基于卷积神经网络的数字手势识别安卓APP，识别数字手势0-10（The number gestures recognition Android APP based on convolutional neural network(CNN), which can recognize the gestures corresponding number 0 to 10）
Code for Paper: Training Software Engineering Agents and Verifiers with SWE-Gym [ICML 2025]
关键参数与规格
关键参数与规格
参数
值
语言
Python
类别
Model Development
子类别
Classical ML
星标数
750
分叉数
257
贡献者
1
创建日期
2019-05-19
更新日期
2026-10-15
1 天星标数
0
7 天星标数
-2
排名
14,230
上次排名
18,948
排名变化
4,718
7 天星标排名
18,436
上次 7 天星标排名
18,370
7 天星标排名变化
-66
总星标排名
14,075
上次总星标排名
14,052
总星标排名变化
-23
国家
Hong Kong
README 摘录
Deep-Learning-Approach-for-Surface-Defect-Detection A Tensorflow implementation of "**Segmentation-Based Deep-Learning Approach for Surface-Defect Detection**" The author submitted the paper to Journal of Intelligent Manufacturing ( where it was published In May 2019 . The test environment You should know I used the Dataset used in the papar, you can download KolektorSDD here. If you train you own datset ,you should change the dataset interfence for you dataset. You can refer to the paper for details of
the experiment. my experimental results on KolektorSDD **Notes:** the first 30 subfolders are used as training sets, the remaining 20 for testing. Although, I did not strictly follow the params of the papar , I still got a good result. **visualization:** testing the KolektorSDD After downloading the KolektorSDD and changing the param[data_dir] Then you can find the result in the "/visulaiation/test" and "Log/*. txt" training the KolektorSDD **First, only the segmentation network is independently trained, then the weights
for the segmentation network are frozen and only the decision network layers are trained. ** training the segment network training the decision network training the total network( not good）
A Tensorflow implementation of "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection"
The author submitted the paper to Journal of Intelligent Manufacturing (https://link.springer.com/article/10.1007/s10845-019-01476-x), where it was published In May 2019 .
The test environment
python 3.6
cuda 9.0
cudnn 7.1.4
Tensorflow 1.12
You should know
I used the Dataset used in the papar, you can download KolektorSDD here.
If you train you own datset ,you should change the dataset interfence for you dataset.
You can refer to the paper for details of the experiment.
my experimental results on KolektorSDD
Notes: the first 30 subfolders are used as training sets, the remaining 20 for testing. Although, I did not strictly follow the params of the papar , I still got a good result.
2019-05-21 09:20:54,634 - utils - INFO - total number of testing samples = 160
2019-05-21 09:20:54,634 - utils - INFO - positive = 22
2019-05-21 09:20:54,634 - utils - INFO - negative = 138
2019-05-21 09:20:54,634 - utils - INFO - TP = 21
2019-05-21 09:20:54,634 - utils - INFO - NP = 0
2019-05-21 09:20:54,634 - utils - INFO - TN = 138
2019-05-21 09:20:54,635 - utils - INFO - FN = 1
2019-05-21 09:20:54,635 - utils - INFO - accuracy(准确率) = 0.9938
2019-05-21 09:20:54,635 - utils - INFO - prescision（查准率） = 1.0000
2019-05-21 09:20:54,635 - utils - INFO - recall（查全率） = 0.9545
visualization:
testing the KolektorSDD
After downloading the KolektorSDD and changing the param[data_dir]
python run.py --test
Then you can find the result in the "/visulaiation/test" and "Log/*.txt"
training the KolektorSDD
First, only the segmentation network is independently trained, then the weights for the segmentation network are frozen and only the decision network layers are trained.
