Jia-xin wang
Jia-xin wang
Command for other stages can't be found, can you share that?
> Sure, you could simply change the config name, like:当然，您可以简单地更改配置名称，例如： > > CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh configs/centernet_AERIS/centernet_res18_stage1.py 4CUDA_VISIBLE_DEVICES=0,1,2,3 端口=29500 bash 工具/dist_train.sh配置/centernet_AERIS/centernet_res18_stage1.py 4 > > Config names find in here: https://github.com/cuiziteng/ECCV_AERIS/tree/master/configs配置名称可在此处找到：...
> 您可以自己设置保存路径的位置，比如在训练指令后加上--work-dir "Your_path"，当然mmdetection也会自己默认给你一个保存地址 > > 例如： CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh configs/centernet_AERIS/centernet_res18_stage1.py --work-dir work_dirsCUDA_VISIBLE_DEVICES=0,1,2,3 端口=29500 bash tools/dist_train.sh configs/centernet_AERIS/centernet_res18_stage1.py --work-dir work_dirs > > 这样就会保存在你当前目录下的work_dirs文件夹 您好，tools目录下有一个的train_SR.py，超分模型是需要单独训练出来吗？还是直接训练centernet_res18_stage1.py 就行呢？
> First of all, wonderful Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models".首先，精彩的“渐进式蒸馏用于扩散模型快速采样”的实现。 However the link to the pre-trained weights in diffusion_distiller (https://cloud.mail.ru/public/mQGz/k1pNzg2ng) seems cannot be reached in...
> label text两列数据 您这种模型部署方法是什么，应该通过什么学习呢？对我来说，模型部署是一个困难的问题
谢谢您 > 你可以看看flask。就几行代码就部署了。遇到看不懂的直接问chatgpt里面会解释
> How to deploy inference code to predict your own images instead of testing datasets like Coco如何部署推理代码来预测您自己的图像，而不是像 Coco 那样测试数据集 Hi, did you find a solution to this and what code...