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High level framework for starting Deep Learning projects

Project description

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`Bootstrap` is a high-level framework for starting deep learning projects.
It aims at accelerating research projects and prototyping by providing a powerful workflow focused on your dataset and model only.

And it is:

- Scalable
- Modular
- Shareable
- Extendable
- Uncomplicated
- Built for reproducibility
- Easy to log and plot anything

It's not a wrapper over pytorch, it's a powerful extension.

## Quick tour

To run an experiment (training + evaluation):
python -m
-o myproject/options/sgd.yaml

To display parsed options from the yaml file:
python -m
-o myproject/options/sgd.yaml

Running an experiment will create 4 files, here is an example with [mnist](

- [options.yaml]( contains the options used for the experiment,
- [logs.txt]( contains all the information given to the logger.
- [logs.json]( contains the following data: train_epoch.loss, train_batch.loss, eval_epoch.accuracy_top1, etc.
- <a href="">view.html</a> contains training and evaluation curves with javascript utilities (plotly).

To save the next experiment in a specific directory:
python -m
-o myproject/options/sgd.yaml
--exp.dir logs/custom

To reload an experiment:
python -m
-o logs/custom/options.yaml
--exp.resume last

## Documentation

The package reference is available on the [documentation website](

It also contains some notes:

- [Installation](
- [Concepts](
- [Quickstart](
- [Directories](
- [Examples](

## Official project modules

- [mnist.bootstrap.pytorch]( is a useful example for starting a quick project with bootstrap
- [vision.bootstrap.pytorch]( contains utilities to train image classifier, object detector, etc. on usual datasets like imagenet, cifar10, cifar100, coco, visual genome, etc.
- [recipe1m.bootstrap.pytorch]( is a project for image-text retrieval related to the Recip1M dataset developped in the context of a [SIGIR18 paper](
- [block.bootstrap.pytorch]( is a project focused on fusion modules related to the VQA 2.0, TDIUC and VRD datasets developped in the context of a [AAAI19 paper](

## Poster

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