MLOps and Data Engineering.
koushikvikram / multimodal-image-retrieval Goto Github PK
View Code? Open in Web Editor NEW๐๐๐ผ๏ธ A deep learning application for retrieving images by searching with text.
๐๐๐ผ๏ธ A deep learning application for retrieving images by searching with text.
MLOps and Data Engineering.
For training and validation sets, load the caption embeddings and corresponding images in memory.
For test set, load only images in memory. Don't load captions.
We'll use images along with their caption embeddings to train an "encoder" that learns how to embed images in caption embedding space.
Setup GitHub Action for running Python tests.
Refer to the following articles:
Explore words for similarity and belonging in the word2vec model after training. Plot the vectors along with their labels using t-SNE.
Reference:
Read the following article and update docstrings in Classes and their Methods:
Train a Convolutional Neural Network to regress Caption Embeddings from Images.
Start with a ResNet pre-trained on ImageNet.
We're teaching the Neural Network to embed Images in the Caption Embedding space.
Write Unit Tests for the Caption Class.
Run the test images through the trained Convolutional Neural Network and get their caption embeddings.
Save these embeddings along with the Image IDs to disk.
Tasks:
Hi, can you please let me know the model that you are using for training and also the multimodal loss function that you are using.
Test the word2vec model. This goes along with exploration and visualization of the model.
Setup the following GitHub Action to automatically generate documentation when code is pushed:
Offer the Image Retrieval System as a WebApp using Streamlit and deploy it to Streamlit cloud from the GitHub repo.
Generate embeddings for each caption and split them into train, validation and test sets.
Save the split datasets to disk. They will be used for training and testing the Convolutional Neural Network.
Test the Dataset class.
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