Safetensors
Transformers
English
CountGD
computer-vision
counting
grounding-dino
model_hub_mixin
multi-modal
open-vocabulary
pytorch_model_hub_mixin
Instructions to use nikigoli/CountGD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikigoli/CountGD with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nikigoli/CountGD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: | |
| - en | |
| library_name: CountGD | |
| license: mit | |
| tags: | |
| - computer-vision | |
| - counting | |
| - grounding-dino | |
| - model_hub_mixin | |
| - multi-modal | |
| - open-vocabulary | |
| - pytorch_model_hub_mixin | |
| - transformers | |
| # CountGD | |
| A Multi-Modal Open-World Counting Model for counting objects in an image with text and image prompts. | |
| For more details, please check out the following links | |
| - Project page: https://www.robots.ox.ac.uk/~vgg/research/countgd/ | |
| - Code: https://github.com/niki-amini-naieni/CountGD | |
| - Demo: https://huggingface.co/spaces/nikigoli/countgd | |
| - Paper: https://arxiv.org/pdf/2407.04619 | |
|  | |
| ## Architecture | |
|  | |
| ## Citation | |
| ``` | |
| @inproceedings{AminiNaieni24, | |
| author = "Amini-Naieni, N. and Han, T. and Zisserman, A.", | |
| title = "CountGD: Multi-Modal Open-World Counting", | |
| booktitle = "NeurIPS", | |
| year = "2024", | |
| } | |
| ``` |