Comments (5)
Hi, you can fix the batch norm layer. In addition, to reduce the gpu memory, you can reduce the input image size from 473 to 378.
from cosnet.
Hi, you can fix the batch norm layer. In addition, to reduce the gpu memory, you can reduce the input image size from 473 to 378.
Thank you for your advice! I will try these settings.
from cosnet.
Hi, you can fix the batch norm layer. In addition, to reduce the gpu memory, you can reduce the input image size from 473 to 378.
Thank you for your advice! I will try these settings.
I'm sorry to bother you
But I'm curious, do you get a better result?
I've met the same question with you, I can only set batch_size=4 and change the input size to 378 using 2 GPU 1080ti
from cosnet.
According to my exps, it seems batch size is not the determinant factor as long as you have a strong pre-trained model.
from cosnet.
According to my exps, it seems batch size is not the determinant factor as long as you have a strong pre-trained model.
Thank you!
from cosnet.
Related Issues (20)
- About sample_range parameter
- AttributeError: 'CoattentionModel' object has no attribute 'main_classifier' HOT 5
- Error after filling a val_seqs.txt HOT 1
- about the attention in CoattentionModel HOT 2
- 关于训练得到的co_attention.pth问题 HOT 2
- 测试代码问题 HOT 4
- 训练阶段创建的saliency_data.txt问题 HOT 4
- out of memory during training HOT 5
- why don't the test code correspondes to paper HOT 1
- Can you provide the source image and GT of The Youtube-Objects?
- 数据集文件夹结构
- CRF postprocessing code
- Could you upload pretrained weight again onto BaiduNetdisk?
- 测试输出异常
- A BIG final LOSS value HOT 1
- Using Group attention get worse result
- Segmentation result
- About FBMS evaluation
- Hi! Where is the pretrained model of your MATNet?
- where is saliency_data.txt
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