1 class5, 1 class8, 6.2m
image 541/638 /content/car_dataset/car/test/images/road320_png.rf.65f55afb9e0eb549cb01be72786c8b42.jpg: 640x640 1 class8, 6.2ms
image 542/638 /content/car_dataset/car/test/images/road321_png.rf.f9c13308da074b8ee78f43f6c7ea54f3.jpg: 640x640 1 class8, 9.2ms
image 543/638 /content/car_dataset/car/test/images/road322_png.rf.6028574359019d435c66689704d0418f.jpg: 640x640 1 class8, 6.9ms
image 544/638 /content/car_dataset/car/test/images/road323_png.rf.b843dd95b3d22917710ac75cca4a57b0.jpg: 640x640 1 class9, 1 class14, 6.9
image 545/638 /content/car_dataset/car/test/images/road339_png.rf.a176f5700f7182473dd4c3a268ebcbbd.jpg: 640x640 1 class14, 6.7ms
image 546/638 /content/car_dataset/car/test/images/road351_png.rf.917ea4e951348855c275b66bb4d890ad.jpg: 640x640 1 class8, 6.7ms
image 547/638 /content/car_dataset/car/test/images/road353_png.rf.deb497f4908212e3409c887fbb0544df.jpg: 640x640 1 class8, 6.3ms
image 548/638 /content/car_dataset/car/test/images/road355_png.rf.c43f3ac674a10af91051d8cb228747ad.jpg: 640x640 1 class8, 7.4ms
image 549/638 /content/car_dataset/car/test/images/road362_png.rf.765355e68b8097399c60024a6f28840c.jpg: 640x640 1 class8, 6.5ms
image 550/638 /content/car_dataset/car/test/images/road367_png.rf.68facda4326805592fce6d2bfb2dba6c.jpg: 640x640 1 class7, 1 class13, 6.4
image 551/638 /content/car_dataset/car/test/images/road388_png.rf.cc49d1d1343172eaa95da35bf070a7d3.jpg: 640x640 1 class13, 6.7ms
image 552/638 /content/car_dataset/car/test/images/road38_png.rf.8b6e15b041aab397eb10b38728fa1e15.jpg: 640x640 1 class1, 6.2ms
image 553/638 /content/car_dataset/car/test/images/road393_png.rf.4fe43a89433923b9841d1435d98c4aae.jpg: 640x640 1 class3, 12.1ms
image 554/638 /content/car_dataset/car/test/images/road397_png.rf.64b46468e2e241a3cb5dbce555894da3.jpg: 640x640 1 class3, 11.7ms
image 555/638 /content/car_dataset/car/test/images/road400_png.rf.03186a0125d7e8f9ccbad9322201c91a.jpg: 640x640 1 class3, 6.9ms
image 556/638 /content/car_dataset/car/test/images/road402_png.rf.8b57601c39d08578c3fba71d26f6dd1a.jpg: 640x640 1 class3, 7.0ms
image 557/638 /content/car_dataset/car/test/images/road412_png.rf.f3dc3c3dcb2d0a679c190b33c3a3178b.jpg: 640x640 1 class3, 7.8ms
image 558/638 /content/car_dataset/car/test/images/road420_png.rf.4db7dbd13a7b9c37ddddb7df9adf0834.jpg: 640x640 1 class13, 8.5ms
image 559/638 /content/car_dataset/car/test/images/road421_png.rf.cd6d5f5724ac207f49f64d2ecc32f9f6.jpg: 640x640 1 class13, 6.7ms
image 560/638 /content/car_dataset/car/test/images/road422_png.rf.9b9d58bbb154a5b030ff2addac608ef8.jpg: 640x640 1 class12, 6.3ms
image 561/638 /content/car_dataset/car/test/images/road431_png.rf.4cf451707a1af9d139094513881b7654.jpg: 640x640 1 class13, 6.1ms
image 562/638 /content/car_dataset/car/test/images/road439_png.rf.3a8f1ca30bf60d80f05733e8d83212d9.jpg: 640x640 1 class13, 6.0ms
image 563/638 /content/car_dataset/car/test/images/road43_png.rf.c576096fc5f0b00379321f94ddffcbc6.jpg: 640x640 6 class0s, 6.2ms
image 564/638 /content/car_dataset/car/test/images/road446_png.rf.f38afaa2d572c8426b05a88bdb4a5d3d.jpg: 640x640 1 class13, 6.2ms
image 565/638 /content/car_dataset/car/test/images/road450_png.rf.4066ba8e8ac659692b69cbcf144c575f.jpg: 640x640 1 class10, 6.2ms
