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jameslahm/
yoloe

object-detection

2503.07465

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YOLOE: Real-Time Seeing Anything

Official PyTorch implementation of YOLOE.

Comparison of performance, training cost, and inference efficiency between YOLOE (Ours) and YOLO-Worldv2 in terms of open text prompts.

YOLOE: Real-Time Seeing Anything.
Ao Wang*, Lihao Liu*, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding

arXiv

We introduce YOLOE(ye), a highly efficient, unified, and open object detection and segmentation model, like human eye, under different prompt mechanisms, like texts, visual inputs, and prompt-free paradigm.

Abstract

Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios. Recent open-set methods leverage text prompts, visual cues, or prompt-free paradigm to overcome this, but often compromise between performance and efficiency due to high computational demands or deployment complexity. In this work, we introduce YOLOE, which integrates detection and segmentation across diverse open prompt mechanisms within a single highly efficient model, achieving real-time seeing anything. For text prompts, we propose Re-parameterizable Region-Text Alignment (RepRTA) strategy. It refines pretrained textual embeddings via a re-parameterizable lightweight auxiliary network and enhances visual-textual alignment with zero inference and transferring overhead. For visual prompts, we present Semantic-Activated Visual Prompt Encoder (SAVPE). It employs decoupled semantic and activation branches to bring improved visual embedding and accuracy with minimal complexity. For prompt-free scenario, we introduce Lazy Region-Prompt Contrast (LRPC) strategy. It utilizes a built-in large vocabulary and specialized embedding to identify all objects, avoiding costly language model dependency. Extensive experiments show YOLOE's exceptional zero-shot performance and transferability with high inference efficiency and low training cost. Notably, on LVIS, with $3\times$ less training cost and $1.4\times$ inference speedup, YOLOE-v8-S surpasses YOLO-Worldv2-S by 3.5 AP. When transferring to COCO, YOLOE-v8-L achieves 0.6 $AP^b$ and 0.4 $AP^m$ gains over closed-set YOLOv8-L with nearly $4\times$ less training time.

Performance

Zero-shot detection evaluation

Fixed AP is reported on LVIS minival set with text (T) / visual (V) prompts.
Training time is for text prompts with detection based on 8 Nvidia RTX4090 GPUs.
FPS is measured on T4 with TensorRT and iPhone 12 with CoreML, respectively.
For training data, OG denotes Objects365v1 and GoldG.
YOLOE can become YOLOs after re-parameterization with zero inference and transferring overhead.

Model	Size	Prompt	Params	Data	Time	FPS	$AP$	$AP_r$	$AP_c$	$AP_f$	Log
YOLOE-v8-S	640	T / V	12M / 13M	OG	12.0h	305.8 / 64.3	27.9 / 26.2	22.3 / 21.3	27.8 / 27.7	29.0 / 25.7	T / V
YOLOE-v8-M	640	T / V	27M / 30M	OG	17.0h	156.7 / 41.7	32.6 / 31.0	26.9 / 27.0	31.9 / 31.7	34.4 / 31.1	T / V
YOLOE-v8-L	640	T / V	45M / 50M	OG	22.5h	102.5 / 27.2	35.9 / 34.2	33.2 / 33.2	34.8 / 34.6	37.3 / 34.1	T / V
YOLOE-11-S	640	T / V	10M / 12M	OG	13.0h	301.2 / 73.3	27.5 / 26.3	21.4 / 22.5	26.8 / 27.1	29.3 / 26.4	T / V
YOLOE-11-M	640	T / V	21M / 27M	OG	18.5h	168.3 / 39.2	33.0 / 31.4	26.9 / 27.1	32.5 / 31.9	34.5 / 31.7	T / V
YOLOE-11-L	640	T / V	26M / 32M	OG	23.5h	130.5 / 35.1	35.2 / 33.7	29.1 / 28.1	35.0 / 34.6	36.5 / 33.8	T / V

Zero-shot segmentation evaluation

The model is the same as above in Zero-shot detection evaluation.
Standard AP^m is reported on LVIS val set with text (T) / visual (V) prompts.

Model	Size	Prompt	$AP^m$	$AP_r^m$	$AP_c^m$	$AP_f^m$
YOLOE-v8-S	640	T / V	17.7 / 16.8	15.5 / 13.5	16.3 / 16.7	20.3 / 18.2
YOLOE-v8-M	640	T / V	20.8 / 20.3	17.2 / 17.0	19.2 / 20.1	24.2 / 22.0
YOLOE-v8-L	640	T / V	23.5 / 22.0	21.9 / 16.5	21.6 / 22.1	26.4 / 24.3
YOLOE-11-S	640	T / V	17.6 / 17.1	16.1 / 14.4	15.6 / 16.8	20.5 / 18.6
YOLOE-11-M	640	T / V	21.1 / 21.0	17.2 / 18.3	19.6 / 20.6	24.4 / 22.6
YOLOE-11-L	640	T / V	22.6 / 22.5	19.3 / 20.5	20.9 / 21.7	26.0 / 24.1

Prompt-free evaluation

The model is the same as above in Zero-shot detection evaluation except the specialized prompt embedding.
Fixed AP is reported on LVIS minival set and FPS is measured on Nvidia T4 GPU with Pytorch.

