You can find the project at MemeflyAI.
| Nick Buukhalter | Harsh Desai | Han Lee |
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Automatic meme generation model using Tensorflow Keras. Model is Dockerized and served as a REST API with FastAPI/uvicorn ASGI endpoint. A separate serving model serving is done with a combination of FastAPI/uvicorn ASGI endpoint with models served using Tensorflow Serving on Sagemaker.
- Numpy
- Pandas
- Tensorflow
- FastAPI
- Selenium
- Tensorflow Serving
- Docker
- MySQL
- MongoDB
- AWS ECR
- AWS Elastic Beanstalk
- AWS S3
- AWS Sagemaker
We used an encoder-decoder architecture for the meme generation task. Pre-trained Inception V3 architecture and weights are used as the encoder to extract embeddings from an input image. At the same time, we encode the texts into text embeddings and concat them together with image embeddings. For the decoder, we used GRU to to map the image and text embeddings to predict the next word in the text string.
At training time, we repeat the same image embeddings as input and send in text sequences in order, e.g., 0. this, 1. this is, 2. this is a, 3. this is a sequence. The model will try to predict the next word in the sequence given the input image embedding and text embeddings. We denote the beginning and the end of a text sequence with startseq and endseq.
At inferencing time, we send in image embeddings and the seed token startseq to the model, and then repeatly send in the image embeddings and the prediction output of the previous timestep, until either we see endseq or reach maximum sentence length. To improve the quality of the output, we used beam search to greedily select the best N sentences. But it has to be noted that beam search is neither optimal nor complete algorithm.
To increase varieties, we tried 1) adding Guassian noise to the input image and 2) choosing top N sentence scores using beam search.
The architecture is summarized here:
- Image
- Text
Please see Data Engineering for details.
Please see Machine Learning Engineering - Deployment for details.
Please see Data Engineering for details.
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.
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- Check first to see if your issue has already been reported.
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- Ensure any install or build dependencies are removed before the end of the layer when doing a build.
- Update the README.md with details of changes to the interface, including new plist variables, exposed ports, useful file locations and container parameters.
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These contribution guidelines have been adapted from this good-Contributing.md-template.
See Data Engineering for details on the data engineering of our project.
See Machine Learning Engineering - Training for details on the training part of our project.
See Machine Learning Engineering - Deployment for details on the deployment of our project.