guanfuchen
guanfuchen
I read the arch from DRN, and I get the findings from paper "Deformable Convolutional Networks" that the Aligned-Inception-ResNet arch can get goodness from both inception and resnet, I wonder...
I check the yolo v3 of this version, but it is slow at the forward time(6.509258985519409)).  Thanks for your reply.
related paper |摘要| |---| |In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally...
related paper |摘要| |---| |Recent anchor-based deep face detectors have achieved promising performance, but they are still struggling to detect hard faces, such as small, blurred and partially occluded faces....
related paper |摘要| |---| |Whilst recent face-recognition (FR) techniques have made significant progress on recognising constrained high-resolution web images, the same cannot be said on natively unconstrained low-resolution images at...
related paper |摘要| |---| |We propose a new face recognition method, called a pairwise relational network (PRN), which takes local appearance features around landmark points on the feature map, and...
首先实现S3FD模型，然后基于该模型继续实现SSH类似的SSD架构的人脸检测模型
The code is great, and I use the code to get coarse result, but I find it difficult to get the best performance on CamVid or other dataset, like segnet,...
ENet is a fast neural network for semantic segmentation, I use the code from [other's](https://github.com/davidtvs/PyTorch-ENet/blob/master/models/enet.py), and I simply train the module in CamVid Dataset, it seems working well, can it...
删除线表示任务完成或基本完成。 - 增加DALI处理数据增广 - 增加多机多卡训练 - 代码重构，参考[Python风格规范](https://zh-google-styleguide.readthedocs.io/en/latest/google-python-styleguide/python_style_rules/) - 增加学习率调整率策略和优化器选择，增加step学习率，由于要多个step间隔，可以用list但是比较麻烦，**预期后面增加文本配置文件**。 参考[schedulers](https://github.com/meetshah1995/pytorch-semseg/blob/master/ptsemseg/schedulers/schedulers.py)和[optimizers](https://github.com/meetshah1995/pytorch-semseg/blob/master/ptsemseg/optimizers/__init__.py) 参考[StepLR](https://pytorch.org/docs/stable/optim.html#torch.optim.lr_scheduler.StepLR) - 将LRN模型多loss损失进行复现 - 总结3D相关的语义分割综述 - add benchmark for different models #9 - ~~增加模型DeConvolution，参考 #48~~ - ~~增加模型FRRN A和FRRN B，参考 #50~~...