This repo introduces a method for implementing the FamNet on a large image set where it is inconvenient to manually select exemplars for every images. It also contains the experiemnts conducted for investigating the quality of the counting result. FamNet is based from this paper:
Learning To Count Everything
Viresh Ranjan, Udbhav Sharma, Thu Nguyen and Minh Hoai
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Link to arxiv preprint: https://arxiv.org/pdf/2104.08391.pdf
The notebooks, "EXP_9_Batches" and "EXP_1_Batch", shows the experiments
of the counting pipeline. The pipeline modules are saved in the package
BubbleCount.
The directories Exemplars, Targets, and Outputs are used in the pipeline