CarDetection-Keras

"Car Detection" is trained in Keras using Tensorflow as back-end. It's taking an image as input and it
gives a binary decision whether a car is present in the image or not. It's a Dense Neural Network. After train, converting the .h5 model into .mlmodel to use in Xcode. (Please check the CarDetection-iOS project for that : https://github.com/ashislaha/CarDetection-iOS ).

Training Data set is uploaded in https://drive.google.com/drive/folders/0B0QC-w3ZqaT1RzlGeGtYeVE2cTA OR https://s3.ap-south-1.amazonaws.com/car-detection-images/Archive.zip .

Put the data set into your working directory to train the model. It contains around 1500 training data & 100 test data where both car & non-car images are present.

If you want to test a random image whether car is present in the image or not : goto "model" directory --> paste your image in "test" folder --> $ python predictions.py --file='test/your_image'

Used both Convolutional Neural Network(CNN) & Dense Neural Network(DNN) to train the model.

CNN gives 99.6% accuracy on Training data & 88.6% on Test data. DNN gives 92% accuracy on Training data & 87% on Test data.

Used Metrics to train in CNN : loss='binary_crossentropy', metrics=['accuracy'], optimizer='adadelta'

Used Metrics to train in DNN : loss='binary_crossentropy', metrics=['accuracy'], optimizer='rmsprop'

CNN TRAIN RESULTS :

1475/1475 [==============================] - 14s - loss: 0.6336 - acc: 0.6142 - val_loss: 0.6956 - val_acc: 0.9207

Epoch 2/25 1475/1475 [==============================] - 15s - loss: 0.5122 - acc: 0.7620 - val_loss: 0.8328 - val_acc: 0.6524

Epoch 3/25 1475/1475 [==============================] - 16s - loss: 0.4236 - acc: 0.8210 - val_loss: 0.2083 - val_acc: 0.9573

Epoch 4/25 1475/1475 [==============================] - 16s - loss: 0.3493 - acc: 0.8508 - val_loss: 0.4898 - val_acc: 0.8537

Epoch 5/25 1475/1475 [==============================] - 16s - loss: 0.2797 - acc: 0.8942 - val_loss: 0.5409 - val_acc: 0.8171

Epoch 6/25 1475/1475 [==============================] - 15s - loss: 0.2392 - acc: 0.9119 - val_loss: 0.5585 - val_acc: 0.8049

Epoch 7/25 1475/1475 [==============================] - 15s - loss: 0.2006 - acc: 0.9281 - val_loss: 0.5729 - val_acc: 0.7683

Epoch 8/25 1475/1475 [==============================] - 15s - loss: 0.1705 - acc: 0.9403 - val_loss: 0.3006 - val_acc: 0.9146

Epoch 9/25 1475/1475 [==============================] - 15s - loss: 0.1404 - acc: 0.9539 - val_loss: 0.0538 - val_acc: 0.9878

Epoch 10/25 1475/1475 [==============================] - 15s - loss: 0.1152 - acc: 0.9647 - val_loss: 0.2950 - val_acc: 0.9085

Epoch 11/25 1475/1475 [==============================] - 15s - loss: 0.0961 - acc: 0.9776 - val_loss: 0.2111 - val_acc: 0.9329

Epoch 12/25 1475/1475 [==============================] - 15s - loss: 0.0770 - acc: 0.9817 - val_loss: 0.3777 - val_acc: 0.8841

Epoch 13/25 1475/1475 [==============================] - 15s - loss: 0.0631 - acc: 0.9864 - val_loss: 0.2729 - val_acc: 0.9329

Epoch 14/25 1475/1475 [==============================] - 15s - loss: 0.0503 - acc: 0.9905 - val_loss: 0.4031 - val_acc: 0.8963

Epoch 15/25 1475/1475 [==============================] - 17s - loss: 0.0440 - acc: 0.9905 - val_loss: 0.3678 - val_acc: 0.8963

Epoch 16/25 1475/1475 [==============================] - 17s - loss: 0.0320 - acc: 0.9946 - val_loss: 0.1558 - val_acc: 0.9512

Epoch 17/25 1475/1475 [==============================] - 17s - loss: 0.0285 - acc: 0.9932 - val_loss: 0.3010 - val_acc: 0.9329

Epoch 18/25 1475/1475 [==============================] - 20s - loss: 0.0245 - acc: 0.9953 - val_loss: 0.3219 - val_acc: 0.9268

Epoch 19/25 1475/1475 [==============================] - 22s - loss: 0.0214 - acc: 0.9966 - val_loss: 0.4263 - val_acc: 0.9085

Epoch 20/25 1475/1475 [==============================] - 21s - loss: 0.0172 - acc: 0.9973 - val_loss: 0.4255 - val_acc: 0.9024

Epoch 21/25 1475/1475 [==============================] - 19s - loss: 0.0155 - acc: 0.9959 - val_loss: 0.2827 - val_acc: 0.9390

Epoch 22/25 1475/1475 [==============================] - 18s - loss: 0.0146 - acc: 0.9959 - val_loss: 0.3245 - val_acc: 0.9329

Epoch 23/25 1475/1475 [==============================] - 18s - loss: 0.0158 - acc: 0.9959 - val_loss: 0.2655 - val_acc: 0.9390

Epoch 24/25 1475/1475 [==============================] - 17s - loss: 0.0116 - acc: 0.9966 - val_loss: 0.3174 - val_acc: 0.9329

Epoch 25/25 1475/1475 [==============================] - 16s - loss: 0.0104 - acc: 0.9966 - val_loss: 0.3039 - val_acc: 0.9329

238/238 [==============================] - 0s
('\nTest accuracy:', 0.8865546223496189)

DNN TRAIN RESULTS :