image 566/638 /content/car_dataset/car/test/images/road453_png.rf.2ac6332c02e34b01f38bd0daf2dee199.jpg: 640x640 1 class10, 8.3ms
image 567/638 /content/car_dataset/car/test/images/road459_png.rf.90e8843192d53e6f83e48ef5a09736a3.jpg: 640x640 1 class1, 1 class12, 6.0
image 568/638 /content/car_dataset/car/test/images/road45_png.rf.de2883de8bdeb35347f7dfbd23b1916c.jpg: 640x640 (no detections), 7.5ms
image 569/638 /content/car_dataset/car/test/images/road466_png.rf.d57ae6325b53f1b11ebb16fbfefd48e0.jpg: 640x640 1 class0, 1 class12, 6.0
image 570/638 /content/car_dataset/car/test/images/road470_png.rf.7670afafae0b681bed4d7bd1ce2f1775.jpg: 640x640 1 class12, 6.3ms
image 571/638 /content/car_dataset/car/test/images/road485_png.rf.27b8f614cd9acbfc022c4c40b8707cbf.jpg: 640x640 1 class12, 8.1ms
image 572/638 /content/car_dataset/car/test/images/road487_png.rf.dc7045b0bb497d4a44959dc9f8466232.jpg: 640x640 1 class12, 6.1ms
image 573/638 /content/car_dataset/car/test/images/road488_png.rf.d96ab5854f2c6a2137b4005752cadeec.jpg: 640x640 1 class12, 6.0ms
image 574/638 /content/car_dataset/car/test/images/road490_png.rf.464f4426af72921b633d86c047b4eb7a.jpg: 640x640 1 class12, 6.0ms
image 575/638 /content/car_dataset/car/test/images/road491_png.rf.878b3a369b2f57b3edeb679ff3e03c23.jpg: 640x640 1 class12, 6.9ms
image 576/638 /content/car_dataset/car/test/images/road506_png.rf.7a13ac3369dd9c4a13997267c82b32a6.jpg: 640x640 1 class12, 6.2ms
image 577/638 /content/car_dataset/car/test/images/road519_png.rf.aa9e93bebe13bca1fd49aa4b08f47354.jpg: 640x640 1 class12, 1 class13, 6.
image 578/638 /content/car_dataset/car/test/images/road51_png.rf.049d6ceefd959818a9f59c81f8d1387a.jpg: 640x640 (no detections), 6.0ms
image 579/638 /content/car_dataset/car/test/images/road520_png.rf.937374420bcb13bddc7b27ad6128d860.jpg: 640x640 1 class12, 8.0ms
image 580/638 /content/car_dataset/car/test/images/road554_png.rf.534262cf45c8f7f6ad9e709bfa74bd81.jpg: 640x640 1 class8, 6.1ms
image 581/638 /content/car_dataset/car/test/images/road573_png.rf.692497fab5c04bfffa1d5f4b2b285213.jpg: 640x640 1 class8, 6.4ms
image 582/638 /content/car_dataset/car/test/images/road574_png.rf.e9300ee48cadd50aff3821e5d303d293.jpg: 640x640 1 class8, 6.5ms
image 583/638 /content/car_dataset/car/test/images/road576_png.rf.f46db39f9f64118df1377c6196897c25.jpg: 640x640 1 class8, 6.1ms
image 584/638 /content/car_dataset/car/test/images/road582_png.rf.930fbae49546d10b46e1f533c4e0a291.jpg: 640x640 1 class0, 2 class1s, 6.3
image 585/638 /content/car_dataset/car/test/images/road585_png.rf.9f55a59547b49a85d4b9012e0f24859c.jpg: 640x640 1 class12, 9.2ms
image 586/638 /content/car_dataset/car/test/images/road587_png.rf.a53ec0d32057731c117123acb2f3b4e1.jpg: 640x640 1 class12, 6.1ms
image 587/638 /content/car_dataset/car/test/images/road596_png.rf.11f43967e3d8f54a7c94b5810f527f23.jpg: 640x640 1 class12, 5.9ms
image 588/638 /content/car_dataset/car/test/images/road59_png.rf.f43173a745a9712aebbbc8f919e0fff6.jpg: 640x640 1 class14, 6.6ms
image 589/638 /content/car_dataset/car/test/images/road604_png.rf.78092f95d0c775d7e2b266dee85e6c5d.jpg: 640x640 1 class12, 6.1ms
image 590/638 /content/car_dataset/car/test/images/road613_png.rf.560c13a685d02ca31a65b6b2b172772a.jpg: 640x640 (no detections), 7.1ms
