This is the repository of the paper "Grasp-Anything: Large-scale Grasp Dataset from Foundation Models"

1. Installation
2. Datasets
3. Training
4. Testing

• Create a virtual environment

$ conda create -n granything python=3.9
$ conda activate granything

• Install pytorch

$ conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
$ pip install -r requirements.txt

• Grasp-Anything will be made publicly available soon. LVIS splits of all datasets will also be included along with the release of Grasp-Anything.
• For other datasets, please obtain following their instructions: Cornell, Jacquard, OCID-grasp, and VMRD.
• All datasets should be include in the following hierarchy:

|- data/
    |- cornell
    |- grasp-anything
    |- jacquard
    |- OCID_grasp
    |- VMRD

We use GR-ConvNet as our default deep network. To train GR-ConvNet on different datasets, you can use the following command:

$ python train_network.py --dataset <dataset> --dataset-path <dataset> --description <your_description> --use-depth 0

For example, if you want to train a GR-ConvNet on Cornell, use the following command:

$ python train_network.py --dataset cornell --dataset-path data/cornell --description training_cornell --use-depth 0

We also provide training for other baselines, you can use the following command:

$ python train_network.py --dataset <dataset> --dataset-path <dataset> --description <your_description> --use-depth 0 --network <baseline_name>

For instance, if you want to train GG-CNN on Cornell, use the following command:

python train_network.py --dataset cornell --dataset-path data/cornell/ --description training_ggcnn_on_cornell --use-depth 0 --network ggcnn

For testing procedure, we can apply the similar commands to test different baselines on different datasets:

python evaluate.py --network <path_to_pretrained_network> --dataset <dataset> --dataset-path data/<dataset> --iou-eval

Important note: <path_to_pretrained_network> is the path to the pretrained model obtained by training procedure. Usually, the pretrained models obtained by training are stored at logs/<timstamp>_<training_description>. You can select the desired pretrained model to evaluate. We do not have to specify neural architecture as the codebase will automatically detect the neural architecture.

Our codebase is developed based on Kumra et al..