Object Detection

Precision & Recall Confusion matrix: Precision: measures how accurate is your predictions. i.e. the percentage of your predictions are correct. $$ \text{precision} = \frac{TP}{TP + FP} $$

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": [.

The problem of sliding windows method is that it does not output the most accuracte bounding boxes. A good way to get this output more accurate bounding boxes is with the YOLO (You Only Look Once) algorithm.

Here we will learn how to get YOLOv4 Object Detection running in the Cloud with Google Colab step by step. Check out the Google Colab Notebook Clone and build DarkNet Clone darknet from AlexeyAB’s repository,

Clone and build Darknet Clone darknet repo git clone https://github.com/AlexeyAB/darknet Change makefile to have GPU and OPENCV enabled cd darknet sed -i 's/OPENCV=0/OPENCV=1/' Makefile sed -i 's/GPU=0/GPU=1/' Makefile sed -i 's/CUDNN=0/CUDNN=1/' Makefile sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile Verify CUDA

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 .

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.

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.

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.