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Multi-modal Queried Object Detection in the Wild

Official code and models for the NeurIPS 2023 paper:

Multi-modal Queried Object Detection in the Wild

Yifan Xu, Mengdan Zhang, Chaoyou Fu, Peixian Chen, Xiaoshan Yang, Ke Li, Changsheng Xu

NeurIPS 2023

MQ-Det is the first multi-modal queried open-world object detector. If you have any questions, please feel free to raise an issue or email yifan.xu@nlpr.ia.ac.cn.

Updates

10/20/2023: Updated an instruction on modulating on customized datasets! Please refer to CUSTOMIZED_PRETRAIN.md.

10/09/2023: Complete code and models are released!

09/22/2023: MQ-Det has beed accepted by NeurIPS 2023 (Updated Verision).

05/30/2023: MQ-Det paper on arxiv https://arxiv.org/abs/2305.18980.

05/27/2023: Finetuning-free code and models are released.

05/25/2023: Project page built.

Citation

If you find our work useful in your research, please consider citing:

@article{xu2024multi,
  title={Multi-modal queried object detection in the wild},
  author={Xu, Yifan and Zhang, Mengdan and Fu, Chaoyou and Chen, Peixian and Yang, Xiaoshan and Li, Ke and Xu, Changsheng},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

Multi-modal Queried Object Detection

We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both textual description with open-set generalization and visual exemplars with rich description granularity as category queries, namely, Multi-modal Queried object Detection, for real-world detection with both open-vocabulary categories and various granularity.

Method

MQ-Det incorporates vision queries into existing well-established language-queried-only detectors.

Features:

• A plug-and-play gated class-scalable perceiver module upon the frozen detector. Corresponding code is implemented here.
• A vision conditioned masked language prediction strategy. Corresponding code is implemented here.
• Compatible with most language-queried object detectors.

Preparation

Environment. Init the environment:

git clone https://github.com/YifanXu74/MQ-Det.git
cd MQ-Det
conda create -n mqdet python=3.9 -y
conda activate mqdet
bash init.sh

The implementation environment in the paper is python==3.9, torch==2.0.1, GCC==8.3.1, CUDA==11.7. Several potential errors and their solutions are presented in DEBUG.md

Data. Prepare Objects365 (for modulated pre-training), LVIS (for evaluation), and ODinW (for evaluation) benchmarks following DATA.md.

Initial Weight. MQ-Det is build upon frozen language-queried detectors. To conduct modulated pre-training, download corresponding pre-trained model weights first.

We apply MQ-Det on GLIP and GroundingDINO:

GLIP-T:
wget https://huggingface.co/GLIPModel/GLIP/resolve/main/glip_tiny_model_o365_goldg_cc_sbu.pth -O MODEL/glip_tiny_model_o365_goldg_cc_sbu.pth
GLIP-L:
wget https://huggingface.co/GLIPModel/GLIP/resolve/main/glip_large_model.pth -O MODEL/glip_large_model.pth
GroundingDINO-T:
wget https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth -O MODEL/groundingdino_swint_ogc.pth

If the links fail, please manually download corresponding weights from the following table or the github pages of GLIP/GroundingDINO.

The weight files should be placed as follows:

MODEL/
    glip_tiny_model_o365_goldg_cc_sbu.pth
    glip_large_model.pth
    groundingdino_swint_ogc.pth

Model Zoo

The table reports the finetuning-free performance with 5 vision queries.

Vision Query Extraction

Take MQ-GLIP-T as an example.

If you wish to extract vision queries from custom dataset, specify the DATASETS.TRAIN in the config file. We provide some examples in our implementation in the following.

Objects365 for modulated pre-training:

python tools/extract_vision_query.py --config_file configs/pretrain/mq-glip-t.yaml --dataset objects365 --add_name tiny 

This will generate a query bank file in MODEL/object365_query_5000_sel_tiny.pth

LVIS for downstream tasks:

python tools/extract_vision_query.py --config_file configs/pretrain/mq-glip-t.yaml --dataset lvis --num_vision_queries 5 --add_name tiny

This will generate a query bank file in MODEL/lvis_query_5_pool7_sel_tiny.pth.

