Update README.md

README.md

@@ -25,11 +25,175 @@ configs:
       path: "parquet_data/val-*"
     - split: test
       path: "parquet_data/test-*"
+tags:
+- counting
+- synthetic
+- computer-vision
+- open-vocabulary
+- segmentation
+pretty_name: MixCount
+task_categories:
+- image-classification
+- object-detection
 ---
-# The MixCount Dataset
-### Check out the [**Project page**](https://corentindumery.github.io/projects/mixcount.html) 
 
-This dataset contains v1-0 of the MixCount dataset, a large-scale and challenging dataset for fine-grained counting. 
-It includes images of objects to count, text and visual prompts to query specific objects, and high-quality dense ground-truth annotations.
-MixCount is designed to be a powerful training dataset, with high diversity, scale, and realism, and reduces visual counting error by around 20% in our experiments.
+# The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting
 
+<p align="center">
+  <a href="https://corentindumery.github.io/"><strong>Corentin Dumery*</strong></a> · <a href="https://reuben.ox.ac.uk/people/niki-amini-naieni">Niki Amini-Naieni*</a> · <a href="https://www.linkedin.com/in/shervin-n">Shervin Naini</a> · <a href="https://people.epfl.ch/pascal.fua">Pascal Fua</a>
+  <br>
+  <sub>EPFL · University of Oxford · Northwestern University · (* equal contribution)</sub>
+</p>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2605.18063"><img src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge" alt="Paper"></a>
+  <a href="https://corentindumery.github.io/projects/mixcount.html"><img src="https://img.shields.io/badge/Project-Page-143038?style=for-the-badge" alt="Project page"></a>
+</p>
+
+<style>
+.mixcount-stats {
+  display: grid;
+  grid-template-columns: repeat(5, minmax(0, 1fr));
+  gap: 10px;
+  max-width: 900px;
+  margin: 0 auto 1.5em;
+}
+.mixcount-stat {
+  background: linear-gradient(180deg, #f8fbfc 0%, #eef4f7 100%);
+  border: 1px solid #d8e4ea;
+  border-radius: 10px;
+  padding: 0.85em 0.5em;
+  text-align: center;
+}
+.mixcount-stat b {
+  display: block;
+  font-size: 1.35em;
+  color: #143038;
+}
+.mixcount-stat span {
+  display: block;
+  margin-top: 0.3em;
+  font-size: 0.82em;
+  color: #4f6468;
+  line-height: 1.3;
+}
+.mixcount-lead {
+  max-width: 820px;
+  margin: 0 auto 1.5em;
+  padding: 1em 1.15em;
+  background: #f7fafb;
+  border-left: 4px solid #5ba4c4;
+  border-radius: 0 8px 8px 0;
+  line-height: 1.55;
+}
+.mixcount-compare-wrap {
+  display: flex;
+  justify-content: center;
+  margin: 0 auto 1.25em;
+  overflow-x: auto;
+}
+.mixcount-compare-wrap table {
+  margin: 0 auto;
+}
+@media (max-width: 720px) {
+  .mixcount-stats { grid-template-columns: repeat(2, 1fr); }
+}
+</style>
+
+<p align="center">
+  <img src="images/teaser.png" width="96%" alt="MixCount sample scenes (2×6 grid)">
+</p>
+
+<div class="mixcount-lead">
+  <b>MixCount</b> is a large-scale synthetic dataset for <b>mixed-object, open-vocabulary counting</b>, the setting that dominates industrial inspection and sorting, but breaks current counting models. Our automatic generation pipeline produces pixel-perfect labels, text prompts at several levels of detail, and visual exemplars at scale.
+</div>
+
+<div class="mixcount-stats">
+  <div class="mixcount-stat"><b>58K</b><span>counting scenes</span></div>
+  <div class="mixcount-stat"><b>1,522</b><span>object classes</span></div>
+  <div class="mixcount-stat"><b>4M+</b><span>counting instances</span></div>
+  <div class="mixcount-stat"><b>−18.3%</b><span>MAE on PairTally (train)</span></div>
