object_detection
Object Detection
Machine learning approaches:
- Viola–Jones object detection framework based on Haar features
- Scale-invariant feature transform (SIFT)
- Histogram of oriented gradients (HOG) features
Deep learning approaches:
- Region-based Convolutinoal Neural Network (R-CNN) 2014 Ross Girshick, et al, UC Berkeley
- Single Shot MultiBox Detector (SSD)
- You Only Look Once (YOLO) 2015 Joseph Redmond et al
- Single-Shot Refinement Neural Network for Object Detection (RefineDet)
- Retina-Net
- Deformable convolutional networks
Pre-trained models
- COCO SSD MobileNet v1 model
Training from scratch
- collect images
- label images
- run training
- test effectiveness
Transfer Training
- start with a pre-trained model
- add your own images and labels
Python libraries with built-in object detection algorithms
- OpenCV
- TensorFlow
What is a model?
- the output of a training session?
- Data structure of a “model” or “network”?
Inference Graph?
Pose Estimation
object_detection.txt · Last modified: 2024/02/14 00:45 by jhagstrand