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🚀 ⭐ The list of the most popular YOLO algorithms - awesome YOLO

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awesome-yolo 🚀 ⭐

Object Detection DNN Algorithms

Most of the DNN object detection algorithm can:

  • classifies object
  • localize object (find the coordinates of the bounding box enclosing the object)

YOLO - You Only Looks Once

Object Detection DNN Algorithms Benchmark

Key parameters of object detection algorithms

  • Accuracy (A) = (Number of Correct Predictions) / (Total Number of Predictions)
    • Accuracy measures the overall correctness of the algorithm's predictions.
  • Precision (P) = (True Positives) / (True Positives + False Positives)
    • It quantifies the algorithm's ability not to label false positives. It measures the fraction of correctly predicted positive instances among all predicted positive instances.
  • Recall (R) = (True Positives) / (True Positives + False Negatives)
    • It quantifies the algorithm's ability to find all positive instances.
  • F1 Score (F1) = 2 * (Precision * Recall) / (Precision + Recall)
    • F1 score providing a balanced measure of the algorithm's performance, it is the harmonic mean of precision and recall.
  • Average Precision (AP) = ∑(P_i) * ΔR_i
    • P_i is the precision value at the i-th recall point.
    • ΔR_i is the change in recall from the i-th to the (i+1)-th recall point
    • AP summarizes the performance of an algorithm across different confidence thresholds
    • It quantifies the precision-recall trade-off for a given class
  • Mean Average Precision (mAP) = (AP_1 + AP_2 + AP_3 + ... + AP_n) / n
    • AP_1, AP_2, ..., AP_n are the Average Precision values for each class
    • n is the total number of classes
  • Intersection over Union (IoU) = (Area of Intersection) / (Area of Union)
    • It is used to determine the accuracy of localization, it measures the overlap between predicted bounding boxes and ground truth bounding boxes
  • Inference Time - time taken to make predictions on a single input image
    • It measures the time it takes for the algorithm to process the input and produce the output (bounding boxes, class predictions) without considering any external factors.
  • Processing Speed (FPS) - time taken by the algorithm to process a given dataset or a single image.
    • Often represented as FPS - Frames Per Second, number of frames (or images) that the algorithm can process per second
    • It takes into account factors such as data loading, pre-processing, and post-processing steps in addition to the inference time.
  • Number of Parameters
    • The number of model parameters indicates the model's complexity and memory requirements.
  • Memory Usage
    • It measures the amount of memory consumed by the algorithm during inference.

Tests and comparisons of models

Yolo v6 vs Yolo v8 Yolo v8 Yolo v7 YoloR vs YoloX Yolo_v5 vs YoloX YoloX

Practical application examples

Detecting pumpkins from drone video Counting Tree Logs by Size Pipe Counting

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