Sliding-window detectors A brute force approach for object detection is to slide windows from left and right, and from up to down to identify objects using classification. To detect different object types at various viewing distances, we use windows of varied sizes and aspect ratios.
2021-02-20
What is a Feature Descriptor A feature descriptor is a representation of an image or an image patch that simplifies the image by extracting useful information and throwing away extraneous information.
2021-02-20
Training YOLOv3 as well as YOLOv3 tiny on custom dataset is similar to training YOLOv4 and YOLOv4 tiny. Only some steps need to be adjusted for YOLOv3 and YOLOv3 tiny:
2021-01-05
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao (more commonly known by their GitHub monikers, WongKinYiu and AlexyAB) have propelled the YOLOv4 model forward by efficiently scaling the network’s design and scale, surpassing the previous state-of-the-art EfficientDet published earlier this year by the Google Research/Brain team.
2021-01-05
We will learn training YOLOv5 on our custom dataset visualizing training logs using trained YOLOv5 for inference exporting trained YOLOv5 from PyTorch to other formats. Clone YOLOv5 and install dependencies git clone https://github.
2020-12-25
Model zoo YOLOv4 model zoo Pretrained models Proper configuration based on GPU We do NOT suggest you train the model with subdivisions equal or larger than 32, it will takes very long training time.
2020-12-19
Bounding box formats comparison and conversion In COCO Json, the format of bounding box is: "bbox": [ <absolute_x_top_left>, <absolute_y_top_left>, <absolute_width>, <absolute_height> ] However, the annotation is different in YOLO. For each .
2020-12-02
The COCO dataset is formatted in JSON and is a collection of “info”, “licenses”, “images”, “annotations”, “categories” (in most cases), and “segment info” (in one case). { "info": {...}, "licenses": [.
2020-12-02
Precision & Recall Confusion matrix: Precision: measures how accurate is your predictions. i.e. the percentage of your predictions are correct. $$ \text{precision} = \frac{TP}{TP + FP} $$ Recall: measures how good you find all the positives.
2020-11-12
2020-11-12