From 6785b839cc1462968f3246ef05047a787c1daf1b Mon Sep 17 00:00:00 2001 From: ioangatop Date: Wed, 20 Mar 2024 13:32:46 +0100 Subject: [PATCH] Update main README (#325) --- README.md | 138 +++++++++++++++++++++++++++--------- docs/images/eva-process.gif | Bin 0 -> 1602073 bytes pyproject.toml | 20 +++++- 3 files changed, 122 insertions(+), 36 deletions(-) create mode 100644 docs/images/eva-process.gif diff --git a/README.md b/README.md index 73d2dea9..cd37a968 100644 --- a/README.md +++ b/README.md @@ -6,72 +6,120 @@ _Oncology FM Evaluation Framework by kaiko.ai_ - - - - - -
-
+[![PyPI](https://img.shields.io/pypi/v/kaiko-eva.svg?logo=python)](https://pypi.python.org/pypi/kaiko-eva) +[![CI](https://github.com/kaiko-ai/eva/workflows/CI/badge.svg)](https://github.com/kaiko-ai/eva/actions?query=workflow%3ACI) +[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg?labelColor=gray)](https://github.com/kaiko-ai/eva#license)

InstallationHow To Use • - Datasets • - Contribute + Documentation • + Datasets • + Benchmarks
+ Contribute • + Acknowledgements

