Update README.md

README.md

@@ -73,9 +73,10 @@ Muskrat (Ondatra zibethicus), Brown Rat (Rattus norvegicus), House Mouse (Mus mu
 Cotton Rat (Sigmodon hispidus), Meadow Vole (Microtus pennsylvanicus), Bank Vole (Clethrionomys glareolus), Deer Mouse
 (Peromyscus maniculatus), White-footed Mouse (Peromyscus leucopus), Striped Field Mouse (Apodemus agrarius). We then
 generated segmentation masks over target animals in the data by processing the media through an algorithm we designed that
-uses a Mask Region Based Convolutional Neural Networks(Mask R-CNN) (8) model with a ResNet-50-FPN backbone (9),
-pretrained on the COCO datasets (10). The processed 443 images were then manually labeled with both pose annotations and
+uses a Mask Region Based Convolutional Neural Networks(Mask R-CNN) (9) model with a ResNet-50-FPN backbone (10),
+pretrained on the COCO datasets (11). The processed 443 images were then manually labeled with both pose annotations and
 segmentation masks. iRodent data is banked at https://zenodo.org/record/8250392.
+**APT-36K** See full details at (12). 
 
 Here is an image with the keypoint guide:
 <p align="center">
@@ -153,4 +154,6 @@ Conference on Neural Information Processing Systems Datasets and Benchmarks Trac
 vision, pages 2961–2969, 2017.
 10. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2016.
 11. Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll’ar,
-and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014
+and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014
+12. Yuxiang Yang, Junjie Yang, Yufei Xu, Jing Zhang, Long Lan, and Dacheng Tao. Apt-36k: A large-scale benchmark for animal pose estimation and
+tracking. Advances in Neural Information Processing Systems, 35:17301–17313, 2022