The motive of this project is to build a pipeline to train and deploy models on Android using Tflite and not to create a good model for colorization.
More training and better dataset can improve the results.
- Pretrained MobileNet
- Custom Encoder-Decoder
The input image is passed through MobileNet and the output from the last layer is concatenated with the Encoder output and then fed to the Decoder. The output of the Decoder is a colorized image.
The model is trained on just 30k images in the Flikr30k dataset. The results are not so good. Brownish images are generated by the model.
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_3 (InputLayer) [(None, 256, 256, 1) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 128, 128, 64) 640 input_3[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 128, 128, 128 73856 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 64, 64, 128) 147584 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 64, 64, 256) 295168 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 32, 32, 256) 590080 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 32, 32, 512) 1180160 conv2d_4[0][0]
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, 1000)] 0
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 32, 32, 512) 2359808 conv2d_5[0][0]
__________________________________________________________________________________________________
repeat_vector (RepeatVector) (None, 1024, 1000) 0 input_2[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 32, 32, 256) 1179904 conv2d_6[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, 32, 32, 1000) 0 repeat_vector[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 32, 32, 1256) 0 conv2d_7[0][0]
reshape[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 256) 321792 concatenate[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 32, 32, 128) 295040 conv2d_8[0][0]
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 64, 64, 128) 147584 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 64, 64, 64) 73792 conv2d_transpose[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 128, 128, 64) 36928 conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 128, 128, 32) 18464 conv2d_transpose_1[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 128, 128, 16) 4624 conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 128, 128, 2) 290 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 256, 256, 2) 38 conv2d_13[0][0]
==================================================================================================
Total params: 6,725,752
Trainable params: 6,725,752
Non-trainable params: 0
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