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Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Abstract

In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary objects with human inputs such as category names or referring expressions. The key solution of open-set object detection is introducing language to a closed-set detector for open-set concept generalization. To effectively fuse language and vision modalities, we conceptually divide a closed-set detector into three phases and propose a tight fusion solution, which includes a feature enhancer, a language-guided query selection, and a cross-modality decoder for cross-modality fusion. While previous works mainly evaluate open-set object detection on novel categories, we propose to also perform evaluations on referring expression comprehension for objects specified with attributes. Grounding DINO performs remarkably well on all three settings, including benchmarks on COCO, LVIS, ODinW, and RefCOCO/+/g. Grounding DINO achieves a 52.5 AP on the COCO detection zero-shot transfer benchmark, i.e., without any training data from COCO. It sets a new record on the ODinW zero-shot benchmark with a mean 26.1 AP.

Installation

cd $MMDETROOT

# source installation
pip install -r requirements/multimodal.txt

# or mim installation
mim install mmdet[multimodal]

NOTE

Grounding DINO utilizes BERT as the language model, which requires access to https://huggingface.co/. If you encounter connection errors due to network access, you can download the required files on a computer with internet access and save them locally. Finally, modify the lang_model_name field in the config to the local path. Please refer to the following code:

from transformers import BertConfig, BertModel
from transformers import AutoTokenizer

config = BertConfig.from_pretrained("bert-base-uncased")
model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, config=config)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

config.save_pretrained("your path/bert-base-uncased")
model.save_pretrained("your path/bert-base-uncased")
tokenizer.save_pretrained("your path/bert-base-uncased")

Inference

cd $MMDETROOT

wget https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth

python demo/image_demo.py \
	demo/demo.jpg \
	configs/grounding_dino/grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py \
	--weights groundingdino_swint_ogc_mmdet-822d7e9d.pth \
	--texts 'bench . car .'

COCO Results and Models

Model Backbone Style COCO mAP Official COCO mAP Pre-Train Data Config Download
Grounding DINO-T Swin-T Zero-shot 48.5 48.4 O365,GoldG,Cap4M config model
Grounding DINO-T Swin-T Finetune 58.1(+0.9) 57.2 O365,GoldG,Cap4M config model | log
Grounding DINO-B Swin-B Zero-shot 56.9 56.7 COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO config model
Grounding DINO-B Swin-B Finetune 59.7 COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO config model | log
Grounding DINO-R50 R50 Scratch 48.9(+0.8) 48.1 config model | log

Note:

  1. The weights corresponding to the zero-shot model are adopted from the official weights and converted using the script. We have not retrained the model for the time being.
  2. Finetune refers to fine-tuning on the COCO 2017 dataset. The R50 model is trained using 8 NVIDIA GeForce 3090 GPUs, while the remaining models are trained using 16 NVIDIA GeForce 3090 GPUs. The GPU memory usage is approximately 8.5GB.
  3. Our performance is higher than the official model due to two reasons: we modified the initialization strategy and introduced a log scaler.

LVIS Results

Model MiniVal APr MiniVal APc MiniVal APf MiniVal AP Val1.0 APr Val1.0 APc Val1.0 APf Val1.0 AP Pre-Train Data Config Download
Grounding DINO-T 18.8 24.2 34.7 28.8 10.1 15.3 29.9 20.1 O365,GoldG,Cap4M config model
Grounding DINO-B 27.9 33.4 37.2 34.7 19.0 24.1 32.9 26.7 COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO config model

Note:

  1. The above are zero-shot evaluation results.
  2. The evaluation metric we used is LVIS FixAP. For specific details, please refer to Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details.