image 591/638 /content/car_dataset/car/test/images/road619_png.rf.afe775af026ad763791eed33be445f64.jpg: 640x640 1 class8, 6.2ms
image 592/638 /content/car_dataset/car/test/images/road629_png.rf.79a049eae4f765c580544c533220d5f5.jpg: 640x640 1 class9, 6.5ms
image 593/638 /content/car_dataset/car/test/images/road62_png.rf.b3288434b3cf3e2b6d2f057c1f5a0502.jpg: 640x640 1 class14, 6.2ms
image 594/638 /content/car_dataset/car/test/images/road633_png.rf.282d128c502ba602126742a5064cd054.jpg: 640x640 1 class14, 6.1ms
image 595/638 /content/car_dataset/car/test/images/road639_png.rf.74306332af06fee257b44f5dd9ae02fb.jpg: 640x640 1 class3, 1 class11, 6.3
image 596/638 /content/car_dataset/car/test/images/road640_png.rf.d095777d03f03f86a0b6b1f5b32dfdd5.jpg: 640x640 1 class3, 1 class8, 1 cl
image 597/638 /content/car_dataset/car/test/images/road64_png.rf.bf315b45291d211d2e01926d50ddfddf.jpg: 640x640 1 class14, 6.7ms
image 598/638 /content/car_dataset/car/test/images/road650_png.rf.b4086d2eec2b5bfe4465ae484a3b762f.jpg: 640x640 1 class9, 5.8ms
image 599/638 /content/car_dataset/car/test/images/road652_png.rf.e82e3cfaed8d2378962fd6c1380f5d02.jpg: 640x640 1 class3, 6.1ms
image 600/638 /content/car_dataset/car/test/images/road653_png.rf.a69e562a3dab509d536c1f6d7689c4de.jpg: 640x640 2 class8s, 6.1ms
image 601/638 /content/car_dataset/car/test/images/road654_png.rf.dccac1ff09c216df1ecd3c90e1d3923b.jpg: 640x640 2 class8s, 6.0ms
image 602/638 /content/car_dataset/car/test/images/road655_png.rf.ff15054285f0f0f70cd01a48ab24b450.jpg: 640x640 2 class8s, 1 class12, 7.
image 603/638 /content/car_dataset/car/test/images/road65_png.rf.35a17912d8a5df128c238eff97db75eb.jpg: 640x640 1 class14, 6.3ms
image 604/638 /content/car_dataset/car/test/images/road661_png.rf.9b3a953ded6512ace828850f97be4472.jpg: 640x640 1 class8, 6.5ms
image 605/638 /content/car_dataset/car/test/images/road666_png.rf.7d914d9d62e7d0fcc23d5bce18bb5d3c.jpg: 640x640 1 class10, 6.3ms
image 606/638 /content/car_dataset/car/test/images/road673_png.rf.4180f0a6afa7416d52c729d8b9305d75.jpg: 640x640 1 class14, 6.4ms
image 607/638 /content/car_dataset/car/test/images/road691_png.rf.3ca3931933e5623e51596577124c3447.jpg: 640x640 1 class3, 1 class5, 6.3m
image 608/638 /content/car_dataset/car/test/images/road692_png.rf.428a4cfb2f374f0acb60ffc5c57e0750.jpg: 640x640 1 class3, 1 class5, 6.7m
image 609/638 /content/car_dataset/car/test/images/road701_png.rf.cdb390be5b29eb46e8125d30c802cde0.jpg: 640x640 1 class3, 1 class5, 7.0m
image 610/638 /content/car_dataset/car/test/images/road708_png.rf.d9bf7da6e0e73af3295909a18a2cabee.jpg: 640x640 1 class3, 1 class5, 6.1m
image 611/638 /content/car_dataset/car/test/images/road709_png.rf.4247ab0b03caf6c144ac9d5203850b81.jpg: 640x640 1 class3, 1 class5, 6.2m
image 612/638 /content/car_dataset/car/test/images/road716_png.rf.b9085b6c292ddd763a6e1481465dd3cd.jpg: 640x640 2 class3s, 1 class12, 6.
image 613/638 /content/car_dataset/car/test/images/road720_png.rf.bdc71460f5121259128f84a720780e9e.jpg: 640x640 1 class8, 1 class10, 6.3
image 614/638 /content/car_dataset/car/test/images/road743_png.rf.09b77e4a47a21b7c3482c28b37c86ecb.jpg: 640x640 1 class3, 1 class5, 6.9m
image 615/638 /content/car_dataset/car/test/images/road747_png.rf.66031af78137deac5c6878292d9ce7db.jpg: 640x640 1 class3, 1 class5, 6.9m
