How to write a DLC Methods Section

How to write a DLC Methods Section#

Pose estimation using DeepLabCut

For body part tracking we used DeepLabCut (version 2.X.X) [Mathis et al, 2018, Nath et al, 2019]. Specifically, we labeled X number of frames taken from X videos/animals (then X% was used for training (default is 95%). We used a X-based neural network (i.e., X = ResNet-50, ResNet-101, MobileNetV2-0.35, MobileNetV2-0.5, MobileNetV2-0.75, MobileNetV2-1, EfficientNet ..X, dlcrnet_ms5, etc.)*** with default parameters* for X number of training iterations. We validated with X number of shuffles, and found the test error was: X pixels, train: X pixels (image size was X by X). We then used a p-cutoff of X (i.e. 0.9) to condition the X,Y coordinates for future analysis. This network was then used to analyze videos from similar experimental settings.

*If any defaults were changed in pose_config.yaml, mention them.

i.e. common things one might change:

  • the loader (options are default, imgaug, tensorpack, deterministic).

  • the post_dist_threshold (default is 17 and determines training resolution).

  • optimizer: do you use the default SGD or ADAM?

*** here, you could add additional citations. If you use ResNets, consider citing Insafutdinov et al 2016 & He et al 2016. If you use the MobileNetV2s consider citing Mathis et al 2021, and Sandler et al, 2018. If you use DLCRNet, please cite Lauer et al, 2021.

Mathis, A. et al. Deeplabcut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience 21, 1281–1289 (2018).

Nath, T. et al. Using deeplabcut for 3d markerless pose estimation across species and behaviors. Nature Protocols 14, 2152–2176 (2019).

Mathis, A. Biasi, T. et al. Pretraining boosts out-of-domain robustness for pose estimation. WACV (2021).

Lauer et al. Multi-animal pose estimation and tracking with DeepLabCut. BioRxiv (2021).

Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M. & Schiele, B. DeeperCut: A deeper, stronger, and faster multi-person pose estimation model. In European Conference on Computer Vision, 34–50 (Springer, 2016).

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510–4520 (2018).

He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778 (2016). URL https://arxiv.org/abs/ 1512.03385.

We also have the network graphic freely available on SciDraw.io if you’d like to use it! https://scidraw.io/drawing/290. If you use our DLC logo, please include the TM symbol, thank you!