ODinW (Object Detection in the Wild) Results

Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER 1 , the first benchmark and toolkit for evaluating (pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is platform for Computer Vision in the Wild (CVinW), and is publicly released at https://computer-vision-in-the-wild.github.io/ELEVATER/

Results and models of ODinW13

Method GLIP-T(A) Official GLIP-T(B) Official GLIP-T(C) Official GroundingDINO-T GroundingDINO-B
AerialMaritimeDrone 0.123 0.122 0.110 0.110 0.130 0.130 0.173 0.281
Aquarium 0.175 0.174 0.173 0.169 0.191 0.190 0.195 0.445
CottontailRabbits 0.686 0.686 0.688 0.688 0.744 0.744 0.799 0.808
EgoHands 0.013 0.013 0.003 0.004 0.314 0.315 0.608 0.764
NorthAmericaMushrooms 0.502 0.502 0.367 0.367 0.297 0.296 0.507 0.675
Packages 0.589 0.589 0.083 0.083 0.699 0.699 0.687 0.670
PascalVOC 0.512 0.512 0.541 0.540 0.565 0.565 0.563 0.711
pistols 0.339 0.339 0.502 0.501 0.503 0.504 0.726 0.771
pothole 0.007 0.007 0.030 0.030 0.058 0.058 0.215 0.478
Raccoon 0.075 0.074 0.285 0.288 0.241 0.244 0.549 0.541
ShellfishOpenImages 0.253 0.253 0.337 0.338 0.300 0.302 0.393 0.650
thermalDogsAndPeople 0.372 0.372 0.475 0.475 0.510 0.510 0.657 0.633
VehiclesOpenImages 0.574 0.566 0.562 0.547 0.549 0.534 0.613 0.647
Average 0.325 0.324 0.320 0.318 0.392 0.392 0.514 0.621

Results and models of ODinW35

Method GLIP-T(A) Official GLIP-T(B) Official GLIP-T(C) Official GroundingDINO-T GroundingDINO-B
AerialMaritimeDrone_large 0.123 0.122 0.110 0.110 0.130 0.130 0.173 0.281
AerialMaritimeDrone_tiled 0.174 0.174 0.172 0.172 0.172 0.172 0.206 0.364
AmericanSignLanguageLetters 0.001 0.001 0.003 0.003 0.009 0.009 0.002 0.096
Aquarium 0.175 0.175 0.173 0.171 0.192 0.182 0.195 0.445
BCCD 0.016 0.016 0.001 0.001 0.000 0.000 0.161 0.584
boggleBoards 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.134
brackishUnderwater 0.016 0..013 0.021 0.027 0.020 0.022 0.021 0.454
ChessPieces 0.001 0.001 0.000 0.000 0.001 0.001 0.000 0.000
CottontailRabbits 0.710 0.709 0.683 0.683 0.752 0.752 0.806 0.797
dice 0.005 0.005 0.004 0.004 0.004 0.004 0.004 0.082
DroneControl 0.016 0.017 0.006 0.008 0.005 0.007 0.042 0.638
EgoHands_generic 0.009 0.010 0.005 0.006 0.510 0.508 0.608 0.764
EgoHands_specific 0.001 0.001 0.004 0.006 0.003 0.004 0.002 0.687
HardHatWorkers 0.029 0.029 0.023 0.023 0.033 0.033 0.046 0.439
MaskWearing 0.007 0.007 0.003 0.002 0.005 0.005 0.004 0.406
MountainDewCommercial 0.218 0.227 0.199 0.197 0.478 0.463 0.430 0.580
NorthAmericaMushrooms 0.502 0.502 0.450 0.450 0.497 0.497 0.471 0.501
openPoetryVision 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.051
OxfordPets_by_breed 0.001 0.002 0.002 0.004 0.001 0.002 0.003 0.799
OxfordPets_by_species 0.016 0.011 0.012 0.009 0.013 0.009 0.011 0.872
PKLot 0.002 0.002 0.000 0.000 0.000 0.000 0.001 0.774
Packages 0.569 0.569 0.279 0.279 0.712 0.712 0.695 0.728
PascalVOC 0.512 0.512 0.541 0.540 0.565 0.565 0.563 0.711
pistols 0.339 0.339 0.502 0.501 0.503 0.504 0.726 0.771
plantdoc 0.002 0.002 0.007 0.007 0.009 0.009 0.005 0.376
pothole 0.007 0.010 0.024 0.025 0.085 0.101 0.215 0.478
Raccoons 0.075 0.074 0.285 0.288 0.241 0.244 0.549 0.541
selfdrivingCar 0.071 0.072 0.074 0.074 0.081 0.080 0.089 0.318
ShellfishOpenImages 0.253 0.253 0.337 0.338 0.300 0.302 0.393 0.650
ThermalCheetah 0.028 0.028 0.000 0.000 0.028 0.028 0.087 0.290
thermalDogsAndPeople 0.372 0.372 0.475 0.475 0.510 0.510 0.657 0.633
UnoCards 0.000 0.000 0.000 0.001 0.002 0.003 0.006 0.754
VehiclesOpenImages 0.574 0.566 0.562 0.547 0.549 0.534 0.613 0.647
WildfireSmoke 0.000 0.000 0.000 0.000 0.017 0.017 0.134 0.410
websiteScreenshots 0.003 0.004 0.003 0.005 0.005 0.006 0.012 0.175
Average 0.134 0.134 0.138 0.138 0.179 0.178 0.227 0.492

