Update README.md

Author: Negiiiin

README.md

@@ -6,6 +6,12 @@
 [![integration tests](https://github.com/VectorInstitute/aieng-template/actions/workflows/integration_tests.yml/badge.svg)](https://github.com/VectorInstitute/pmc-data-extraction/actions/workflows/integration_tests.yml)
 [![license](https://img.shields.io/github/license/VectorInstitute/aieng-template.svg)](https://github.com/VectorInstitute/pmc-data-extraction/blob/main/LICENSE.md)
 
+<div align="center">
+    <img src="https://github.com/VectorInstitute/pmc-data-extraction/blob/0a969136344a07267bb558d01f3fe76b36b93e1a/media/open-pmc-pipeline.png?raw=true" 
+     alt="Open-PMC Pipeline" 
+     width="1000" />
+</div>
+
 A toolkit to download, augment, and benchmark Open-PMC; a large dataset of image-text pairs extracted from open-access scientific articles on PubMedCentral.
 
 For more details, see the following resources:
@@ -34,110 +40,112 @@ for dependency management. Please make sure it is installed.
 Then, follow below instructions to set up your virtual environment.
 
 1. Create a venv with python3.10 and activate it.
-bash
+```bash
 python --version  # must print 3.10
 python -m venv <your-venv-name>
 source <your-venv-name>/bin/activate
-
+```
 
 2. Navigate to the root directory of pmc-data-extraction repository and install dependencies.
 Two of the required dependencies are [mmlearn](https://github.com/VectorInstitute/mmlearn) and [open_clip](https://github.com/mlfoundations/open_clip).
-You have the option to either install them with pip or from source.
+You have the option to either install them with `pip` or from source.
 
-To install mmlearn and open_clip with pip, run
-bash
+To install `mmlearn` and `open_clip` with `pip`, run
+```bash
 cd path/to/pmc-data-extraction
 pip install --upgrade pip
 poetry install --no-root --with test,open_clip,mmlearn --all-extras
-
+```
 then skip to step 6: Check Installations.
 
-To install mmlearn and open_clip from source, run
-bash
+To install `mmlearn` and `open_clip` from source, run
+```bash
 cd path/to/pmc-data-extraction
 pip install --upgrade pip
 poetry install --no-root --with test --all-extras
+```
+The above command assumes that you would install `mmlearn` or `open_clip` packages from source using the submodules found in `pmc-data-extraction/openpmcvl/`experiment.
 
-The above command assumes that you would install mmlearn or open_clip packages from source using the submodules found in pmc-data-extraction/Open-PMCvl/experiment.
-
-3. Clone mmlearn and open_clip submodules.
-bash
+3. Clone `mmlearn` and `open_clip` submodules.
+```bash
 git submodule init
 git submodule update
+```
+You should see the source files inside `pmc-data-extraction/openpmcvl/experiment/open_clip` and `pmc-data-extraction/openpmcvl/experiment/mmlearn`.
 
-You should see the source files inside pmc-data-extraction/Open-PMCvl/experiment/open_clip and pmc-data-extraction/Open-PMCvl/experiment/mmlearn.
-
-4. Install mmlearn from source.
-bash
-cd Open-PMCvl/experiment/mmlearn
+4. Install `mmlearn` from source.
+```bash
+cd openpmcvl/experiment/mmlearn
 python3 -m pip install -e .
+```
 
-
-5. Install open_clip from source.
-bash
+5. Install `open_clip` from source.
+```bash
 cd ../open_clip
 make install
 make install-training
-
+```
 
 6. Check installations.
-bash
+```bash
 pip freeze | grep mmlearn
 pip freeze | grep open_clip
 python
 > import mmlearn
 > import open_clip
 > mmlearn.__file__
 > open_clip.__file__
+```
 
+**Note:** Since these submodules (`mmlearn` and `open_clip`) are only part of the main branch in a single repository, if you change your branch to a branch where these submodules don't exist, your python interpretor won't be able to find these packages and you will face errors.
 
