EngineX-Cambricon/enginex-mlu370-any2any

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This model was released on 2024-01-04 and added to Hugging Face Transformers on 2025-08-01.

MM Grounding DINO

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. This model also supports LLMDet inference. You can find LLMDet checkpoints under the LLMDet collection.

Tip

Click on the MM Grounding DINO models in the right sidebar for more examples of how to apply MM Grounding DINO to different MM Grounding DINO tasks.

The example below demonstrates how to generate text based on an image with the [AutoModelForZeroShotObjectDetection] class.

import torch
from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor, infer_device
from transformers.image_utils import load_image


# Prepare processor and model
model_id = "openmmlab-community/mm_grounding_dino_tiny_o365v1_goldg_v3det"
device = infer_device()
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}")

Notes

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)

This implementation also supports inference for LLMDet. Here's a table of LLMDet models and their 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
llmdet_tiny	(O365,GoldG,GRIT,V3Det) + GroundingCap-1M	44.7	37.3	39.5	50.7	34.9	26.0	30.1	44.3
llmdet_base	(O365,GoldG,V3Det) + GroundingCap-1M	48.3	40.8	43.1	54.3	38.5	28.2	34.3	47.8
llmdet_large	(O365V2,OpenImageV6,GoldG) + GroundingCap-1M	51.1	45.1	46.1	56.6	42.0	31.6	38.8	50.2