image 616/638 /content/car_dataset/car/test/images/road760_png.rf.cb25cb1c5c71e6bbf07a9dc3b11915d2.jpg: 640x640 1 class3, 7.7ms
image 617/638 /content/car_dataset/car/test/images/road770_png.rf.192c2e4a2994c3c1bdfa14f69586392c.jpg: 640x640 1 class1, 1 class12, 1 c
image 618/638 /content/car_dataset/car/test/images/road773_png.rf.b467561d9bc1abfaa69c770187f44c38.jpg: 640x640 1 class12, 9.6ms
image 619/638 /content/car_dataset/car/test/images/road774_png.rf.01952886ddecdd8280c5df688e7f3f0e.jpg: 640x640 (no detections), 15.2ms
image 620/638 /content/car_dataset/car/test/images/road794_png.rf.3569e6160a38765029861dd0a34ebfdd.jpg: 640x640 1 class13, 7.8ms
https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 16/19
5/22/25, 10:17 PM image Processing 3 - Colab
image 621/638 /content/car_dataset/car/test/images/road803_png.rf.ec1de6db189d7a63e98b91529236a7d1.jpg: 640x640 1 class3, 6.6ms
image 622/638 /content/car_dataset/car/test/images/road804_png.rf.c07a9b7b679e771fad740f296ee3a8f7.jpg: 640x640 1 class3, 6.8ms
image 623/638 /content/car_dataset/car/test/images/road811_png.rf.e8ae9aefbf35658164ee88a52ed39bd8.jpg: 640x640 1 class13, 6.4ms
image 624/638 /content/car_dataset/car/test/images/road822_png.rf.b3ba7f5457042a083bad8aac3fe5f819.jpg: 640x640 1 class0, 1 class1, 1 cl
image 625/638 /content/car_dataset/car/test/images/road836_png.rf.05522d5697471753f15583e9c74d9908.jpg: 640x640 1 class12, 6.3ms
image 626/638 /content/car_dataset/car/test/images/road841_png.rf.7ddce1ff42545b101a28f63e6a1e5af7.jpg: 640x640 1 class12, 1 class13, 7.
image 627/638 /content/car_dataset/car/test/images/road842_png.rf.eaef28c5ea8a82fe3cfba78634bebf18.jpg: 640x640 1 class12, 6.6ms
image 628/638 /content/car_dataset/car/test/images/road84_png.rf.3c4a24183c70e3af08fc01ae65761c88.jpg: 640x640 1 class14, 6.1ms
image 629/638 /content/car_dataset/car/test/images/road850_png.rf.74fa86d3aaa9d85f0c87bf9bb5ce433e.jpg: 640x640 1 class12, 6.1ms
image 630/638 /content/car_dataset/car/test/images/road851_png.rf.30b2392d540fed2bdafd783c8defefb9.jpg: 640x640 1 class12, 6.1ms
image 631/638 /content/car_dataset/car/test/images/road856_png.rf.c56e130a9afef0aa81d3359ce467f4b1.jpg: 640x640 1 class12, 6.2ms
image 632/638 /content/car_dataset/car/test/images/road859_png.rf.f0e4bbd39d237c79817d4ade35676364.jpg: 640x640 1 class10, 1 class13, 6.
image 633/638 /content/car_dataset/car/test/images/road85_png.rf.86fab2367257fba23a95cad102a7dd82.jpg: 640x640 1 class14, 6.0ms
image 634/638 /content/car_dataset/car/test/images/road865_png.rf.d20a4a3f4030905929c2739f03eba9b8.jpg: 640x640 1 class13, 8.9ms
image 635/638 /content/car_dataset/car/test/images/road86_png.rf.618acea5b57f584ad3cf0c2ddd8be8b3.jpg: 640x640 1 class14, 6.5ms
image 636/638 /content/car_dataset/car/test/images/road872_png.rf.d95002240567bc0114365d0ba181071f.jpg: 640x640 1 class8, 1 class14, 6.4
image 637/638 /content/car_dataset/car/test/images/road876_png.rf.723330bcac2a5e357c0a325590dbff3c.jpg: 640x640 1 class3, 1 class8, 6.3m
image 638/638 /content/car_dataset/car/test/images/road94_png.rf.77920ec51ee4fe99a6fb6f8d99f67954.jpg: 640x640 1 class14, 6.9ms
Speed: 3.5ms preprocess, 7.3ms inference, 1.4ms postprocess per image at shape (1, 3, 640, 640)
Results saved to runs/detect/predict