---- +### Introduction + +_`eva`_ is an evaluation framework for oncology foundation models (FMs) by [kaiko.ai](https://kaiko.ai/). Check out the [documentation](https://kaiko-ai.github.io/eva/) for more information. + +
-### _About_ + -`eva` is [kaiko.ai](https://kaiko.ai/)'s evaluation framework for oncology foundation models (FMs). Check out the documentation (LINK TO BE ADDED) for more information. +
+### Highlights: +- Easy and reliable benchmark of Oncology FMs +- Automatic embedding inference and evaluation of a downstream task +- Native support of popular medical [datasets](https://kaiko-ai.github.io/eva/dev/datasets/) and models +- Produce statistics over multiple evaluation fits and multiple metrics ## Installation -*Note: this section will be revised for the public package when publishing eva* +Simple installation from PyPI: +```sh +# to install the core version only +pip install kaiko-eva -- Create and activate a virtual environment with Python 3.10+ +# to install the expanded `vision` version +pip install 'kaiko-eva[vision]' -- Install *eva* and the *eva-vision* package with: +# to install everything +pip install 'kaiko-eva[all]' +``` +To install the latest version of the `main` branch: +```sh +pip install "kaiko-eva[vision] @ git+https://github.com/kaiko-ai/eva.git" ``` -pip install 'kaiko-eva[vision]' + +You can verify that the installation was successful by executing: +```sh +eva --version ``` -- To be able to use the existing configs, download them from the [*eva* GitHub repo](https://github.com/kaiko-ai/eva/tree/main) and move them to directory where you installed *eva*. +## How To Use -### Run *eva* +_eva_ can be used directly from the terminal as a CLI tool as follows: +```sh +eva {fit,predict,predict_fit} --config url/or/path/to/the/config.yaml +``` -Now you can run a complete *eva* workflow, for example with: +For example, to perform a downstream evaluation of DINO ViT-S/16 on the BACH dataset with linear probing by first inferring the embeddings and performing 5 sequential fits, execute: +```sh +eva predict_fit --config https://raw.githubusercontent.com/kaiko-ai/eva/main/configs/vision/dino_vit/offline/bach.yaml ``` -eva fit --config configs/vision/dino_vit/online/bach.yaml + +> [!NOTE] +> All the datasets that support automatic download in the repo have by default the option to automatically download set to false. For automatic download you have to manually set download=true. + + +To view all the possibles, execute: +```sh +eva --help ``` -This will: - - Download and extract the dataset, if it has not been downloaded before. - - Fit a model consisting of the frozen FM-backbone and a classification head on the train split. - - Evaluate the trained model on the validation split and report the results. +For more information, please refer to the [documentation](https://kaiko-ai.github.io/eva/dev/user-guide/tutorials/offline_vs_online/) and [tutorials](https://kaiko-ai.github.io/eva/dev/user-guide/advanced/replicate_evaluations/). + +## Benchmarks + +In this section you will find model benchmarks which were generated with _eva_. + +### Table I: WSI patch-level benchmark -For more information, documentation and tutorials, refer to the documentation (LINK TO BE ADDED). +
-## Datasets +
-The following datasets are supported natively: +| Model | BACH | CRC | MHIST | PCam/val | PCam/test | +|--------------------------------------------------|-------|-------|-------|----------|-----------| +| ViT-S/16 _(random)_ [1] | 0.410 | 0.617 | 0.501 | 0.753 | 0.728 | +| ViT-S/16 _(ImageNet)_ [1] | 0.695 | 0.935 | 0.831 | 0.864 | 0.849 | +| ViT-B/8 _(ImageNet)_ [1] | 0.797 | 0.943 | 0.828 | 0.903 | 0.893 | +| DINO(p=16) [2] | 0.710 | 0.935 | 0.814 | 0.870 | 0.856 | +| Phikon [3] | 0.725 | 0.935 | 0.777 | 0.912 | 0.915 | +| ViT-S/16 _(kaiko.ai)_ [4] | 0.797 | 0.943 | 0.828 | 0.903 | 0.893 | +| ViT-S/8 _(kaiko.ai)_ [4] | 0.834 | 0.946 | 0.832 | 0.897 | 0.887 | +| ViT-B/16 _(kaiko.ai)_ [4] | 0.810 | 0.960 | 0.826 | 0.900 | 0.898 | +| ViT-B/8 _(kaiko.ai)_ [4] | 0.865 | 0.956 | 0.809 | 0.913 | 0.921 | +| ViT-L/14 _(kaiko.ai)_ [4] | 0.870 | 0.930 | 0.809 | 0.908 | 0.898 | + +_Table I: Linear probing evaluation of FMs on patch-level downstream datasets.
We report averaged balanced accuracy +over 5 runs_, with an average standard deviation of ±0.003. -### Vision +
-#### Patch-level pathology datasets: - - [BACH](./docs/datasets/bach.md) - - [CRC](./docs/datasets/crc.md) - - [MHIST](./docs/datasets/mhist.md) - - [PatchCamelyon](./docs/datasets/patch_camelyon.md) +
-#### Radiology datasets: - - [TotalSegmentator](./docs/datasets/total_segmentator.md) +_References_: +1. _"Emerging properties in self-supervised vision transformers”_ +2. _"Benchmarking self-supervised learning on diverse pathology datasets”_ +3. _"Scaling self-supervised learning for histopathology with masked image modeling”_ +4. _"Towards training Large-Scale Medical Foundation Models: from TCGA to hospital-scale pathology FMs”_ ## Contributing @@ -79,6 +127,26 @@ _eva_ is an open source project and welcomes contributions of all kinds. Please All contributors must follow the [code of conduct](./docs/CODE_OF_CONDUCT.md). + +## Acknowledgements + +Our codebase is built using multiple opensource contributions + +
+ +[![python](https://img.shields.io/badge/-Python-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit) +[![pytorch](https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/) +[![lightning](https://img.shields.io/badge/-⚡️_Lightning-792ee5?logo=pytorchlightning&logoColor=white)](https://pytorchlightning.ai/)
+[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/) +[![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) +[![Checked with pyright](https://microsoft.github.io/pyright/img/pyright_badge.svg)](https://microsoft.github.io/pyright/)
+[![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm-project.org) +[![Nox](https://img.shields.io/badge/%F0%9F%A6%8A-Nox-D85E00.svg)](https://github.com/wntrblm/nox) +[![Built with Material for MkDocs](https://img.shields.io/badge/Material_for_MkDocs-526CFE?logo=MaterialForMkDocs&logoColor=white)](https://squidfunk.github.io/mkdocs-material/) + +
+ ---
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"foundation-models", +] +classifiers = [ + "Topic :: Software Development :: Build Tools", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] readme = "README.md" authors = [ { name = "Ioannis Gatopoulos", email = "ioannis@kaiko.ai" }, @@ -32,6 +45,11 @@ dependencies = [ "toolz>=0.12.1", ] +[project.urls] +Homepage = "https://kaiko-ai.github.io/eva/dev/" +Repository = "https://github.com/kaiko-ai/eva" +Documentation = "https://kaiko-ai.github.io/eva/dev/" + [project.license] file = "LICENSE"

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