[{"title":"( 36 个子文件 58.51MB ) Traffic-sign-classifier:在该项目中,使用深度神经网络和卷积神经网络对交通标志进行分类。 对模型进行了训练,使其可以使用“德国交通标志数据集”从自然图像中解码交通标志。 训练模型后,将在网络上发现的交通标志的新图像上对模型进行测试","children":[{"title":"Traffic-sign-classifier-master","children":[{"title":".gitattributes <span style='color:#111;'> 52B </span>","children":null,"spread":false},{"title":"Traffic_Sign_Classifier.ipynb <span style='color:#111;'> 4.41MB </span>","children":null,"spread":false},{"title":"visualize_cnn.png <span style='color:#111;'> 232.09KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 5.66KB </span>","children":null,"spread":false},{"title":"model.index <span style='color:#111;'> 3.93KB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 115B </span>","children":null,"spread":false},{"title":"examples","children":[{"title":"placeholder.png <span style='color:#111;'> 5.04KB </span>","children":null,"spread":false},{"title":"grayscale.jpg <span style='color:#111;'> 25.63KB </span>","children":null,"spread":false},{"title":"visualization.jpg <span style='color:#111;'> 15.07KB </span>","children":null,"spread":false},{"title":"random_noise.jpg <span style='color:#111;'> 24.28KB </span>","children":null,"spread":false}],"spread":true},{"title":"writeup_template.md <span style='color:#111;'> 8.97KB </span>","children":null,"spread":false},{"title":"Traffic_Sign_Classifier.html <span style='color:#111;'> 4.40MB </span>","children":null,"spread":false},{"title":"Traffic_Sign_Classifier.py <span style='color:#111;'> 30.97KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"test_images","children":[{"title":"2.jpg <span style='color:#111;'> 825.48KB </span>","children":null,"spread":false},{"title":"8.jpg <span style='color:#111;'> 136.07KB </span>","children":null,"spread":false},{"title":"6.jpg <span style='color:#111;'> 66.13KB </span>","children":null,"spread":false},{"title":"1.jpg <span style='color:#111;'> 11.03KB </span>","children":null,"spread":false},{"title":"4.jpg <span style='color:#111;'> 12.61KB </span>","children":null,"spread":false},{"title":"5.jpg <span style='color:#111;'> 7.06KB </span>","children":null,"spread":false},{"title":"7.jpg <span style='color:#111;'> 32.00KB </span>","children":null,"spread":false},{"title":"3.jpg <span style='color:#111;'> 139.00KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 8.23KB </span>","children":null,"spread":false},{"title":"model.meta <span style='color:#111;'> 369.18KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"utils.cpython-35.pyc <span style='color:#111;'> 6.02KB </span>","children":null,"spread":false}],"spread":false},{"title":"signnames.csv <span style='color:#111;'> 999B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 46B </span>","children":null,"spread":false},{"title":"model.data-00000-of-00001 <span style='color:#111;'> 78.89MB </span>","children":null,"spread":false},{"title":"checkpoint <span style='color:#111;'> 67B </span>","children":null,"spread":false},{"title":"documentation","children":[{"title":"inception.png <span style='color:#111;'> 15.15KB </span>","children":null,"spread":false},{"title":"download_data.png <span style='color:#111;'> 988.63KB </span>","children":null,"spread":false},{"title":"data.png <span style='color:#111;'> 236.32KB </span>","children":null,"spread":false},{"title":"model.png <span style='color:#111;'> 284.14KB </span>","children":null,"spread":false},{"title":"probabilities_data.png <span style='color:#111;'> 1.04MB </span>","children":null,"spread":false},{"title":"labeled_data.png <span style='color:#111;'> 1004.30KB </span>","children":null,"spread":false},{"title":"graphs.png <span style='color:#111;'> 15.22KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":true}]