Update README.md

README.md

@@ -75,4 +75,69 @@ configs:
     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},
+}
+```