Model	Size	Params	$AP$	$AP_r$	$AP_c$	$AP_f$	FPS	Log
YOLOE-v8-S	640	13M	21.0	19.1	21.3	21.0	95.8	PF
YOLOE-v8-M	640	29M	24.7	22.2	24.5	25.3	45.9	PF
YOLOE-v8-L	640	47M	27.2	23.5	27.0	28.0	25.3	PF
YOLOE-11-S	640	11M	20.6	18.4	20.2	21.3	93.0	PF
YOLOE-11-M	640	24M	25.5	21.6	25.5	26.1	42.5	PF
YOLOE-11-L	640	29M	26.3	22.7	25.8	27.5	34.9	PF

Downstream transfer on COCO

During transferring, YOLOE-v8 / YOLOE-11 is exactly the same as YOLOv8 / YOLO11.
For Linear probing, only the last conv in classification head is trainable.
For Full tuning, all parameters are trainable.

Model	Size	Epochs	$AP^b$	$AP^b_{50}$	$AP^b_{75}$	$AP^m$	$AP^m_{50}$	$AP^m_{75}$	Log
Linear probing
YOLOE-v8-S	640	10	35.6	51.5	38.9	30.3	48.2	32.0	LP
YOLOE-v8-M	640	10	42.2	59.2	46.3	35.5	55.6	37.7	LP
YOLOE-v8-L	640	10	45.4	63.3	50.0	38.3	59.6	40.8	LP
YOLOE-11-S	640	10	37.0	52.9	40.4	31.5	49.7	33.5	LP
YOLOE-11-M	640	10	43.1	60.6	47.4	36.5	56.9	39.0	LP
YOLOE-11-L	640	10	45.1	62.8	49.5	38.0	59.2	40.6	LP
Full tuning
YOLOE-v8-S	640	160	45.0	61.6	49.1	36.7	58.3	39.1	FT
YOLOE-v8-M	640	80	50.4	67.0	55.2	40.9	63.7	43.5	FT
YOLOE-v8-L	640	80	53.0	69.8	57.9	42.7	66.5	45.6	FT
YOLOE-11-S	640	160	46.2	62.9	50.0	37.6	59.3	40.1	FT
YOLOE-11-M	640	80	51.3	68.3	56.0	41.5	64.8	44.3	FT
YOLOE-11-L	640	80	52.6	69.7	57.5	42.4	66.2	45.2	FT

Installation

conda virtual environment is recommended.

bash

conda create -n yoloe python=3.10 -y
conda activate yoloe

pip install -r requirements.txt
pip install -e .
pip install -e lvis-api
pip install -e ml-mobileclip
pip install -e CLIP

Demo

If desired objects are not identified, pleaset set a smaller confidence threshold, e.g., for visual prompts with handcrafted shape or cross-image prompts.

bash

# Optional for mirror: export HF_ENDPOINT=https://hf-mirror.com
pip install gradio==4.42.0 gradio_image_prompter==0.1.0 fastapi==0.112.2
python app.py
# Please visit http://127.0.0.1:7860

Prediction

Text prompt

python predict.py

Visual prompt

python predict_vp.py

Prompt free

python predict_pf.py

Validation

Data

Please download LVIS following here or lvis.yaml.
We use this minival.txt with background images for evaluation.

bash

# For evaluation with visual prompt, please obtain the referring data.
python tools/generate_lvis_visual_prompt_data.py

Zero-shot evaluation on LVIS

For text prompts, python val.py.
For visual prompts, python val_vp.py

For Fixed AP, please refer to the comments in val.py and val_vp.py, and use tools/eval_fixed_ap.py for evaluation.

Prompt-free evaluation

bash

python val_pe_free.py
python tools/eval_open_ended.py --json ../datasets/lvis/annotations/lvis_v1_minival.json --pred runs/detect/val/predictions.json --fixed

Downstream transfer on COCO

python val_coco.py

Training

The training includes three stages:

YOLOE is trained with text prompts for detection and segmentation for 30 epochs.
Only visual prompt encoder (SAVPE) is trained with visual prompts for 2 epochs.
Only specialized prompt embedding for prompt free is trained for 1 epochs.