This is the accuray after trained by 50 epochs , almost 92% accurate on train data & on unknown test data 87% accurate

1639/1639 [==============================] - 1s - loss: 5.9528 - acc: 0.4997
Epoch 2/50 1639/1639 [==============================] - 1s - loss: 0.7847 - acc: 0.6516
Epoch 3/50 1639/1639 [==============================] - 1s - loss: 0.6361 - acc: 0.6840
Epoch 4/50 1639/1639 [==============================] - 1s - loss: 0.6202 - acc: 0.6901
Epoch 5/50 1639/1639 [==============================] - 1s - loss: 0.5706 - acc: 0.7035
Epoch 6/50 1639/1639 [==============================] - 1s - loss: 0.5586 - acc: 0.7212
Epoch 7/50 1639/1639 [==============================] - 1s - loss: 0.5272 - acc: 0.7364
Epoch 8/50 1639/1639 [==============================] - 1s - loss: 0.5192 - acc: 0.7572
Epoch 9/50 1639/1639 [==============================] - 1s - loss: 0.4962 - acc: 0.7608
Epoch 10/50 1639/1639 [==============================] - 1s - loss: 0.4754 - acc: 0.7749
Epoch 11/50 1639/1639 [==============================] - 1s - loss: 0.4548 - acc: 0.7877
Epoch 12/50 1639/1639 [==============================] - 1s - loss: 0.4559 - acc: 0.7993
Epoch 13/50 1639/1639 [==============================] - 1s - loss: 0.4350 - acc: 0.7999
Epoch 14/50 1639/1639 [==============================] - 1s - loss: 0.4194 - acc: 0.8212
Epoch 15/50 1639/1639 [==============================] - 1s - loss: 0.3927 - acc: 0.8170
Epoch 16/50 1639/1639 [==============================] - 1s - loss: 0.3953 - acc: 0.8231
Epoch 17/50 1639/1639 [==============================] - 1s - loss: 0.3828 - acc: 0.8286
Epoch 18/50 1639/1639 [==============================] - 1s - loss: 0.3720 - acc: 0.8383
Epoch 19/50 1639/1639 [==============================] - 1s - loss: 0.3543 - acc: 0.8408
Epoch 20/50 1639/1639 [==============================] - 1s - loss: 0.3486 - acc: 0.8475
Epoch 21/50 1639/1639 [==============================] - 1s - loss: 0.3400 - acc: 0.8481
Epoch 22/50 1639/1639 [==============================] - 1s - loss: 0.3153 - acc: 0.8542
Epoch 23/50 1639/1639 [==============================] - 1s - loss: 0.3149 - acc: 0.8633
Epoch 24/50 1639/1639 [==============================] - 1s - loss: 0.2994 - acc: 0.8700
Epoch 25/50 1639/1639 [==============================] - 1s - loss: 0.2977 - acc: 0.8658
Epoch 26/50 1639/1639 [==============================] - 1s - loss: 0.2908 - acc: 0.8713
Epoch 27/50 1639/1639 [==============================] - 1s - loss: 0.2725 - acc: 0.8761
Epoch 28/50 1639/1639 [==============================] - 1s - loss: 0.2640 - acc: 0.8914
Epoch 29/50 1639/1639 [==============================] - 1s - loss: 0.2467 - acc: 0.8883
Epoch 30/50 1639/1639 [==============================] - 1s - loss: 0.2375 - acc: 0.8932
Epoch 31/50 1639/1639 [==============================] - 1s - loss: 0.2549 - acc: 0.8883
Epoch 32/50 1639/1639 [==============================] - 1s - loss: 0.2557 - acc: 0.8926
Epoch 33/50 1639/1639 [==============================] - 1s - loss: 0.2406 - acc: 0.8993
Epoch 34/50 1639/1639 [==============================] - 1s - loss: 0.2407 - acc: 0.8981
Epoch 35/50 1639/1639 [==============================] - 1s - loss: 0.2246 - acc: 0.9024
Epoch 36/50 1639/1639 [==============================] - 1s - loss: 0.2290 - acc: 0.9091
Epoch 37/50 1639/1639 [==============================] - 1s - loss: 0.2134 - acc: 0.9109
Epoch 38/50 1639/1639 [==============================] - 1s - loss: 0.2129 - acc: 0.9048
Epoch 39/50 1639/1639 [==============================] - 1s - loss: 0.2411 - acc: 0.9073
Epoch 40/50 1639/1639 [==============================] - 1s - loss: 0.1841 - acc: 0.9158
Epoch 41/50 1639/1639 [==============================] - 1s - loss: 0.2019 - acc: 0.9182
Epoch 42/50 1639/1639 [==============================] - 1s - loss: 0.1755 - acc: 0.9207
Epoch 43/50 1639/1639 [==============================] - 1s - loss: 0.2057 - acc: 0.9140
Epoch 44/50 1639/1639 [==============================] - 1s - loss: 0.1791 - acc: 0.9219
Epoch 45/50 1639/1639 [==============================] - 1s - loss: 0.1613 - acc: 0.9256
Epoch 46/50 1639/1639 [==============================] - 1s - loss: 0.1857 - acc: 0.9176
Epoch 47/50 1639/1639 [==============================] - 1s - loss: 0.1731 - acc: 0.9182
Epoch 48/50 1639/1639 [==============================] - 1s - loss: 0.1661 - acc: 0.9286
Epoch 49/50 1639/1639 [==============================] - 1s - loss: 0.1607 - acc: 0.9213
Epoch 50/50 1639/1639 [==============================] - 1s - loss: 0.1721 - acc: 0.9201
32/238 [===>..........................] - ETA: 0s('\nTest accuracy:', 0.87394957832929465)