Ultralytics 8.3.142 🚀 Python-3.11.12 torch-2.6.0+cu124 CPU (Intel Xeon 2.00GHz)

💡 ProTip: Export to OpenVINO format for best performance on Intel CPUs. Learn more at https://docs.ultralytics.com/integrations/openvi
PyTorch: starting from 'runs/detect/train/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 19, 8400) (6.0
requirements: Ultralytics requirements ['onnx>=1.12.0,<1.18.0', 'onnxslim>=0.1.53', 'onnxruntime-gpu'] not found, attempting AutoUpdate.

requirements: AutoUpdate success ✅ 5.1s

WARNING ⚠️ requirements: Restart runtime or rerun command for updates to take effect

ONNX: starting export with onnx 1.17.0 opset 19...

ONNX: slimming with onnxslim 0.1.53...
ONNX: export success ✅ 6.9s, saved as 'runs/detect/train/weights/best.onnx' (11.7 MB)

Export complete (7.2s)

Results saved to /content/runs/detect/train/weights
Predict: yolo predict task=detect model=runs/detect/train/weights/best.onnx imgsz=640
Validate: yolo val task=detect model=runs/detect/train/weights/best.onnx imgsz=640 data=/content/data.yaml
Visualize: https://netron.app
'runs/detect/train/weights/best.onnx'
 

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 17/19
5/22/25, 10:17 PM image Processing 3 - Colab

for result in results:

boxes = result.boxes # Bounding boxes
for box in boxes:
print(f"Class: {box.cls}, Confidence: {box.conf}, Coordinates: {box.xyxy}")

Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.5871], device='cuda:0'), Coordinates: tensor([[ 44.3708, 207.4824, 69.44 
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.9398], device='cuda:0'), Coordinates: tensor([[109.6046, 223.0486, 137.43
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.8988], device='cuda:0'), Coordinates: tensor([[249.3648, 204.7752, 288.47
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.4467], device='cuda:0'), Coordinates: tensor([[109.7953, 222.8124, 136.3
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9825], device='cuda:0'), Coordinates: tensor([[122.1660, 126.9445, 310.0
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.9387], device='cuda:0'), Coordinates: tensor([[163.4044, 226.2785, 265.53
Class: tensor([10.], device='cuda:0'), Confidence: tensor([0.9710], device='cuda:0'), Coordinates: tensor([[156.2976, 229.2353, 304.3
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.6820], device='cuda:0'), Coordinates: tensor([[125.7298, 176.6149, 152.5
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9784], device='cuda:0'), Coordinates: tensor([[176.0188, 121.5685, 242.02
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9503], device='cuda:0'), Coordinates: tensor([[174.0316, 202.8824, 240.49
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9860], device='cuda:0'), Coordinates: tensor([[133.9438, 215.3849, 228.64
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9621], device='cuda:0'), Coordinates: tensor([[138.1336, 103.2205, 231.51
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9877], device='cuda:0'), Coordinates: tensor([[327.9370, 242.8295, 416.00
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9412], device='cuda:0'), Coordinates: tensor([[325.9185, 120.2000, 415.66
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9860], device='cuda:0'), Coordinates: tensor([[171.2834, 266.5224, 267.49
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9633], device='cuda:0'), Coordinates: tensor([[168.9428, 145.1982, 266.49
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9383], device='cuda:0'), Coordinates: tensor([[186.1794, 235.0927, 217.47
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9172], device='cuda:0'), Coordinates: tensor([[188.5796, 197.7482, 219.64
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.6347], device='cuda:0'), Coordinates: tensor([[209.4017, 219.8660, 234.91
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.4071], device='cuda:0'), Coordinates: tensor([[244.6829, 219.7334, 271.55
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.3065], device='cuda:0'), Coordinates: tensor([[210.0546, 220.1864, 233.9
Class: tensor([10.], device='cuda:0'), Confidence: tensor([0.7227], device='cuda:0'), Coordinates: tensor([[139.4350, 201.7313, 177.1
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.3153], device='cuda:0'), Coordinates: tensor([[139.3121, 201.8066, 176.94
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9836], device='cuda:0'), Coordinates: tensor([[ 93.6751, 143.1699, 130.79
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9700], device='cuda:0'), Coordinates: tensor([[ 89.2341, 188.2773, 126.62
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9922], device='cuda:0'), Coordinates: tensor([[317.4345, 223.3471, 414.98
Class: tensor([5.], device='cuda:0'), Confidence: tensor([0.9621], device='cuda:0'), Coordinates: tensor([[327.1533, 104.7614, 415.49
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9807], device='cuda:0'), Coordinates: tensor([[159.9016, 114.8357, 244.29
Class: tensor([1.], device='cuda:0'), Confidence: tensor([0.3120], device='cuda:0'), Coordinates: tensor([[287.6501, 185.0264, 294.80
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.2817], device='cuda:0'), Coordinates: tensor([[194.1718, 170.9883, 237.2
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.2618], device='cuda:0'), Coordinates: tensor([[194.2102, 169.8029, 236.3
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.3305], device='cuda:0'), Coordinates: tensor([[257.4434, 195.8129, 281.9
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.9867], device='cuda:0'), Coordinates: tensor([[143.3032, 221.2311, 226.3
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9759], device='cuda:0'), Coordinates: tensor([[200.0543, 228.3208, 240.18
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.9717], device='cuda:0'), Coordinates: tensor([[246.8862, 184.1744, 297.52
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.9901], device='cuda:0'), Coordinates: tensor([[152.4664, 186.9402, 225.2
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9640], device='cuda:0'), Coordinates: tensor([[148.9277, 80.0936, 231.1
Class: tensor([0.], device='cuda:0'), Confidence: tensor([0.9059], device='cuda:0'), Coordinates: tensor([[136.1651, 214.7709, 171.66
Class: tensor([1.], device='cuda:0'), Confidence: tensor([0.5128], device='cuda:0'), Coordinates: tensor([[173.3856, 174.9509, 227.50
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.9456], device='cuda:0'), Coordinates: tensor([[202.5854, 130.2173, 284.2
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.8394], device='cuda:0'), Coordinates: tensor([[151.2213, 148.4740, 206.0
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.3397], device='cuda:0'), Coordinates: tensor([[151.1173, 148.3867, 206.0
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.9336], device='cuda:0'), Coordinates: tensor([[104.0651, 158.2453, 173.5
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9768], device='cuda:0'), Coordinates: tensor([[153.5454, 88.3868, 260.0
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.4472], device='cuda:0'), Coordinates: tensor([[110.8402, 128.5355, 136.4
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.3934], device='cuda:0'), Coordinates: tensor([[157.7995, 122.1361, 186.7
Class: tensor([12.], device='cuda:0'), Confidence: tensor([0.9255], device='cuda:0'), Coordinates: tensor([[180.4070, 72.7234, 241.7
Class: tensor([10.], device='cuda:0'), Confidence: tensor([0.3205], device='cuda:0'), Coordinates: tensor([[178.8064, 156.9307, 212.3
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.2535], device='cuda:0'), Coordinates: tensor([[178.6811, 156.6211, 212.3
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9759], device='cuda:0'), Coordinates: tensor([[117.8159, 151.1938, 315.3
Class: tensor([13.], device='cuda:0'), Confidence: tensor([0.5522], device='cuda:0'), Coordinates: tensor([[218.9159, 89.1186, 264.1
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9831], device='cuda:0'), Coordinates: tensor([[152.9597, 129.0453, 263.0
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.9876], device='cuda:0'), Coordinates: tensor([[117.9570, 211.9091, 162.25
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.2910], device='cuda:0'), Coordinates: tensor([[308.7433, 272.6884, 325.6
Class: tensor([8.], device='cuda:0'), Confidence: tensor([0.9781], device='cuda:0'), Coordinates: tensor([[151.8591, 116.0396, 245.91
Class: tensor([3.], device='cuda:0'), Confidence: tensor([0.2655], device='cuda:0'), Coordinates: tensor([[115.2300, 308.1103, 135.43
Class: tensor([14.], device='cuda:0'), Confidence: tensor([0.9779], device='cuda:0'), Coordinates: tensor([[105.8727, 94.4180, 318.7