Data

Images	Raw Annotations	Processed Annotations
Objects365v1	objects365_train.json	objects365_train_segm.json
GQA	final_mixed_train_noo_coco.json	final_mixed_train_noo_coco_segm.json
Flickr30k	final_flickr_separateGT_train.json	final_flickr_separateGT_train_segm.json

For annotations, you can directly use our preprocessed ones or use the following script to obtain the processed annotations with segmentation masks.

bash

# Generate segmentation data
conda create -n sam2 python==3.10.16
conda activate sam2
pip install -r sam2/requirements.txt
pip install -e sam2/

python tools/generate_sam_masks.py --img-path ../datasets/Objects365v1/images/train --json-path ../datasets/Objects365v1/annotations/objects365_train.json --batch
python tools/generate_sam_masks.py --img-path ../datasets/flickr/full_images/ --json-path ../datasets/flickr/annotations/final_flickr_separateGT_train.json
python tools/generate_sam_masks.py --img-path ../datasets/mixed_grounding/gqa/images --json-path ../datasets/mixed_grounding/annotations/final_mixed_train_no_coco.json

# Generate objects365v1 labels
python tools/generate_objects365v1.py

Then, please generate the data and embedding cache for training.

bash

# Generate grounding segmentation cache
python tools/generate_grounding_cache.py --img-path ../datasets/flickr/full_images/ --json-path ../datasets/flickr/annotations/final_flickr_separateGT_train_segm.json
python tools/generate_grounding_cache.py --img-path ../datasets/mixed_grounding/gqa/images --json-path ../datasets/mixed_grounding/annotations/final_mixed_train_no_coco_segm.json

# Generate train label embeddings
python tools/generate_label_embedding.py
python tools/generate_global_neg_cat.py

At last, please download MobileCLIP-B(LT) for text encoder.

wget https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_blt.pt

Text prompt

bash

# For models with l scale, please change the initialization by referring to the comments in Line 549 in ultralytics/nn/moduels/head.py
# If you want to train YOLOE only for detection, you can use `train.py` 
python train_seg.py

Visual prompt

bash

# For visual prompt, because only SAVPE is trained, we can adopt the detection pipleline with less training time

# First, obtain the detection model
python tools/convert_segm2det.py
# Then, train the SAVPE module
python train_vp.py
# After training, please use tools/get_vp_segm.py to add the segmentation head
# python tools/get_vp_segm.py

Prompt free

bash

# Generate LVIS with single class for evaluation during training
python tools/generate_lvis_sc.py

# Similar to visual prompt, because only the specialized prompt embedding is trained, we can adopt the detection pipleline with less training time
python tools/convert_segm2det.py
python train_pe_free.py
# After training, please use tools/get_pf_free_segm.py to add the segmentation head
# python tools/get_pf_free_segm.py

Transferring

After pretraining, YOLOE-v8 / YOLOE-11 can be re-parameterized into the same architecture as YOLOv8 / YOLO11, with zero overhead for transferring.

Linear probing

Only the last conv, ie., the prompt embedding, is trainable.

python train_pe.py

Full tuning

All parameters are trainable, for better performance.

bash

# For models with s scale, please change the epochs to 160 for longer training
python train_pe_all.py

Export

After re-parameterization, YOLOE-v8 / YOLOE-11 can be exported into the identical format as YOLOv8 / YOLO11.

bash

pip install onnx coremltools onnxslim
python export.py

Benchmark

For TensorRT, please refer to benchmark.sh.
For CoreML, please use the benchmark tool from XCode 14.
For prompt-free setting, please refer to tools/benchmark_pf.py.

Acknowledgement

The code base is built with ultralytics, YOLO-World, MobileCLIP, lvis-api, CLIP, and GenerateU.

Thanks for the great implementations!

Citation

If our code or models help your work, please cite our paper:

BibTeX

@misc{wang2025yoloerealtimeseeing,
      title={YOLOE: Real-Time Seeing Anything}, 
      author={Ao Wang and Lihao Liu and Hui Chen and Zijia Lin and Jungong Han and Guiguang Ding},
      year={2025},
      eprint={2503.07465},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.07465}, 
}

jameslahm/yoloe

YOLOE: Real-Time Seeing Anything

Performance

Zero-shot detection evaluation

Zero-shot segmentation evaluation

Prompt-free evaluation

Downstream transfer on COCO

Installation

Demo

Prediction

Text prompt

Visual prompt

Prompt free

Validation

Data

Zero-shot evaluation on LVIS

Prompt-free evaluation

Downstream transfer on COCO

Training

Data

Text prompt

Visual prompt

Prompt free

Transferring

Linear probing

Full tuning

Export

Benchmark

Acknowledgement

Citation

jameslahm/
yoloe