一、本文介绍 本文记录的是改进RT-DETR的损失函数,将其替换成Slide Loss,并详细说明了优化原因,注意事项等。Slide Loss函数可以有效地解决样本不平衡问题,为困难样本赋予更高的权重,使模型在训练过程中更加关注困难样本。若是在自己的数据集中发现容易样本的数量非常大,而困难样本相对稀疏,可尝试使用Slide Loss来提高...
专注于改进RT-DETR模型,🚀 in PyTorch >, Support to improve backbone, neck, head, loss, IoU and other modules🚀based on Ultralytics - iscyy/RTDETR
🔥🔥🔥专注于改进YOLOv8模型,NEW - YOLOv8 🚀 RT-DETR 🥇 in PyTorch >, Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀 Resources Readme License AGPL-3.0 license Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repository Release...
Tags GPU Language Python License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input1 file arrow_right_alt Output276 files arrow_right_alt Logs185.2 second run - successful arrow_right_alt Comments0 comments arrow_right_alt...
v=8c774d19641ee12bca33:2:2935571 at https://www.kaggle.com/static/assets/app.js?v=8c774d19641ee12bca33:2:2931952 at Object.next (https://www.kaggle.com/static/assets/app.js?v=8c774d19641ee12bca33:2:2932057) at j (https://www.kaggle.com/static/assets/app.js?v=8c774d...