 

metrics = model.val(data='/content/data.yaml')
print(metrics)

0 31231 0 31331 0 31431 0 31532 0 31632 0 31732 0 31832 0 31932 0 32032 0 321
https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 18/19
5/22/25, 10:17 PM image Processing 3 - Colab
0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.321
0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.345 
0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.369
0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.393
0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.417
0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.441
0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.465
0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.489
0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.513
0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.537
0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.561
0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.585
0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.609
0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.633
0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.657
0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.681
0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.705
0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.729
0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.753
0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.777
0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.80
0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.825
0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.849
0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.873
0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.89
0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.921
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.945
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.969
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.993
[ 0.92593, 0.92593, 0.90741, ..., 0, 0, 0],
[ 1, 1, 1, ..., 0, 0, 0],
...,
[ 1, 1, 1, ..., 0, 0, 0],
[ 1, 1, 1, ..., 0, 0, 0],
[ 1, 1, 1, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: np.float64(0.799405115099259)
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.41882, 0.46001, 0.78647, 0.85635, 0.81591, 0.88295, 0.84795, 0.89489, 0.84976,
names: {0: 'class0', 1: 'class1', 2: 'class2', 3: 'class3', 4: 'class4', 5: 'class5', 6: 'class6', 7: 'class7', 8: 'class8', 9: 'clas
plot: True
results_dict: {'metrics/precision(B)': np.float64(0.9237832867920784), 'metrics/recall(B)': np.float64(0.8270031328037614), 'metrics/
save_dir: PosixPath('runs/detect/val2')
speed: {'preprocess': 0.614373873908773, 'inference': 4.019885352059684, 'loss': 0.0008939750296803198, 'postprocess': 1.322913962546
task: 'detect'

results = model.predict(source='/content/car_dataset/video.mp4', save=True, conf=0.25)

video 1/1 (frame 434/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.2ms

video 1/1 (frame 435/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.2ms 
video 1/1 (frame 436/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.1ms
video 1/1 (frame 437/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.1ms
video 1/1 (frame 438/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.1ms
video 1/1 (frame 439/508) /content/car_dataset/video.mp4: 640x640 1 class1, 7.4ms
video 1/1 (frame 440/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.6ms
video 1/1 (frame 441/508) /content/car_dataset/video.mp4: 640x640 1 class1, 5.9ms
video 1/1 (frame 442/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.3ms

video 1/1 (frame 443/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.2ms

video 1/1 (frame 444/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.1ms 
video 1/1 (frame 445/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.0ms
video 1/1 (frame 446/508) /content/car_dataset/video.mp4: 640x640 1 class1, 8.0ms
video 1/1 (frame 447/508) /content/car_dataset/video.mp4: 640x640 1 class1, 7.4ms
video 1/1 (frame 448/508) /content/car_dataset/video.mp4: 640x640 1 class0, 1 class1, 6.2ms
video 1/1 (frame 449/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.0ms
video 1/1 (frame 450/508) /content/car_dataset/video.mp4: 640x640 (no detections), 6.6ms
video 1/1 (frame 451/508) /content/car_dataset/video.mp4: 640x640 (no detections), 8.5ms
video 1/1 (frame 452/508) /content/car_dataset/video.mp4: 640x640 (no detections), 6.2ms
video 1/1 (frame 453/508) /content/car_dataset/video.mp4: 640x640 1 class1, 6.0ms
video 1/1 (frame 454/508) /content/car_dataset/video.mp4: 640x640 2 class1s, 7.6ms
video 1/1 (frame 455/508) /content/car dataset/video mp4: 640x640 2 class1s 5 9ms

https://colab.research.google.com/drive/16HXbhycfx2MpUvT8yGfoXBHGIk4xiQhX#scrollTo=LQ6kyH2lBdD7&printMode=true 19/19