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Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.doi:10.1016/j.patcog.2020.107404Xuebin QinZichen ZhangChenyang HuangMasood DehghanMartin JagersandPattern Recognition
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基于u2net网络进行简单修改使其部署到rk3588板子上. Contribute to Ley-WL/U2Net-rknn development by creating an account on GitHub.
Official GitHub (Pytorch implementation): https://github.com/NathanUA/U-2-Net Getting Started ###Requirements Tested with Python 3.7 and Tensorflow 2.3. pip install -r requirements.txt Download Data The model is trained on the DUTS image dataset for Salient Object Detection: http://saliencydetec...
Official code: https://github.com/xuebinqin/DIS. U2Net + ISNet GT encoder, training base on ssim loss, iou loss and bce loss. Using weighted binary cross-entropy (BCE) loss enhances the capability to extract foreground pixels. Mutil loss isnet.py ssim_loss = SSIM(window_size=11,size_...
Official code: https://github.com/xuebinqin/DIS. U2Net + ISNet GT encoder, training base on ssim loss, iou loss and bce loss. Using weighted binary cross-entropy (BCE) loss enhances the capability to extract foreground pixels. Mutil loss isnet.py ssim_loss = SSIM(window_size=11,size_...
U2Net + ISNet GT encoder, training base on ssim loss, iou loss and bce loss,experimented on tooth segmentation on panoramic X-ray images. - U2Net-with-multi-loss/Inference.py at main · xuanandsix/U2Net-with-multi-loss
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基于u2net网络进行简单修改使其部署到rk3588板子上. Contribute to Ley-WL/U2Net-rknn development by creating an account on GitHub.