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.ipynb_checkpoints/README-checkpoint.md

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+---
+license: cc-by-4.0
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: file_name
+      dtype: string
+    - name: total_count
+      dtype: int64
+    - name: num_classes
+      dtype: int64
+    - name: class_names
+      sequence: string
+    - name: class_counts
+      sequence: int64
+    - name: class_descriptions
+      sequence: string
+    - name: objects
+      struct:
+        - name: bbox
+          sequence:
+            sequence: float64
+        - name: category
+          sequence: int64
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: parquet_data/train-*
+      - split: validation
+        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: Bridging the Data Gap for Open-Vocabulary Object Counting
+
+<p align="center">
+  <a href="https://corentindumery.github.io/">Corentin Dumery*</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 | **MixCount** |
+|---|:---:|:---:|:---:|:---:|
+| Multiple object types per image | | ✓ | ✓ | **✓** |
+| Fine-grained text prompts | | ✓ | | **✓** |
+| External visual exemplars | | | | **✓** |
+| Segmentation & bounding boxes | | | ✓ | **✓** |
+| # images | 6,135 | 681 | 20K | **58,000** |
+| # object classes | 147 | 98 | 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, an AWS Studentship, the Reuben Foundation, a Qualcomm Innovation Fellowship (mentors: Dr Farhad Zanjani and Dr Davide Abati), and the AIMS CDT program at the University of Oxford.

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