README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: split
    dtype: string
  - name: image
    dtype: image
  - name: seg
    dtype: image
  - name: num_classes
    dtype: int64
  - name: inds
    sequence:
      sequence: int64
  - name: ids
    sequence: string
  - name: classes
    sequence: string
  - name: sizes
    sequence:
      sequence: float64
  - name: centers
    sequence:
      sequence:
        sequence: float64
  - name: occlusions
    sequence:
      sequence: int64
  - name: area
    sequence:
      sequence: int64
  - name: unoccluded_area
    sequence:
      sequence: int64
  - name: bboxes
    sequence:
      sequence:
        sequence:
          sequence: int64
  - name: centers_crop672
    sequence:
      sequence:
        sequence: float64
  - name: occlusions_crop672
    sequence:
      sequence: int64
  - name: area_crop672
    sequence:
      sequence: int64
  - name: bboxes_crop672
    sequence:
      sequence:
        sequence:
          sequence: int64
  splits:
  - name: val
    num_bytes: 2755568627.75
    num_examples: 2413
  - name: test
    num_bytes: 2361310977.25
    num_examples: 2115
  - name: train
    num_bytes: 5265670004.75
    num_examples: 4757
  download_size: 10255201099
  dataset_size: 10382549609.75
configs:
- config_name: default
  data_files:
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
  - split: train
    path: data/train-*
license: mit
---

# Multi-Class Class-Agnostic Counting Dataset
**[Project Page](https://MCAC.active.vision/) |
[ArXiv](https://arxiv.org/abs/2309.04820) |
[Download](https://www.robots.ox.ac.uk/~lav/Datasets/MCAC/MCAC.zip)
**

[Michael Hobley](https://scholar.google.co.uk/citations?user=2EftbyIAAAAJ&hl=en), 
[Victor Adrian Prisacariu](http://www.robots.ox.ac.uk/~victor/). 

[Active Vision Lab (AVL)](https://www.robots.ox.ac.uk/~lav/),
University of Oxford.


MCAC is the first multi-class class-agnostic counting dataset. each image contains between 1 and 4 classes of
object and between 1 and 300 objects per class.
The classes of objects present in the Train, Test and Val splits are mutually exclusive, and where possible
aligned with the class splits in [FSC-133](https://github.com/ActiveVisionLab/LearningToCountAnything).
Each object is labeled with an instance, class and model number as well as its center coordinate, bounding box
coordinates and its percentage occlusion
Models are taken from [ShapeNetSem]. The original model IDs and manually
verified category labels are preserved.
MCAC-M1 is the single-class images from MCAC. This is useful when comparing methods that are not suited to
multi-class cases.

## File Hierarchy 

File Hierarchy:

    ├── dataset_pytorch.py
    ├── make_gaussian_maps.py
    ├── test
    ├── train
    │   ├── 1511489148409439
    │   ├── 3527550462177290
    │   |   ├──img.png
    │   |   ├──info.json
    │   |   ├──seg.png
    │   ├──4109417696451021
    │   └── ...
    └── val

  
## Precompute Density Maps 
To precompute ground truth density maps for other resolutions, occlusion percentages, and gaussian standard deviations use the code from our [GitHub](https://github.com/ActiveVisionLab/MCAC):

```sh
cd PATH/TO/MCAC/
python make_gaussian_maps.py  --occulsion_limit <desired_max_occlusion>  --crop_size 672 --img_size <desired_resolution> --gauss_constant <desired_gaussian_std>;
```
## Pytorch Dataset
There is a pytorch dataset written on our [GitHub](https://github.com/ActiveVisionLab/MCAC). 
This randomises the bounding boxes durig training but uses consistent bounding boxes for testing.


## Citation
```
@article{hobley2023abc,
    title={ABC Easy as 123: A Blind Counter for Exemplar-Free Multi-Class Class-agnostic Counting}, 
    author={Michael A. Hobley and Victor A. Prisacariu},
    journal={arXiv preprint arXiv:2309.04820},
    year={2023},
}
```