Yolo
- 1. You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi (2016)
- 2. The YOLO Detection System
- 3. The YOLO Detection System (1) resizes the input image to 448 × 448. (2) runs a single convolutional network on the image. (3) thresholds the resulting detections by the model’s confidence.
- 5. Bounding Box, Confidence and Class Probability YOLO reframes object detection as a regression problem. • The image is divided into an S × S grid and for each grid cell predicts B bounding boxes (x, y, w, h), confidence for those boxes, and C class probabilities. • These predictions are encoded as an S × S × (B ∗ 5 + C) tensor.
- 6. Bounding Box, Confidence and Class Probability The confidence of the bounding box Formally we define confidence as Pr(Object) ∗ IOU . If no object exists in that cell, the confidence scores should be zero.
- 7. The Neural Network Architecture For evaluating YOLO on PASCAL VOC, we use S = 7, B = 2. PASCAL VOC has 20 labelled classes so C = 20. Our final prediction is a 7 × 7 × (2∗5 + 20) tensor.
- 8. Loss Function The size of the bounding box The confidence of the bounding box The probability of the class
- 10. Confusion Matrix
- 11. Intersection Over Union (IOU) and Object Detection https://devblogs.nvidia.com/exploring-spacenet-dataset-using-digits/
- 12. Recall-Precision Curve and Average Precision https://acutecaretesting.org/en/articles/precision- recall-curves-what-are-they-and-how-are-they-used Ideally, the value of the Precision does not decrease as the increase of the value of Recall. The general definition for the Average Precision (AP) is finding the area under the precision-recall curve.
- 13. https://medium.com/@jonathan_hui/ma p-mean-average-precision-for-object- detection-45c121a31173 The dataset contains 5 apples only. We collect all the predictions made for apples in all the images and rank it in descending order according to the predicted confidence level. The second column indicates whether the prediction is correct or not. In this example, the prediction is correct if IoU ≥ 0.5. Recall-Precision Curve and Average Precision
- 14. An average for the 11-point interpolated AP is calculated and the curve is divided from 0 to 1.0 into 11 points Average Precision (AP) is the area under the precision-recall curve. mAP (mean average precision) is the average of the AP for each class. Average Precision and mean Average Precision
- 16. Fast YOLO uses a neural network with fewer convolutional layers (9 instead of 24) and fewer filters in those layers. Comparison to Other Real-Time Systems YOLO is 10 mAP more accurate than the fast version while still well above real-time in speed.
- 17. VOC 2007 Error Analysis •Correct: correct class and IOU > .5 • Localization: correct class, .1 < IOU < .5 • Similar: class is similar, IOU > .1 • Other: class is wrong, IOU > .1 • Background: IOU < .1 for any object Localization errors account for more of YOLO’s errors than all other sources combined. Fast R-CNN makes much fewer localization errors but far more background errors.