All the YOLOv6 models have been pre-trained on the COCO object detection dataset. You can find the pre-trained weights on the official GitHub repository. There is also a YOLOv6 Large model trained with the ReLU activation [1], which tends to be faster but with a slightly lower mAP (51.7...
We providevalidation results on detection, pose estimation, and map-ping. The dataset to train the network for underwaterlitter detection, together with our code, are made publicat https://www.kaggle.com/datasets/davianmartinovci/litter-detection and respectively https://github.com/ReteDavid/IFAC...
4.1. Dataset To demonstrate the effectiveness and versatility of the object detection model proposed in this paper, we chose two underwater imaging datasets, namely, the Starfish dataset and the DUO dataset, for validation purposes. The utilization of these datasets allowed us to evaluate the model...
Figure 7. PR Curves of different methods for object categories on the Brackish dataset (IoU = 0.5). (a) Starfish. (b) Crab. (c) Fish. (d) Smallfish. (e) Shrimp. (f) Jellyfish. Table 4. Results of comparative study for different methods in detection precision on the Brackish datas...
To configure our dataset, we gather the point clouds obtained from the two previously mentioned sources in Section 3.2.1. First, we take 192 point clouds from our previous dataset (from now on, referred to as set SASV). Second, we extract point clouds from the AUV immersions. From the ...
The training process can be performed in an Ubuntu environment using a docker and consists in short of the following steps: (a) a tensor flow object detection GitHub repository is cloned, (b) the training dataset has to be prepared in tfrecord format, (c) a new label map should be ...
The training process can be performed in an Ubuntu environment using a docker and consists in short of the following steps: (a) a tensor flow object detection GitHub repository is cloned, (b) the training dataset has to be prepared in tfrecord format, (c) a new label map should be ...