Flickr30k Results

Model Pre-Train Data Val R@1 Val R@5 Val R@10 Tesst R@1 Test R@5 Test R@10 Config Download
Grounding DINO-T O365,GoldG,Cap4M 87.8 96.6 98.0 88.1 96.9 98.2 config model | log

Note:

  1. @1,5,10 refers to precision at the top 1, 5, and 10 positions in a predicted ranked list.
  2. The pretraining data used by Grounding DINO-T is O365,GoldG,Cap4M, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)[refcoco/grounding_dino_swin-t_pretrain_zeroshot_refcoco.py].

Test Command

cd mmdetection
bash tools/dist_test.sh configs/grounding_dino/flickr30k/grounding_dino_swin-t-pretrain_zeroshot_flickr30k.py checkpoints/groundingdino_swint_ogc_mmdet-822d7e9d.pth 8

Referring Expression Comprehension Results

Method Grounding DINO-T
(O365,GoldG,Cap4M)		 Grounding DINO-B
(COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO)
RefCOCO val @1,5,10 50.77/89.45/94.86 84.61/97.88/99.10
RefCOCO testA @1,5,10 57.45/91.29/95.62 88.65/98.89/99.63
RefCOCO testB @1,5,10 44.97/86.54/92.88 80.51/96.64/98.51
RefCOCO+ val @1,5,10 51.64/86.35/92.57 73.67/96.60/98.65
RefCOCO+ testA @1,5,10 57.25/86.74/92.65 82.19/97.92/99.09
RefCOCO+ testB @1,5,10 46.35/84.05/90.67 64.10/94.25/97.46
RefCOCOg val @1,5,10 60.42/92.10/96.18 78.33/97.28/98.57
RefCOCOg test @1,5,10 59.74/92.08/96.28 78.11/97.06/98.65
gRefCOCO val Pr@(F1=1, IoU≥0.5),N-acc 41.32/91.82 46.18/81.44
gRefCOCO testA Pr@(F1=1, IoU≥0.5),N-acc 27.23/90.24 38.60/76.06
gRefCOCO testB Pr@(F1=1, IoU≥0.5),N-acc 29.70/93.49 35.87/80.58

Note:

  1. @1,5,10 refers to precision at the top 1, 5, and 10 positions in a predicted ranked list.
  2. Pr@(F1=1, IoU≥0.5),N-acc from the paper GREC: Generalized Referring Expression Comprehension
  3. The pretraining data used by Grounding DINO-T is O365,GoldG,Cap4M, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)[refcoco/grounding_dino_swin-t_pretrain_zeroshot_refcoco.py].
  4. The pretraining data used by Grounding DINO-B is COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO, and the corresponding evaluation configuration is (grounding_dino_swin-t_pretrain_zeroshot_refcoco)[refcoco/grounding_dino_swin-b_pretrain_zeroshot_refcoco.py].