ODinW for downstream tasks:

python tools/extract_vision_query.py --config_file configs/pretrain/mq-glip-t.yaml --dataset odinw-13 --num_vision_queries 5 --add_name tiny

This will generate query bank files for each dataset in ODinW in MODEL/{dataset}_query_5_pool7_sel_tiny.pth.

Some paramters corresponding to the query extraction:

The above script has already set all paramters well. One only needs to pass:

--config_file is the pretraining config files.

--dataset contains some pre-defined datasets including objects365, lvis, odinw-13, and odinw-35.

--num_vision_queries controls the number of vision queries for each category you want to extract from the training dataset, and can be an arbitrary number. This will set both VISION_QUERY.MAX_QUERY_NUMBER and DATASETS.FEW_SHOT to num_vision_queries. Note that here DATASETS.FEW_SHOT is only for accelerating the extraction process.

--add_name is only a mark for different models. For training/evaluating with MQ-GLIP-T/MQ-GLIP-L/MQ-GroundingDINO, we set --add_name to 'tiny'/'large'/'gd'.

For customized usage, one can modify the commands in the script, or pass additional parameters through --opt, for example,

python tools/extract_vision_query.py --config_file configs/pretrain/mq-glip-t.yaml --dataset lvis --opt 'VISION_QUERY.MAX_QUERY_NUMBER 50 DATASETS.FEW_SHOT 50'

Here are several parameters may be used during query extraction, more details can be found in the code:

DATASETS.FEW_SHOT: if set k>0, the dataset will be subsampled to k-shot for each category when initializing the dataset. This is completed before training. Not used during pre-training.

VISION_QUERY.MAX_QUERY_NUMBER: the max number of vision queries for each category when extracting the query bank. Only used during query extraction. Note that the query extraction is conducted before training and evaluation.

VISION_QUERY.NUM_QUERY_PER_CLASS controls how many queries to provide for each category during one forward process in training and evaluation. Not used during query extraction.

Usually, we set

VISION_QUERY.MAX_QUERY_NUMBER=5000, VISION_QUERY.NUM_QUERY_PER_CLASS=5, DATASETS.FEW_SHOT=0 during pre-training.

VISION_QUERY.MAX_QUERY_NUMBER=k, VISION_QUERY.NUM_QUERY_PER_CLASS=k, DATASETS.FEW_SHOT=k during few-shot (k-shot) fine-tuning.

Modulated Training

Take MQ-GLIP-T as an example.

python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --config-file configs/pretrain/mq-glip-t.yaml --use-tensorboard OUTPUT_DIR 'OUTPUT/MQ-GLIP-TINY/'

To conduct pre-training, one should first extract vision queries before start training following the above instruction.

To pre-train on custom datasets, please specify DATASETS.TRAIN and VISION_SUPPORT.SUPPORT_BANK_PATH in the config file. More details can be found in CUSTOMIZED_PRETRAIN.md.

Finetuning-free Evaluation

Take MQ-GLIP-T as an example.

LVIS Evaluation

MiniVal:

python -m torch.distributed.launch --nproc_per_node=4 \
tools/test_grounding_net.py \
--config-file configs/pretrain/mq-glip-t.yaml \
--additional_model_config configs/vision_query_5shot/lvis_minival.yaml \
VISION_QUERY.QUERY_BANK_PATH MODEL/lvis_query_5_pool7_sel_tiny.pth \
MODEL.WEIGHT ${model_weight_path} \
TEST.IMS_PER_BATCH 4

Val 1.0:

python -m torch.distributed.launch --nproc_per_node=4 \
tools/test_grounding_net.py \
--config-file configs/pretrain/mq-glip-t.yaml \
--additional_model_config configs/vision_query_5shot/lvis_val.yaml \
VISION_QUERY.QUERY_BANK_PATH MODEL/lvis_query_5_pool7_sel_tiny.pth \
MODEL.WEIGHT ${model_weight_path} \
TEST.IMS_PER_BATCH 4 

Please follow the above instruction to extract corresponding vision queries. Note that --nproc_per_node must equal to TEST.IMS_PER_BATCH.

