Comments (4)
哇还好你提醒我了,配置文件忘上传了;
因为每个网络发行时均通过调整channel nums或input size等控制模型尺寸,就像yolo那样有不同版本如tiny,所以你通过配置文件可以区分vit不同版本参数异同
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配置文件已更新
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好的收到!
btw,提一个小小的建议,输出的log文件夹中可以包含一下使用的参数配置文件(。・ω・。)ノ
from awesome-backbones.
谢谢建议,已更新参数配置文件的保存,此外还新增类别激活图可视化
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Related Issues (20)
- 权重大小不匹配 HOT 2
- evaluation 进行模型评估时,数据集从何而来 HOT 1
- 该程序想要在pycharm中运行该怎么使用 HOT 1
- 如果用mvit,t2t_vit,visiontransformer三个算法分别得到三个模型,如何用adaboost将他们集成呢,这是可行的吗
- 调用evaluation脚本时每次准确率结果会有细微的差别 HOT 9
- mobileNet演示就报错TT,请问这个问题怎么解决啊?
- mobileNet演示项目问题? HOT 1
- 主要组成部分 HOT 1
- t2t_vit_19模型主要包含哪些部分 HOT 1
- 如何提升训练效果? HOT 1
- 有想过加一个后台来简化训模型的难度吗 HOT 1
- ONNX
- 测试集精度过高 HOT 1
- 有没有考虑过把自监督的模型也加进来?比如经典的对比学习等。
- 批量测试报错 HOT 1
- batch_test有没有结果保存呀,没找到,只有一个video在播放,有没有类似训练时直接输出的precision之类的呀 HOT 2
- how to change to the 2 classes classification? HOT 3
- cv2.error: OpenCV(4.8.0) /io/opencv/modules/imgcodecs/src/loadsave.cpp:802: error: (-215:Assertion failed) !buf.empty() in function 'imdecode_'
- 后续会加入一些新的模型吗比如squeezenet、xception
- 加入新的主干运行后出现Initialize the weights. Traceback (most recent call last): File "tools/train.py", line 171, in <module> main() File "tools/train.py", line 136, in main max_iters = data_cfg.get('train').get('epoches')*len(train_loader), TypeError: unsupported operand type(s) for *: 'NoneType' and 'int'的问题怎么解决 HOT 1
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