Datasets:

CorentinDumery
/

MixCount

@@ -1,207 +0,0 @@
----
-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.