neuronandchords/movie_genre_prediction

Prediction of a movie genre by it's poster or cover. Accuracy 91.17%

Movie-Genre-Prediction

Predict the genre of any Movie, TV Series, etc using the cover/ poster.

Concept

The aim that we target is to predict the genre of the movie, right? So, if you give your brains a little thinking what we are dealing with is actually a Multi-Label-Classification model. Why? A movie can not always belong to a particular genre, like action or comedy. The movie can be a combination of two or more genres. Hence, multi-label image classification. The model that we're building is a specific architecture of Convolutional Neural Networks. Here, we try to use CNN's to learn and recognize the relationships between a pixel and surrounding pixels and try to determine the category/ genre it is most likely to fit into. As we already discussed it won't just belong to a category so we'll be displaying the top three categories it belongs to along with their scores.

Data and pre-processing

The dataset we’ll be using contains the poster images of several multi-genre movies. I have made some changes in the dataset and converted it into a structured format, i.e. a folder containing the images and a .csv file for true labels. You can download the structured dataset from here.

Framework

For a quick setup and testing I've used TensorFlow. Will be adding a PyTorch version anytime soon.

GPU and Memory Requirements

I would highly suggest to work with the model on Cloud as it contains a dataset of 7000 images of size 400*400 and would require high memory(RAM) along with TPUs to speed up the computations. I would suggest a minimum of 35GB Ram with Google Cloud Engine backend TPUs and nothing less than that, because that's what my model took for training.

Edit You can reduce the target size from (400,400,3) to whatever your memory requirements you want. For example you can change it to (250,250,3) and it work fine at 16GB of RAM but TPU support is must for enhanced and fast computations. It may/may not affect the accuracy as more complex features cannot be identified in a samller fig size. The model files have been updated to this version so everyone can use it.

Final Predictions

The accuracy achieved was 0.9117 which can be improved by using a more diverse and structured dataset and extensive TPU resources at hand.