liuheng0111

Yes， my _BASE_ is already Base-CenterNet2.yaml. Trainning command is: python train_net.py --num-gpus 8 --config-file configs/CenterNet2_R50_1x.yaml

My traing [log](https://drive.google.com/file/d/1_7lEN8psHNGZfaC9DwMJJIOq_MZIe2tH/view?usp=sharing) is very different from yours. My loss like: total_loss: 1.169 loss_box_reg_stage0: 0.1548 loss_box_reg_stage1: 0.2466 loss_box_reg_stage2: 0.2614 loss_centernet_agn_neg: 0.02222 loss_centernet_agn_pos: 0.05661 loss_centernet_loc: 0.1677 loss_cls_stage0: 0.09623 loss_cls_stage1: 0.08068 But...

My traing [log](https://drive.google.com/file/d/1_7lEN8psHNGZfaC9DwMJJIOq_MZIe2tH/view?usp=sharing). Another Question: I want to train detect with my self dataset, some boxes have not category, some boxes have category. I use two ways: 1: add another...

@xingyizhou The original code and config trained [log](https://drive.google.com/file/d/16RrHyjlSWILfc2hP3OS2CS0zLhYtW_BL/view?usp=sharing)

> ### Question > I got loss to be 0 when training on Qwen2 backend, > > {'loss': 0.0, 'learning_rate': 0.00015267175572519084, 'epoch': 0.0} 0%|▎ | 20/8720 [01:38 > What could...

I use qwen1.5-7b-chat in the pretrain stage is normal, but sft stage loss is zero. I checked the conversation is aligned. Is there any suggestions @lucasjinreal ? In the training...

> hi，如果直接全量放开可能会影响本身vit的能力，所以我们使用了layer wise lr decay让vit的浅层改动较小，深层adapt到新的任务上，这部分code在整理中，近期会开源 可以详细的说明一下layer wise lr decay的实现逻辑吗？或者提供一段代码？

@LightDXY pretrain阶段采用了多少训练数据，laion400M和CC3M全部都用了吗？看paper里说的训练了2个epoch，这么庞大的数据训练需要很多卡和很长时间吧？

> hi，如果直接全量放开可能会影响本身vit的能力，所以我们使用了layer wise lr decay让vit的浅层改动较小，深层adapt到新的任务上，这部分code在整理中，近期会开源 大概什么时候会开源？