Yolov3 loss function. These factors continue to constrain the performance of existing methods. IoU Loss directly optimizes overlap but fails when boxes do not intersect. This should be 1 if the bounding box prior overlaps a ground truth object by more than any other bounding box prior. YOLO v6 YOLO v6 was proposed in 2022 by Li et al. Jan 16, 2024 · Which loss function serves as the inspiration for the focal loss, binary cross entropy or categorical cross entropy? To answer this question let’s first explore the fundamentals of this loss function and how it helps in model training. YOLOv3, introduced in 2018, contained only "incremental" improvements, including the use of a more complex backbone network, multiple scales for detection, and a more sophisticated loss function. In addition, the SAM attention mechanism is combined to reduce the background interference, and the complete IOU (CIOU) loss function is used instead of the original IOD loss function to However, while focusing on globally salient regions, these methods often underperform in preserving local details, limiting detection gains in detail-rich aerial scenes. First, based on the YOLOv3-tiny network, residual network and improved SPP structure are introduced to enrich feature extraction and reduce feature loss. Mar 29, 2019 · I was going to write my own implementation of the YOLOv3 and coming up with some problem with the loss function. These improvements strengthen feature extraction capability, increase localization accuracy, and effectively reduce the interference of low-quality samples. YOLO v4 and YOLO v5 use a similar loss function to train the model. YOLOv3, launched in 2018, further enhanced the model's performance using a more efficient backbone network, multiple anchors, and spatial pyramid pooling. In YOLOv3 S = 13, 26, 52 S = 13, 26, 52 for all the scales. Oct 9, 2020 · So a carefully and thoughtfully crafted loss function can pack a lot of information into a small feature map. YOLOv4 was released in 2020, introducing innovations like Mosaic data augmentation, a new anchor-free detection head, and a new loss function. In the next section we will discuss the most important features of the YOLO loss function. Oct 23, 2018 · YOLOv3 predicts an objectness score for each bounding box using logistic regression. A well-documented TensorFlow implementation of the YOLOv3 model. The one thing to add is that as there are 3 scales of detection for YOLOv3, the loss function you have will only sum over one of these scales. Confidence loss. . S. Jun 1, 2022 · From the tutorial: Loss function in YOLO v3? [formula] Loss = Regression Loss + Confidence Loss + Classification Loss [fig] Total loss function. Jul 23, 2025 · For training the model, we need to define a loss function on which our model can optimize. as an improvement over previous versions. However, YOLO v5 introduces a new term called "CIoU loss," which is a variant of the IoU loss function designed to improve the model's performance on imbalanced datasets. P. The paper discusses that the YOLO (v3) architecture was optimized on a combination of four losses: no object loss, object loss, box coordinate loss, and class loss. The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models. Loss function improvements aim to enhance the accuracy and stability of bounding box regression. A hands-on project on YOLOv3 gave me a great understanding of convolution neural networks in general and many state-of-the-art methods. Moreover, I want to push it further by combining it with an LSTM (long short-term memory) algorithm like Deep SORT and create a object and pedestrian tracker. The original paper mention that he uses Binary Cross Entropy on the class prediction part, which is what I did. Classification loss. - akozd/tensorflow_yolo_v3 In v3 they use 3 boxes across 3 different "scales" You can try getting into the nitty-gritty details of the loss, either by looking at the python/keras implementation v2, v3 (look for the function yolo_loss) or directly at the c implementation v3 (look for delta_yolo_box, and delta_yolo_class). Both Base-YOLOv3 and SSD models were enhanced using the BEA method and its proposed loss functions. The model integrates the ConvNeXt V2 module, EMA multi-scale attention mechanism, and WIoU v3 loss function, and is developed by optimizing YOLOv8 as the baseline model. I can't say exactly as the loss function was never explicitly given in YOLOv3 but I think it is almost correct. To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. 我們的主要改進分兩部份，一是修改YOLOv3的輸入端，使用RGB與深度影像作為輸入，且將YOLOv3 中的 Darknet-53 架構加入通道注意力 (channel attention) 強化擷取特徵能力，並使用這些特徵進行多尺度的偵測與辨識；二是物件的3D位移分量藉由物件中心與相機的距離來估計 Explores the final layers and loss functions of the YOLO v1, v2 and v3 deep object detectors. d1opcb, 40ri, vgrgl, aetkp, ypixu, bjwng, vteir, woyp, rsb40, yuyij,