Meshed-Memory-Transformer

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Meshed-Memory Transformer is an open-source AI tool for image captioning, providing reference code for the CVPR 2020 paper. It enables researchers to reproduce results and train models for image description tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is meshed-memory-transformer?

Meshed-Memory Transformer (M²) is an open-source project that provides the reference code for the paper "Meshed-Memory Transformer for Image Captioning" presented at CVPR 2020. This tool is designed for researchers and developers working in computer vision and natural language processing. It allows users to set up a conda environment, download necessary data like COCO annotations and detection features, and then evaluate or train their own image captioning models. The repository includes scripts for both testing and training, with configurable arguments for batch size, number of memory vectors, and learning rate scheduling. It requires Python 3.6 and specific data preparation steps to function correctly.

Best used for

Ideal for AI researchers and engineers who need to implement and experiment with advanced image captioning models, reproduce published research results, and train custom models on large datasets like COCO. Especially valuable for academic studies and developing sophisticated visual-semantic applications.

Common actions

generate image captions

train AI models

evaluate computer vision models

implement transformer models

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Capabilities

Key features

Image captioning
Transformer architecture
COCO dataset support
Model evaluation
Model training
Configurable parameters

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for Meshed-Memory Transformer?

The Meshed-Memory Transformer requires Python 3.6 and a conda environment. Users need to create the 'm2release' conda environment using the provided 'environment.yml' file and download spacy data by executing 'python -m spacy download en'.

What data is needed to run the Meshed-Memory Transformer code?

To run the code, you need annotations and detection features for the COCO dataset. This includes downloading the 'annotations.zip' file and the 'coco_detections.hdf5' file, which is approximately 53.5 GB and contains detection features for each image.

How can I reproduce the results from the Meshed-Memory Transformer paper?

To reproduce the paper's results, you must download the pretrained model file 'meshed_memory_transformer.pth' and place it in the code folder. Then, run 'python test.py' with specified arguments for batch size, workers, and paths to features and annotations.

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Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra Research & Education › Scientific Computing

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