+  <div class="mixcount-stat"><b>−20.14%</b><span>MAE on FSC-147 (train)</span></div>
+</div>
+
+## Usage
+
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+
+ds = load_dataset("CorentinDumery/MixCount", split="train[:1]")
+
+sample = ds[0]
+print(sample["file_name"], sample["total_count"], "objects,", sample["num_classes"], "classes")
+
+plt.imshow(sample["image"])
+plt.axis("off")
+plt.show()
+```
+
+## Overview
+
+Object counting models often struggle in **mixed-object scenes**. Common failure modes include:
+
+- **(a)** Distinguishing **visually similar objects** (e.g. *big marbles* in PairTally)
+- **(b)** Recognizing **self-similar components** as a single entity (e.g. counting pairs of sunglasses rather than lenses)
+- **(c)** Ignoring **repetitive background patterns** and focusing on the queried object class
+
+MixCount combines the scale of synthetic datasets with the photorealism of real-world 3D captures while targeting these failure modes. Training on MixCount yields about **20% lower error** on recent open-vocabulary counting benchmarks.
+
+<p align="center">
+  <img src="images/bridging.png" width="68%" alt="Training on MixCount improves CountGD++ on PairTally, FSC-147, and MixCount">
+</p>
+
+## Dataset overview
+
+<div class="mixcount-compare-wrap">
+
+| | FSC-147 | PairTally | MCAC / SITUATE | **MixCount** |
+|---|:---:|:---:|:---:|:---:|
+| Multiple object types per image | | ✓ | ✓ | **✓** |
+| Fine-grained text prompts | | ✓ | | **✓** |
+| External visual exemplars | | | | **✓** |
+| Segmentation & bounding boxes | | | ✓ | **✓** |
+| # images | 6,135 | 681 | 6.9K–20K | **58,000** |
+| # object classes | 147 | 98 | 4–343 | **1,522** |
+
+</div>
+
+
+**Visual & text inputs.** Multiple visual exemplars per object (external crops and in-scene crops at different scales), together with **short, concise, and detailed** text descriptions for flexible open-vocabulary counting prompts.
+
+<p align="center">
+  <img src="images/features.png" width="65%" alt="MixCount exemplars and tiered text descriptions">
+</p>
+
+**Dense annotations.** Pixel-perfect counting supervision plus instance and class segmentations, bounding boxes, depth, and normal maps.
+
+<p align="center">
+  <img src="images/annotations.png" width="65%" alt="MixCount dense annotations">
+</p>
+
+**Automatic generator.** Objects, distractors, environment, and camera placement are sampled procedurally to create photorealistic training scenes from high-quality real-world captures of objects, materials, and lighting.
+
+<p align="center">
+  <img src="images/data_generator.png" width="65%" alt="MixCount data generation pipeline">
+</p>
+
+
+See the [project page](https://corentindumery.github.io/projects/mixcount.html) and [paper](https://arxiv.org/abs/2605.18063) for additional details.
+
+## Citation
+
+```bibtex
+@article{dumery2026mixcount,
+  title   = {The MixCount Dataset: Bridging the Data Gap for Open-Vocabulary Object Counting},
+  author  = {Dumery, Corentin and Amini-Naieni, Niki and Naini, Shervin and Fua, Pascal},
+  journal = {arXiv preprint arXiv:2605.18063},
+  year    = {2026}
+}
+```
+
+## Acknowledgements
+
+We thank [DTC](https://www.projectaria.com/datasets/dtc/), [VasTextures](https://sites.google.com/view/infinitexture/home), [LavalIndoor](http://hdrdb.com/indoor/), and [PolyHaven](https://polyhaven.com/), as well as the [Blender Foundation](https://www.blender.org/). We also thank Andrew Zisserman for insightful discussions. This work is partially funded by the Swiss National Science Foundation.