MM Grounding DINO (base variant)
MM Grounding DINO model was proposed in An Open and Comprehensive Pipeline for Unified Object Grounding and Detection by Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang.
MM Grounding DINO improves upon the Grounding DINO by improving the contrastive class head and removing the parameter sharing in the decoder, improving zero-shot detection performance on both COCO (50.6(+2.2) AP) and LVIS (31.9(+11.8) val AP and 41.4(+12.6) minival AP).
You can find all the original MM Grounding DINO checkpoints under the MM Grounding DINO collection.
Intended uses
You can use the raw model for zero-shot object detection.
Here's how to use the model for zero-shot object detection:
import torch
from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor
from transformers.image_utils import load_image
# Prepare processor and model
model_id = "rziga/mm_grounding_dino_base_all"
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
# Prepare inputs
image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = load_image(image_url)
text_labels = [["a cat", "a remote control"]]
inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
# Run inference
with torch.no_grad():
outputs = model(**inputs)
# Postprocess outputs
results = processor.post_process_grounded_object_detection(
outputs,
threshold=0.4,
target_sizes=[(image.height, image.width)]
)
# Retrieve the first image result
result = results[0]
for box, score, labels in zip(result["boxes"], result["scores"], result["labels"]):
box = [round(x, 2) for x in box.tolist()]
print(f"Detected {labels} with confidence {round(score.item(), 3)} at location {box}")
Training Data
This model was trained on:
Evaluation results
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Here's a table of models and their object detection performance results on COCO (results from official repo):
Model Backbone Pre-Train Data Style COCO mAP mm_grounding_dino_tiny_o365v1_goldg Swin-T O365,GoldG Zero-shot 50.4(+2.3) mm_grounding_dino_tiny_o365v1_goldg_grit Swin-T O365,GoldG,GRIT Zero-shot 50.5(+2.1) mm_grounding_dino_tiny_o365v1_goldg_v3det Swin-T O365,GoldG,V3Det Zero-shot 50.6(+2.2) mm_grounding_dino_tiny_o365v1_goldg_grit_v3det Swin-T O365,GoldG,GRIT,V3Det Zero-shot 50.4(+2.0) mm_grounding_dino_base_o365v1_goldg_v3det Swin-B O365,GoldG,V3Det Zero-shot 52.5 mm_grounding_dino_base_all Swin-B O365,ALL - 59.5 mm_grounding_dino_large_o365v2_oiv6_goldg Swin-L O365V2,OpenImageV6,GoldG Zero-shot 53.0 mm_grounding_dino_large_all Swin-L O365V2,OpenImageV6,ALL - 60.3 -
Here's a table of MM Grounding DINO tiny models and their object detection performance on LVIS (results from official repo):
Model Pre-Train Data MiniVal APr MiniVal APc MiniVal APf MiniVal AP Val1.0 APr Val1.0 APc Val1.0 APf Val1.0 AP mm_grounding_dino_tiny_o365v1_goldg O365,GoldG 28.1 30.2 42.0 35.7(+6.9) 17.1 22.4 36.5 27.0(+6.9) mm_grounding_dino_tiny_o365v1_goldg_grit O365,GoldG,GRIT 26.6 32.4 41.8 36.5(+7.7) 17.3 22.6 36.4 27.1(+7.0) mm_grounding_dino_tiny_o365v1_goldg_v3det O365,GoldG,V3Det 33.0 36.0 45.9 40.5(+11.7) 21.5 25.5 40.2 30.6(+10.5) mm_grounding_dino_tiny_o365v1_goldg_grit_v3det O365,GoldG,GRIT,V3Det 34.2 37.4 46.2 41.4(+12.6) 23.6 27.6 40.5 31.9(+11.8)
BibTeX entry and citation info
@article{zhao2024open,
title={An Open and Comprehensive Pipeline for Unified Object Grounding and Detection},
author={Zhao, Xiangyu and Chen, Yicheng and Xu, Shilin and Li, Xiangtai and Wang, Xinjiang and Li, Yining and Huang, Haian},
journal={arXiv preprint arXiv:2401.02361},
year={2024}
}