filmov
tv
Object Detection of a Diverse Object Data Set
Показать описание
Part of the ECE 542 Virtual Symposium (Spring 2020)
Computer vision becomes more advanced as we can extract more meaningful information from images in real time. This can be applied to a variety of applications, such as autonomous robotics. The purpose of our project is to train a Neural Network for object detection that is capable of real-time, accurate predictions. The end goal is to create bounding boxes and label them according to their classification.
Utilizing the open images data set to train our model, we were able to get an abundant amount of images for our neural network. Since our training model contained over 100 classes, we would be able to identify these diverse objects in any image and relay the information to different use cases such as autonomous robotics.
Computer vision becomes more advanced as we can extract more meaningful information from images in real time. This can be applied to a variety of applications, such as autonomous robotics. The purpose of our project is to train a Neural Network for object detection that is capable of real-time, accurate predictions. The end goal is to create bounding boxes and label them according to their classification.
Utilizing the open images data set to train our model, we were able to get an abundant amount of images for our neural network. Since our training model contained over 100 classes, we would be able to identify these diverse objects in any image and relay the information to different use cases such as autonomous robotics.
0:08:34
Object Detection of a Diverse Object Data Set
0:02:32
Image classification vs Object detection vs Image Segmentation | Deep Learning Tutorial 28
0:00:14
YOLO11, Faster R-CNN and DETR Object Detection | Comparison
0:12:13
Top Object Detection Models in 2023 | Model Selection Guide sponsored by Intel
0:00:31
YOLOv8 Comparison with Latest YOLO models
0:05:39
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Label
0:05:04
Image Segmentation, Semantic Segmentation, Instance Segmentation, and Panoptic Segmentation
0:16:05
What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
0:04:58
Sliding Window Object Detection | Deep Learning Tutorial 30 (Tensorflow, Keras & Python)
0:16:23
Introduction to Object Detection in Deep Learning
0:21:26
How to Train YOLO Object Detection Models in Google Colab (YOLO11, YOLOv8, YOLOv5)
4:33:47
Object Detection 101 Course - Including 4xProjects | Computer Vision
0:08:06
Different Algorithms For Object Detection in Computer Vision | Object Detection Techniques
0:02:57
Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
0:00:27
Object Detection AI: COCO Dataset Explained
0:13:46
How to Capture and Label Training Data to Improve Object Detection Model Accuracy
0:01:00
Image Classification VS Object Detection
0:12:23
Captioning Images With Diverse Objects
0:00:35
Automatic detection of diverse ground objects with UgCS
1:12:32
Lecture 15: Object Detection
0:05:38
YOLO Object Detection Explained | What Is It? | STT
1:11:49
YOLOv11: How to Train for Object Detection on a Custom Dataset | Step-by-step guide
0:00:57
Object Detection using OpenImages Pretrained Model #computervision #objectdetection
0:05:36
YOLOv10: A Leap Away from NMS - Advanced Object Detection Explained
Комментарии