The goal of image captioning is to convert a given input image into a natural language description. The encoder-decoder framework is widely used for this task. The image encoder is a convolutional neural network (CNN). In this tutorial, we used resnet-152 model pretrained on the ILSVRC-2012-CLS image classification dataset. The decoder is a long short-term memory (LSTM) network.
For the encoder part, the pretrained CNN extracts the feature vector from a given input image. The feature vector is linearly transformed to have the same dimension as the input dimension of the LSTM network. For the decoder part, source and target texts are predefined. For example, if the image description is "Giraffes standing next to each other", the source sequence is a list containing ['<start>', 'Giraffes', 'standing', 'next', 'to', 'each', 'other'] and the target sequence is a list containing ['Giraffes', 'standing', 'next', 'to', 'each', 'other', '<end>']. Using these source and target sequences and the feature vector, the LSTM decoder is trained as a language model conditioned on the feature vector.
git clone https://github.com/nsuryaa/smart-glass-for-blind.git
cd image_captioningpip install -r requirements.txt
chmod +x download.sh
./download.shpython build_vocab.py
python resize.pypython train.py python sample.py --image='png/example.png'If you do not want to train the model from scratch, you can use a pretrained models.
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