diff --git a/.gitignore b/.gitignore
index 323f873..1396646 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,7 @@ old_data/*
 src/image2structure/compilation/webpage/test_data/valid_repo/_site/feed.xml
 tmp/*
 credentials/*
+experimental/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/README.md b/README.md
index 99942c3..519f18e 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,19 @@
-# Image2Structure - Data collection
+# Image2Struct
+[Paper](TODO) | [Website](https://crfm.stanford.edu/helm/image2structure/latest/) | Datasets ([Webpages](https://huggingface.co/datasets/stanford-crfm/i2s-webpage), [Latex](https://huggingface.co/datasets/stanford-crfm/i2s-latex), [Music sheets](https://huggingface.co/datasets/stanford-crfm/i2s-musicsheet)) | [Leaderboard](https://crfm.stanford.edu/helm/image2structure/latest/#/leaderboard) | [HELM repo](https://github.com/stanford-crfm/helm)
 
-This repository contains the data collection for the Image2Structure project.
+Welcome, the `image2struct` Python package contains code usied in the **Image2Struct: A Benchmark for Evaluating Vision-Language Models in Extracting Structured Information from Images** paper. This repo includes the following features:
+* Data collection: scrapers, filters, compilers, and uploaders for the different data types (Latex, Webpages, MusicSheets) from public sources (ArXiV, GitHub, IMSLP, ...)
+* Dataset upload: upload the datasets to the Hugging Face Datasets Hub
+* Wild data collection: collection of screenshots from webpages specified by a determined list of URLs and formatting of equations screenshots of your choice.
+
+This repo **does not** contain:
+* The evaluation code: the evaluation code is available in the [HELM repo](https://github.com/stanford-crfm/helm).
 
 ## Installation
-To install the package, you can use pip:
+To install the package, you can use `pip` and `conda`:
 
+    conda create -n image2struct python=3.9.18 -y
+    conda activate image2struct
     pip install -e ".[all]"
 
 Some formats require additional dependencies. To install all dependencies, use:
@@ -14,7 +23,69 @@ Some formats require additional dependencies. To install all dependencies, use:
 Finally, create a `.env` file by copying the `.env.example` file and filling in the required values.
 
 
-# Contributing
+## Usage
+
+### Data collection
+
+You can run `image2structure-collect` to collect data from different sources. For example, to collect data from GitHub Pages:
+
+    image2structure-collect --num_instances 300 --num_instances_at_once 50 --max_instances_per_date 40 --date_from 2024-01-01 --date_to 2024-02-20 --timeout 30 --destination_path data webpage --language css --port 4000 --max_size_kb 100
+
+The general arguments are:
+* `--num_instances`: the number of instances to collect
+* `--num_instances_at_once`: the number of instances to collect at once. This means that when the scraper is called, it won't ask the API used (here GitHub Developer API) for more than `num_instances_at_once` instances. This is useful to avoid hitting the rate limit.
+* `--max_instances_per_date`: the maximum number of instances to collect for a single date. This is useful to avoid collecting too many instances for a single date.
+* `--date_from`: the starting date to collect instances from.
+* `--date_to`: the ending date to collect instances from.
+* `--timeout`: the timeout in seconds for each instance collection.
+* `--destination_path`: the path to save the collected data to.
+
+Then you can add specific arguments for the data type you want to collect. To do so simply add the data type, here `webpage`, followed by the data-specific arguments. You can find the data-specific arguments in the `src/image2struct/run_specs.py` file.
+
+The script will save the collected data to the specified destination path under this format:
+
+    output_path
+    ├── subcategory1
+    │   ├── assets
+    │   ├── images
+    │   |   ├── uuid1.png
+    │   |   ├── uuid2.png
+    │   |   └── ...
+    │   ├── metadata
+    │   |   ├── uuid1.json
+    │   |   ├── uuid2.json
+    │   |   └── ...
+    │   ├── structures # Depends on the data type
+    │   |   ├── uuid1.{tex,tar.gz,...}
+    │   |   ├── uuid2.{tex,tar.gz,...}
+    │   |   └── ...
+    │   ├── (text) # Depends on the data type
+    │   |   ├── uuid1.txt
+    │   |   ├── uuid2.txt
+    │   |   └── ...
+    ├── subcategory2
+    └── ...
+        
+
+### Upload datasets
+
+Once you have collected some datasets, you can upload them to the Hugging Face Datasets Hub. For example, to upload the latex dataset:
+
+    image2structure-upload --data-path data/latex --dataset-name stanford-crfm/i2s-latex --max-instances 50
+
+This will upload the dataset to the Hugging Face Datasets Hub under the `stanford-crfm/i2s-latex` dataset name. The `max-instances` argument specifies the maximum number of instances to upload. The `--data-path` argument specifies the path to the dataset files. These files should respect the format outputed by the collection scripts.
+
+
+### Wild data collection
+
+There are two scripts to build the wild datasets: `src/image2struct/wildwild/wild_latex.py` and `src/image2struct/wildwild/wild_webpage.py`. You can simply run them to format the data (you will need to collect screenshots of equations manually for the `wild_latex` script while the `wild_webpage` will take screenshots of websites by itself):
+
+    python src/image2struct/wild/wild_webpage.py
+    python src/image2struct/wild/wild_latex.py
+
+You can then upload the datasets to the Hugging Face Datasets Hub as explained above.
+
+## Contributing
 To contribute to this project, first install the development dependencies:
 
     pip install -e ".[dev]"