Update README.md

README.md

@@ -37,3 +37,43 @@ tags:
 - art
 pretty_name: VisualOverload
 ---
+# VisualOverload
+<p align="center">
+<img src="https://github.com/paulgavrikov/visualoverload/blob/main/assets/logo.jpg?raw=true" width="400"> <br>
+</p>
+Is basic image understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question–answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free visual understanding and reasoning of details in densely populated (or, *overloaded*) scenes. Our dataset consists of high-resolution scans of public-domain paintings that are populated with multiple figures, actions, and unfolding subplots set against elaborately detailed backdrops. Questions were handcrafted to probe for a thorough understanding of the scene.
+
+
+
+## 📂 Load the dataset
+
+The easiest way to load the dataset is to use HuggingFace's `datasets`.
+
+```python
+from datasets import load_dataset
+
+vol_dataset = load_dataset("paulgavrikov/visualoverload")
+```
+
+Each sample contains the following fields
+
+- `question_id`: Unique identifier of each question. 
+- `image`: A PIL JPEG image. Most of our images match the total pixel count of 4k (3840x2160 px) in different aspect ratios. 
+- `question`: A question about the image.
+- `question_type`: Type of question. Will be one of `choice` (response expected to be "A", "B", "C", or "D"), `counting` (freeform), or `ocr` (freeform). You can use this information to request a suitable output format. 
+- `options`: This is the list of options for `question_type=choice` and empty otherwise. Please treat the options as answers options `A, B, C, D` (4 options) or `A, B` (2 options).
+- `difficulty`: Meta-data about the difficulty of the question. One of `easy`, `medium`, or `hard`.
+- `category`:  Meta-data about the question task. One of `activity`, `attributes`, `counting`, `ocr`, `reasoning`, or `scene`.
+- `default_prompt`: You can use this prompt to stay compliant with our results. It is a simple combination of the question and answers, with some additional output format constraints. This should work well for most models.
+
+## 🎯 Evaluate your model
+
+Please see [GitHub](https://github.com/paulgavrikov/visualoverload/) for an example evaluation script that generates a correct submission JSON.
+
+All of our ground truth labels are private. The only way to score your submission is to use the [evaluation server](https://huggingface.co/spaces/paulgavrikov/visualoverload-submit). You will need to sign in with a HuggingFace account.  
+
+Your predictions should be a list of dictionaries, each containing an `question_id` field and a `response` field. For multiple choice questions, the `response` field should contain the predicted answer choice. For open-ended questions, the `response` field should contain the option letter (A-D). We will apply simple heuristics to clean the responses, but please ensure they are as accurate as possible.
+
+
+## 🏆 Submit to the leaderboard
+We welcome all submissions for model *or* method (including prompting-based) to our dataset. Please create a [GitHub issue](https://github.com/paulgavrikov/visualoverload/issues) following the template and include your predictions as JSON.