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Vehicle and Pedestrian Detection Using YOLOv8 and Kitti dataset 

Code With Aarohi
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YOLOv8, a state-of-the-art object detection algorithm, is leveraged for vehicle and pedestrian detection in real-time scenarios. Trained on the KITTI dataset, it accurately identifies vehicles and pedestrians, ensuring road safety and enhancing autonomous driving systems.
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For queries: You can comment in comment section or you can email me at aarohisingla1987@gmail.com
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Utilizing YOLOv8 with the KITTI dataset enables precise localization and classification of vehicles and pedestrians, even in challenging environments such as varying lighting conditions and occlusions. This robust detection system serves as a crucial component in enhancing road safety measures and advancing the development of autonomous vehicles by providing reliable perception capabilities.
#yolov8 #computervision #objectdetection #pedestrian #kittidataset

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5 окт 2024

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Комментарии : 36   
@plutogaming8981
@plutogaming8981 4 месяца назад
Can you make a project , where we can find the distance of each car or pedistrian from the view of camera using open cv
@CodeWithAarohi
@CodeWithAarohi 4 месяца назад
Noted!
@deepakthirukkumaran9636
@deepakthirukkumaran9636 2 месяца назад
hi ma'am, can in get the colab link for the code
@rishisharma5827
@rishisharma5827 7 месяцев назад
I love your work. Thank you so much
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Glad you enjoy it!
@pifordtechnologiespvtltd5698
@pifordtechnologiespvtltd5698 7 месяцев назад
Simply wonderful again. Keep Rocking
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Thank you!
@CC-yy1wg
@CC-yy1wg 7 месяцев назад
Hello Teacher nice presentation . Am a student and also doing a small project related to tracking and detection of Multiple of objects ( Av related ) but I have been having difficulties . With some data sets and results . Is there any way I would reach you to talk more ? I will be glad on your help
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
You can email me at aarohisingla1987@gmail.com
@sanketpatil6599
@sanketpatil6599 7 месяцев назад
Hello Mam I am Mtech students NIT Surat.my dissertation work is in vehicle detection and tracking for Indian conditions using UAV I have done vehicle detection using Yolov8 with custom dataset of 4lak +annotations of vehicles now I am working on Tracking but couldn't able to process regarding that I want to discuss with can you help to resolve the issue
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Sure, Mail me at aarohisingla1987@gmail.com
@sanketpatil6599
@sanketpatil6599 7 месяцев назад
​@@CodeWithAarohi mam I have sent you the mail
@NanoGi-lt5fc
@NanoGi-lt5fc 7 месяцев назад
Mam plzz make end to end projects for our placement plzz with Amazon ec2 for deployment
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Sure!
@solomondargie5028
@solomondargie5028 6 месяцев назад
Could you make video for detectron2 using kitti dataset please?
@CodeWithAarohi
@CodeWithAarohi 6 месяцев назад
I will try!
@solomondargie5028
@solomondargie5028 6 месяцев назад
@@CodeWithAarohi Thanks madam. and if you can do it in google colab if not it's ok using Jupiter notebook.🙏
@naveenkumarjadi2915
@naveenkumarjadi2915 7 месяцев назад
Hi mam can you tell me how did you converted the label files , i can filers the labels but how co-ordinates converted.
@MahfuzurRahman-swe
@MahfuzurRahman-swe 7 месяцев назад
same question please help
@g.ananthanarayanan6993
@g.ananthanarayanan6993 24 дня назад
@@MahfuzurRahman-swe did you do it? If yes how did you do it?
@automotor_in
@automotor_in 7 месяцев назад
lots of information , thank u
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Glad it was helpful!
@chicatechhub
@chicatechhub 3 месяца назад
how you converted the labels from string to float like pedestrain to 0
@Abdelkader-hq7ds
@Abdelkader-hq7ds 2 месяца назад
It is done in through the yaml file
@g.ananthanarayanan6993
@g.ananthanarayanan6993 24 дня назад
@@Abdelkader-hq7ds How do we do it? Can you explain it it detail.
@sreedeviprasad8143
@sreedeviprasad8143 6 месяцев назад
Which is best pedestrain detection dataset?
@CodeWithAarohi
@CodeWithAarohi 6 месяцев назад
The "best" pedestrian detection dataset depends on your specific requirements. Some widely used datasets include COCO, Caltech Pedestrian, KITTI, INRIA Person, CityPersons, Penn-Fudan, and UA-DETRAC. Each has its own strengths and focuses, so choose based on your project's needs.
@arnavthakur5409
@arnavthakur5409 6 месяцев назад
Nice video
@CodeWithAarohi
@CodeWithAarohi 6 месяцев назад
Thanks
@ashimasingla103
@ashimasingla103 7 месяцев назад
Nice video.
@CodeWithAarohi
@CodeWithAarohi 7 месяцев назад
Thanks!
@afridirahman3906
@afridirahman3906 5 месяцев назад
Can you provide the github link of this ipnyb file please.
@CodeWithAarohi
@CodeWithAarohi 5 месяцев назад
I don't have this code on github. You can just prepare the data.yaml file as per your dataset and execute below mentioned commands to train and test model. Train: from ultralytics import YOLO # Load a model model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training) # Train the model with 2 GPUs results = model.train(data='coco8.yaml', epochs=100, imgsz=640) Predict: from ultralytics import YOLO # Load a pretrained YOLOv8n model model = YOLO('best.pt') # path of trained model # Define path to the image file source = 'path/to/image.jpg' # Run inference on the source results = model(source) # list of Results objects
@skp6914
@skp6914 4 месяца назад
can you share the code?
@kstwok
@kstwok 6 месяцев назад
Is it possible to get source code for this?
@CodeWithAarohi
@CodeWithAarohi 6 месяцев назад
Code is available for channel members (Contribution level 2)
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