-**Note:** Since these submodules (mmlearn and open_clip) are only part of the main branch in a single repository, if you change your branch to a branch where these submodules don't exist, your python interpretor won't be able to find these packages and you will face errors.
 
 ## Download and parse image-caption pairs from Pubmed Articles
-The codebase used to download Pubmed articles and parse image-text pairs from them is stored in Open-PMCvl/foundation.
+The codebase used to download Pubmed articles and parse image-text pairs from them is stored in `openpmcvl/foundation`.
 This codebase heavily relies on [Build PMC-OA](https://github.com/WeixiongLin/Build-PMC-OA) codebase[[1]](#1).
 To download and parse articles with licenses that allow commercial use, run
-bash
+```bash
 # activate virtual environment
 source /path/to/your/venv/bin/activate
 # navigate to root directory of the package
-cd Open-PMCvl/foundation
+cd openpmcvl/foundation
 # download all 11 volumes with commercailly usable license
 python -u src/fetch_oa.py --num-retries 5 --extraction-dir path/to/download/directory/commercial --license-type comm --volumes 0 1 2 3 4 5 6 7 8 9 10 11
-
+```
 To download and parse open-access articles which are not allowed commercial use, run
-bash
+```bash
 python -u src/fetch_oa.py --num-retries 5 --extraction-dir path/to/download/directory/noncommercial --license-type noncomm --volumes 1 2 3 4 5 6 7 8 9 10 11
-
+```
 To download and parse open-access articles which other licenses than what is mentioned above, run
-bash
+```bash
 python -u src/fetch_oa.py --num-retries 5 --extraction-dir path/to/download/directory/other --license-type other --volumes 0 1 2 3 4 5 6 7 8 9 10 11
+```
 
 
 ## Run Benchmarking Experiments
-We use mmlearn to run benchmarking experiments.
-Many experiments can be run with our dataset and mmlearn.
+We use `mmlearn` to run benchmarking experiments.
+Many experiments can be run with our dataset and `mmlearn`.
 A simple example of training with our dataset is given below:
-bash
+```bash
 # navigate to root directory of the repository
 cd pmc-data-extraction
 # set pythonpath
 export PYTHONPATH="./"
 # run training experiment
 mmlearn_run \
-    'hydra.searchpath=[pkg://Open-PMCvl.experiment.configs]' \
+    'hydra.searchpath=[pkg://openpmcvl.experiment.configs]' \
     +experiment=pmcoa2_matched \
     experiment_name=pmcoa2_matched_train \
     dataloader.train.batch_size=256 \
     task.encoders.text.pretrained=False \
     task.encoders.rgb.pretrained=False
-
+```
 
 Four downstream evaluation experiments can be run with checkpoints generated during training: cross-modal retrieval, zero-shot classification, linear probing, and patient-to-patient retrieval.
 An example of cross-modal retrieval on the MIMIC-IV-CXR dataset is given below:
-bash
+```bash
 mmlearn_run \
-    'hydra.searchpath=[pkg://Open-PMCvl.experiment.configs]' \
+    'hydra.searchpath=[pkg://openpmcvl.experiment.configs]' \
     +experiment=pmcoa2_matched \
     experiment_name=pmcoa2_matched_retrieval_mimic \
     job_type=eval \
@@ -148,9 +156,10 @@ mmlearn_run \
     datasets.test.mimic.transform.job_type=eval \
     dataloader.test.batch_size=64 \
     resume_from_checkpoint="path/to/model/checkpoint"
+```
+For more comprehensive examples of shell scripts that run various experiments with Open-PMC, refer to `openpmcvl/experiment/scripts`.
+For more information about `mmlearn`, please refer to the package's [official codebase](https://github.com/VectorInstitute/mmlearn).
 
-For more comprehensive examples of shell scripts that run various experiments with Open-PMC, refer to Open-PMCvl/experiment/scripts.
-For more information about mmlearn, please refer to the package's [official codebase](https://github.com/VectorInstitute/mmlearn).
 
 ## Citation
 If you find the code useful for your research, please consider citing