ODinW / Custom Dataset Evaluation

python tools/eval_odinw.py --config_file configs/pretrain/mq-glip-t.yaml \
--opts 'MODEL.WEIGHT ${model_weight_path}' \
--setting finetuning-free \
--add_name tiny \
--log_path 'OUTPUT/odinw_log/'

The results are stored at OUTPUT/odinw_log/.

If you wish to use custom vision queries or datasets, specify --task_config and --custom_bank_path. The task_config should be like the ones in ODinW configs, and make sure DATASETS.TRAIN_DATASETNAME_SUFFIX to be "_vision_query" to enable the dataset with vision queries. The custom_bank_path should be extracted following the instruction. For example,

python tools/eval_odinw.py --config_file configs/pretrain/mq-glip-t.yaml \
--opts 'MODEL.WEIGHT ${model_weight_path}' \
--setting finetuning-free \
--add_name tiny \
--log_path 'OUTPUT/custom_log/'
--task_config ${custom_config_path}
--custom_bank_path ${custom_bank_path}

Fine-Tuning

Take MQ-GLIP-T as an example.

python tools/eval_odinw.py --config_file configs/pretrain/mq-glip-t.yaml \
--opts 'MODEL.WEIGHT ${model_weight_path}' \
--setting 3-shot \
--add_name tiny \
--log_path 'OUTPUT/odinw_log/'

This command will first automatically extract the vision query bank from the (few-shot) training set. Then conduct fine-tuning. If you wish to use custom vision queries, add 'VISION_QUERY.QUERY_BANK_PATH custom_bank_path' to the --opts argment, and also modify the dataset_configs in the tools/eval_odinw.py.

If set VISION_QUERY.QUERY_BANK_PATH to '', the model will automatically extract the vision query bank from the (few-shot) training set before fine-tuning.

Note that current code does not support GroundingDINO for few-shot fine-tuning. Please try modulated pre-training and finetuning-free evaluation.

Single-Modal Evaluation

Here we provide introduction on utilizing single modal queries, such as visual exemplars or textual description.

Follow the command as in Finetuning-free Evaluation. But set the following hyper-parameters.

To solely use vision queries, add hyper-parameters:

VISION_QUERY.MASK_DURING_INFERENCE True VISION_QUERY.TEXT_DROPOUT 1.0

To solely use language queries, add hyper-parameters:

VISION_QUERY.ENABLED FALSE

For example, to solely use vision queries,

python -m torch.distributed.launch --nproc_per_node=4 \
tools/test_grounding_net.py \
--config-file configs/pretrain/mq-glip-t.yaml \
--additional_model_config configs/vision_query_5shot/lvis_minival.yaml \
VISION_QUERY.QUERY_BANK_PATH MODEL/lvis_query_5_pool7_sel_tiny.pth \
MODEL.WEIGHT ${model_weight_path} \
TEST.IMS_PER_BATCH 4 \
VISION_QUERY.MASK_DURING_INFERENCE True VISION_QUERY.TEXT_DROPOUT 1.0

python tools/eval_odinw.py --config_file configs/pretrain/mq-glip-t.yaml \
--opts 'MODEL.WEIGHT ${model_weight_path} VISION_QUERY.MASK_DURING_INFERENCE True VISION_QUERY.TEXT_DROPOUT 1.0' \
--setting finetuning-free \
--add_name tiny \
--log_path 'OUTPUT/odinw_vision_log/'