This is a simple "updated" Demo of Visual Question Answering by VQA_Demo which uses pretrained models (see VGG16 and models/VQA) to answer a given question about the given image.
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Keras version 2.0.4
- Modular deep learning library based on python
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Tensorflow
- For the development of this project, I used Tensorflow 1.1.0
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scikit-learn
- Quintessential machine library for python
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Spacy version 1.8.2
- Used to load Glove vectors (word2vec)
- You may have to upgrade your Spacy to use Glove vectors (default is Goldberg Word2Vec)
- To upgrade & install Glove Vectors
- pip install spacy
- python -m spacy download en
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OpenCV
- OpenCV is used only to resize the image and change the color channels,
- You may use other libraries as long as you can pass a 224x224 BGR Image (NOTE: BGR and not RGB)
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VGG 16 Pretrained Weights
- Please download the weights file vgg16_weights.h5
python demo.py
Put your test images in the dataset/ directory
Expected Output: 095.2 % train 00.67 % subway 00.54 % mcdonald's 00.38 % bus 00.33 % train station