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Developed a Deep Neural Network model which classifies the traffic signs.By using Digital Image Processing techniques likes Gray Scale Conversion,Histogram Equalization,Image normalization ,we preprocessed the images.By using Convoultional Neural Network model, from keras framework developed a working model. This model gives 96% accurate results.

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prudhvinadhreddy06/German-Traffic-Sign-Classifier

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German-Traffic-Sign-Classifier

You can download data from this link https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip

Achievied accuracy around 96%

Train on 34799 samples

Epoch 1/30 34799/34799 [==============================] - 10s 286us/step - loss: 1.8930 - acc: 0.4808 - val_loss: 0.5692 - val_acc: 0.8365

Epoch 2/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.4983 - acc: 0.8466 - val_loss: 0.3246 - val_acc: 0.9104

Epoch 3/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.2855 - acc: 0.9135 - val_loss: 0.2394 - val_acc: 0.9348

Epoch 4/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.1926 - acc: 0.9406 - val_loss: 0.2302 - val_acc: 0.9394

Epoch 5/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.1488 - acc: 0.9550 - val_loss: 0.2184 - val_acc: 0.9423

Epoch 6/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.1168 - acc: 0.9647 - val_loss: 0.2276 - val_acc: 0.9386

Epoch 7/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.1011 - acc: 0.9686 - val_loss: 0.2097 - val_acc: 0.9440

Epoch 8/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0800 - acc: 0.9739 - val_loss: 0.2097 - val_acc: 0.9428

Epoch 9/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0681 - acc: 0.9793 - val_loss: 0.1818 - val_acc: 0.9519

Epoch 10/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0622 - acc: 0.9811 - val_loss: 0.1961 - val_acc: 0.9504

Epoch 11/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0548 - acc: 0.9834 - val_loss: 0.1868 - val_acc: 0.9540

Epoch 12/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0476 - acc: 0.9849 - val_loss: 0.1835 - val_acc: 0.9559

Epoch 13/30 34799/34799 [==============================] - 7s 200us/step - loss: 0.0445 - acc: 0.9859 - val_loss: 0.2101 - val_acc: 0.9494

Epoch 14/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0401 - acc: 0.9874 - val_loss: 0.1861 - val_acc: 0.9549

Epoch 15/30 34799/34799 [==============================] - 7s 200us/step - loss: 0.0348 - acc: 0.9888 - val_loss: 0.1880 - val_acc: 0.9560

Epoch 16/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0352 - acc: 0.9884 - val_loss: 0.1856 - val_acc: 0.9571

Epoch 17/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0323 - acc: 0.9896 - val_loss: 0.1857 - val_acc: 0.9572

Epoch 18/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0291 - acc: 0.9907 - val_loss: 0.1917 - val_acc: 0.9567

Epoch 19/30 34799/34799 [==============================] - 7s 198us/step - loss: 0.0274 - acc: 0.9909 - val_loss: 0.2023 - val_acc: 0.9566

Epoch 20/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0284 - acc: 0.9908 - val_loss: 0.1707 - val_acc: 0.9601

Epoch 21/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0241 - acc: 0.9917 - val_loss: 0.1939 - val_acc: 0.9584

Epoch 22/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0218 - acc: 0.9928 - val_loss: 0.2135 - val_acc: 0.9551

Epoch 23/30 34799/34799 [==============================] - 7s 201us/step - loss: 0.0210 - acc: 0.9933 - val_loss: 0.1906 - val_acc: 0.9604

Epoch 24/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0212 - acc: 0.9934 - val_loss: 0.1893 - val_acc: 0.9587

Epoch 25/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0175 - acc: 0.9943 - val_loss: 0.1878 - val_acc: 0.9605

Epoch 26/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0203 - acc: 0.9938 - val_loss: 0.2004 - val_acc: 0.9583

Epoch 27/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0180 - acc: 0.9939 - val_loss: 0.1876 - val_acc: 0.9607

Epoch 28/30 34799/34799 [==============================] - 7s 200us/step - loss: 0.0156 - acc: 0.9949 - val_loss: 0.1933 - val_acc: 0.9610

Epoch 29/30 34799/34799 [==============================] - 7s 200us/step - loss: 0.0158 - acc: 0.9951 - val_loss: 0.2096 - val_acc: 0.9578

Epoch 30/30 34799/34799 [==============================] - 7s 199us/step - loss: 0.0162 - acc: 0.9949 - val_loss: 0.1909 - val_acc: 0.9606

Test loss: 0.19094456916148936

Test accuracy: 0.9605700712494688

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Developed a Deep Neural Network model which classifies the traffic signs.By using Digital Image Processing techniques likes Gray Scale Conversion,Histogram Equalization,Image normalization ,we preprocessed the images.By using Convoultional Neural Network model, from keras framework developed a working model. This model gives 96% accurate results.

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