| --- |
| license: mit |
| language: |
| - en |
| library_name: pytorch |
| pipeline_tag: zero-shot-object-detection |
| tags: |
| - CountGD++ |
| - computer-vision |
| - counting |
| - grounding-dino |
| - model_hub_mixin |
| - multi-modal |
| - open-vocabulary |
| - pytorch_model_hub_mixin |
| --- |
| |
| # CountGD++ |
|
|
| **CountGD++: Generalized Prompting for Open-World Counting** (CVPR 2026) |
|
|
| A multi-modal open-world counting model. You say what to count with text, |
| visual exemplars (boxes on example objects), or both. You can also say what |
| **not** to count with negative text and negative exemplars. Exemplars can come |
| from the image being counted or from a different image. |
|
|
| [Paper](https://arxiv.org/abs/2512.23351) · [Code](https://github.com/niki-amini-naieni/CountGDPlusPlus) |
|
|
|  |
|
|
| This repository holds both the **weights** (`model.safetensors`, `config.json`) |
| and the **inference code**, so nothing has to be downloaded from Google Drive. |
|
|
| ## Usage |
|
|
| ```bash |
| pip install torch torchvision "transformers<4.50" timm addict "yapf==0.40.1" termcolor \ |
| scipy matplotlib opencv-python-headless pycocotools pyyaml huggingface_hub safetensors |
| ``` |
|
|
| ```python |
| import sys |
| from huggingface_hub import snapshot_download |
| |
| repo = snapshot_download("nikigoli/CountGDPlusPlus") # code + weights |
| sys.path.insert(0, repo) |
| from countgdpp import CountGDPlusPlus, count |
| |
| model = CountGDPlusPlus.from_pretrained("nikigoli/CountGDPlusPlus").to("cuda").eval() |
| |
| # Text only |
| print(count(model, f"{repo}/img/336.jpg", "strawberry")["count"]) |
| |
| # Positive text + negative text |
| print(count(model, f"{repo}/img/336.jpg", "strawberry", negative_texts=["blueberry"])["count"]) |
| |
| # Text + visual exemplars drawn on another image, with a negative exemplar |
| result = count( |
| model, f"{repo}/img/336.jpg", "strawberry", |
| exemplar_boxes=[[x1, y1, x2, y2]], # pixels, on exemplar_image |
| exemplar_image=f"{repo}/img/synthetic_strawberry.jpg", |
| negative_texts=["blueberry"], |
| negative_exemplar_boxes=[[[x1, y1, x2, y2]]], # one list per negative text |
| negative_exemplar_images=[f"{repo}/img/synthetic_blueberry.jpg"], |
| ) |
| print(result["count"], result["boxes"]) # boxes: normalized (cx, cy, w, h) |
| ``` |
|
|
| `example.py` is a runnable version that also saves a picture of the detections. |
|
|
| Notes: |
| - The text encoder (`bert-base-uncased`) is fetched from the Hugging Face Hub |
| by `transformers` the first time the model loads. |
| - The compiled multi-scale deformable attention kernels are optional. Without |
| them the model uses an equivalent pure-PyTorch implementation on CPU or GPU. |
| To compile the kernels, see the GitHub repository. |
| - Pseudo-exemplars and adaptive cropping used for some benchmark results are not |
| part of `count()`. See the test scripts in the GitHub repository. |
|
|
| ## Architecture |
|
|
|  |
|
|
| ## Citation |
|
|
| ```bibtex |
| @InProceedings{AminiNaieni26b, |
| title={CountGD++: Generalized Prompting for Open-World Counting}, |
| author={Amini-Naieni, N. and Zisserman, A.}, |
| booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year={2026} |
| } |
| ``` |
|
|