Test Command

cd mmdetection
./tools/dist_test.sh configs/grounding_dino/refcoco/grounding_dino_swin-t_pretrain_zeroshot_refexp.py https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth 8
./tools/dist_test.sh configs/grounding_dino/refcoco/grounding_dino_swin-b_pretrain_zeroshot_refexp.py https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth 8

Description Detection Dataset

pip install ddd-dataset
Method mode Grounding DINO-T
(O365,GoldG,Cap4M)		 Grounding DINO-B
(COCO,O365,GoldG,Cap4M,OpenImage,ODinW-35,RefCOCO)
FULL/short/middle/long/very long concat 17.2/18.0/18.7/14.8/16.3 20.2/20.4/21.1/18.8/19.8
FULL/short/middle/long/very long parallel 22.3/28.2/24.8/19.1/13.9 25.0/26.4/27.2/23.5/19.7
PRES/short/middle/long/very long concat 17.8/18.3/19.2/15.2/17.3 20.7/21.7/21.4/19.1/20.3
PRES/short/middle/long/very long parallel 21.0/27.0/22.8/17.5/12.5 23.7/25.8/25.1/21.9/19.3
ABS/short/middle/long/very long concat 15.4/17.1/16.4/13.6/14.9 18.6/16.1/19.7/18.1/19.1
ABS/short/middle/long/very long parallel 26.0/32.0/33.0/23.6/15.5 28.8/28.1/35.8/28.2/20.2

Note:

  1. Considering that the evaluation time for Inter-scenario is very long and the performance is low, it is temporarily not supported. The mentioned metrics are for Intra-scenario.
  2. concat is the default inference mode for Grounding DINO, where it concatenates multiple sub-sentences with "." to form a single sentence for inference. On the other hand, "parallel" performs inference on each sub-sentence in a for-loop.

Custom Dataset

To facilitate fine-tuning on custom datasets, we use a simple cat dataset as an example, as shown in the following steps.

1. Dataset Preparation

cd mmdetection
wget https://download.openmmlab.com/mmyolo/data/cat_dataset.zip
unzip cat_dataset.zip -d data/cat/

cat dataset is a single-category dataset with 144 images, which has been converted to coco format.

cat dataset

2. Config Preparation

Due to the simplicity and small number of cat datasets, we use 8 cards to train 20 epochs, scale the learning rate accordingly, and do not train the language model, only the visual model.

The Details of the configuration can be found in grounding_dino_swin-t_finetune_8xb2_20e_cat

3. Visualization and Evaluation

Due to the Grounding DINO is an open detection model, so it can be detected and evaluated even if it is not trained on the cat dataset.

The single image visualization is as follows:

cd mmdetection
python demo/image_demo.py data/cat/images/IMG_20211205_120756.jpg configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py --weights https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth --texts cat.
cat dataset

The test dataset evaluation on single card is as follows:

python tools/test.py configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.867
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.931
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.867
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.903
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.907
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.907
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.907

4. Model Training and Visualization

./tools/dist_train.sh configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py 8 --work-dir cat_work_dir

The model will be saved based on the best performance on the test set. The performance of the best model (at epoch 16) is as follows:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.905
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 1.000
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.923
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.905
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.927
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.937
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.937
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.937

We can find that after fine-tuning training, the training of the cat dataset is increased from 86.7 to 90.5.

If we do single image inference visualization again, the result is as follows:

cd mmdetection
python demo/image_demo.py data/cat/images/IMG_20211205_120756.jpg configs/grounding_dino/grounding_dino_swin-t_finetune_8xb2_20e_cat.py --weights cat_work_dir/best_coco_bbox_mAP_epoch_16.pth --texts cat.
cat dataset