CountGD: Multi-Modal Open-World Counting

Niki Amini-Naieni, Tengda Han, & Andrew Zisserman

Official PyTorch implementation for CountGD. Details can be found in the paper, [Paper] [Project page].

Try Using CountGD to Count with Text, Visual Exemplars, or Both Together Through the App [HERE].

CountGD Architecture

Preparation
CountGD Inference & Pre-Trained Weights
CountGD Train
Citation
Acknowledgements

Preparation

1. Download Dataset

In our project, the FSC-147 dataset is used. Please visit following link to download this dataset.

FSC-147

2. Install GCC

Install GCC. In this project, GCC 11.3 and 11.4 were tested. The following command installs GCC and other development libraries and tools required for compiling software in Ubuntu.

sudo apt update
sudo apt install build-essential

3. Clone Repository

git clone git@github.com:niki-amini-naieni/CountGD.git

4. Set Up Anaconda Environment:

The following commands will create a suitable Anaconda environment for running the CountGD training and inference procedures. To produce the results in the paper, we used Anaconda version 2024.02-1.

conda create -n countgd python=3.9.19
conda activate countgd
cd CountGD
pip install -r requirements.txt
export CC=/usr/bin/gcc-11 # this ensures that gcc 11 is being used for compilation
cd models/GroundingDINO/ops
python setup.py build install
python test.py # should result in 6 lines of * True
pip install git+https://github.com/facebookresearch/segment-anything.git
cd ../../../

5. Download Pre-Trained Weights

Make the checkpoints directory inside the CountGD repository.
```
mkdir checkpoints
```
Execute the following command.
```
python download_bert.py
```

Download the pretrained Swin-B GroundingDINO weights.

wget -P checkpoints https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha2/groundingdino_swinb_cogcoor.pth

Download the pretrained ViT-H Segment Anything Model (SAM) weights.

wget -P checkpoints https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

CountGD Inference & Pre-Trained Weights

The model weights used in the paper can be downloaded from Google Drive link (1.2 GB). To reproduce the results in the paper, run the following commands after activating the Anaconda environment set up in step 4 of Preparation. Make sure to change the directory and file names in datasets_fsc147_val.json and datasets_fsc147_test.json to the ones you set up in step 1 of Preparation. Make sure that the model file name refers to the model that you downloaded.

For the validation set (takes ~ 26 minutes on 1 RTX 3090 GPU):

python -u main_inference.py --output_dir ./countgd_val -c config/cfg_fsc147_val.py --eval --datasets config/datasets_fsc147_val.json --pretrain_model_path checkpoints/checkpoint_fsc147_best.pth --options text_encoder_type=checkpoints/bert-base-uncased --crop --sam_tt_norm --remove_bad_exemplar

For the validation set with no Segment Anything Model (SAM) test-time normalization and, hence, slightly reduced counting accuracy (takes ~ 6 minutes on 1 RTX 3090 GPU):

python -u main_inference.py --output_dir ./countgd_val -c config/cfg_fsc147_val.py --eval --datasets config/datasets_fsc147_val.json --pretrain_model_path checkpoints/checkpoint_fsc147_best.pth --options text_encoder_type=checkpoints/bert-base-uncased --crop --remove_bad_exemplar

For the test set (takes ~ 26 minutes on 1 RTX 3090 GPU):

python -u main_inference.py --output_dir ./countgd_test -c config/cfg_fsc147_test.py --eval --datasets config/datasets_fsc147_test.json --pretrain_model_path checkpoints/checkpoint_fsc147_best.pth --options text_encoder_type=checkpoints/bert-base-uncased --crop --sam_tt_norm --remove_bad_exemplar

For the test set with no Segment Anything Model (SAM) test-time normalization and, hence, slightly reduced counting accuracy (takes ~ 6 minutes on 1 RTX 3090 GPU):

python -u main_inference.py --output_dir ./countgd_test -c config/cfg_fsc147_test.py --eval --datasets config/datasets_fsc147_test.json --pretrain_model_path checkpoints/checkpoint_fsc147_best.pth --options text_encoder_type=checkpoints/bert-base-uncased --crop --remove_bad_exemplar

Note: Inference can be further sped up by increasing the batch size for evaluation

CountGD Train

Training code and instructions coming soon!

Citation

@article{AminiNaieni24,
  author = "Amini-Naieni, N. and Han, T. and Zisserman, A.",
  title = "CountGD: Multi-Modal Open-World Counting",
  booktitle = "arXiv",
  year = "2024",
}

Acknowledgements

This repository is based on the Open-GroundingDino and uses code from the GroundingDINO repository. If you have any questions about our code implementation, please contact us at niki.amini-naieni@eng.ox.ac.uk.

puruBHU/CountGD