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\n\n![](\"https://api.travis-ci.org/Cadene/bootstrap.pytorch.svg?branch=master\"/)
\n\n`Bootstrap` is a high-level framework for starting deep learning projects.\nIt aims at accelerating research projects and prototyping by providing a powerful workflow focused on your dataset and model only.\n\nAnd it is:\n\n- Scalable\n- Modular\n- Shareable\n- Extendable\n- Uncomplicated\n- Built for reproducibility\n- Easy to log and plot anything\n\nIt's not a wrapper over pytorch, it's a powerful extension.\n\n## Quick tour\n\nTo run an experiment (training + evaluation):\n```\npython -m bootstrap.run\n -o myproject/options/sgd.yaml\n```\n\nTo display parsed options from the yaml file:\n```\npython -m bootstrap.run\n -o myproject/options/sgd.yaml\n -h\n```\n\nRunning an experiment will create 4 files, here is an example with [mnist](https://github.com/Cadene/mnist.bootstrap.pytorch):\n\n- [options.yaml](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/options.yaml) contains the options used for the experiment,\n- [logs.txt](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/logs.txt) contains all the information given to the logger.\n- [logs.json](https://github.com/Cadene/bootstrap.pytorch/blob/master/docs/assets/logs/mnist/sgd/logs.json) contains the following data: train_epoch.loss, train_batch.loss, eval_epoch.accuracy_top1, etc.\n- view.html contains training and evaluation curves with javascript utilities (plotly).\n\n\nTo save the next experiment in a specific directory:\n```\npython -m bootstrap.run\n -o myproject/options/sgd.yaml\n --exp.dir logs/custom\n```\n\nTo reload an experiment:\n```\npython -m bootstrap.run\n -o logs/custom/options.yaml\n --exp.resume last\n```\n\n\n## Documentation\n\nThe package reference is available on the [documentation website](http://remicadene.com/bootstrap).\n\nIt also contains some notes:\n\n- [Installation](http://remicadene.com/bootstrap/#installation)\n- [Concepts](http://remicadene.com/bootstrap/concepts.html)\n- [Quickstart](http://remicadene.com/bootstrap/quickstart.html)\n- [Directories](http://remicadene.com/bootstrap/directories.html)\n- [Examples](http://remicadene.com/bootstrap/examples.html)\n\n## Official project modules\n\n- [mnist.bootstrap.pytorch](https://github.com/Cadene/mnist.bootstrap.pytorch) is a useful example for starting a quick project with bootstrap\n- [vision.bootstrap.pytorch](https://github.com/Cadene/vision.bootstrap.pytorch) contains utilities to train image classifier, object detector, etc. on usual datasets like imagenet, cifar10, cifar100, coco, visual genome, etc.\n- [recipe1m.bootstrap.pytorch](https://github.com/Cadene/recipe1m.bootstrap.pytorch) is a project for image-text retrieval related to the Recip1M dataset developped in the context of a [SIGIR18 paper](https://arxiv.org/abs/1804.11146).\n- [block.bootstrap.pytorch](https://github.com/Cadene/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](http://remicadene.com/pdfs/paper_aaai2019.pdf).\n\n## Poster\n\n![](\"http://remicadene.com/bootstrap/_static/img/bootstrap_poster_mini.png\")
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