| --- |
| tags: |
| - object-counting |
| pipeline_tag: object-detection |
| --- |
| ## Overview |
|
|
| Model overview: |
| The model uses 384 x 384 training crops, a class-token-free |
| ViT-B/16, intermediate blocks `[2, 5, 8, 11]`, and a 64-channel stride-four |
| refined feature map. FeatUp-style JBU restores image-aligned detail before the |
| proposal and verification heads. |
|
|
| Training follows the CounTR-length two-stage FSC-147 schedule, followed by |
| optional CARPK fine-tuning. Learning rates are linearly scaled as |
| `base_lr * effective_batch_size / 256`, with AdamW and cosine decay. |
|
|
| | Stage | Epochs | Batch | Base LR | Single-GPU LR | Warm-up | Weight decay | |
| |---|---:|---:|---:|---:|---:|---:| |
| | FSC-147 MAE pretraining | 500 | 8 | 1.5e-4 | 4.6875e-6 | 10 | 0.05 | |
| | FSC-147 counting fine-tuning | 1000 | 26 | 2.0e-4 | 2.03125e-5 | 10 | 0.05 | |
| | CARPK counting fine-tuning | 1000 | 8 | 2.0e-4 | 6.25e-6 | 10 | 0.05 | |
|
|
|
|
| Full paper is available on [arXiv](https://arxiv.org/abs/2607.16826), and code available on [GitHub](https://github.com/r28112072-rgb/upcount). |
|
|
| ## Citation |
|
|
| ``` |
| @article{wijaya2026spatially, |
| title={Spatially-Aware Class-Agnostic Object Counting}, |
| author={Wijaya, Robert and Hossain, Md Tanvir and Kau, Amanda and Cheung, Ngai-Man}, |
| journal={arXiv preprint arXiv:2607.16826}, |
| year={2026} |
| } |
| ``` |
