MCAC: A Multi-Class Class-Agnostic Counting Dataset

Michael A. Hobley, Victor A. Prisacariu
University of Oxford

Paper Download Method

MCAC

MCAC Example. Each object in the RGB image has an associated: Model ID, Class ID, Center Coordinate, Bounding Box and Occlusion Percentage.

More examples can be seen here.


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.
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.

Statistic

Count Values Num Classes
# Classes Min Median Mean Max Min Median Mean Max
MCAC
Train 8298 287 1 23 46.87 298 1 2 1.75 4
Val 4286 37 1 24 46.52 297 1 2 1.78 4
Test 3640 19 1 25 50.80 296 1 2 1.72 4
Total 16224 343 1 23 47.66 298 1 2 1.75 4
MCAC-M1
Train 2186 287 5 102 112.42 298 1 1 1 1
Val 1060 37 3 96 110.56 297 1 1 1 1
Test 1013 19 1 117 124.89 291 1 1 1 1
Total 4259 343 1 102 114.89 298 1 1 1 1



Benchmark Results

Val Test
Method Shots MAE RMSE NAE SRE MAE RMSE NAE SRE
Mean N/A 39.87 53.56 3.07 11.40 42.67 59.68 2.79 10.93
Median N/A 36.25 58.15 1.51 6.70 39.81 65.36 1.38 6.73
FamNet 3 24.76 41.12 1.12 6.86 26.40 45.52 1.04 6.87
BMNet 3 15.83 27.07 0.71 4.97 17.29 29.83 0.75 6.08
CounTR 3 15.07 26.26 0.63 4.79 16.12 29.28 0.67 5.71
ABC123 0 8.96 15.93 0.29 2.02 9.52 17.64 0.28 2.23

File hierarchy

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

Precomputing Denisty-Maps

To precompute Gaussian density maps of a different resolution or standard deviation run the following. 

    cd PATH/TO/MCAC/
    python make_gaussian_maps.py  --occulsion_limit   --crop_size 672 --img_size  --gauss_constant ;

Citation

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