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YOLO-World Model

The YOLO-World Model introduces an advanced, real-time Ultralytics YOLOv8-based approach for Open-Vocabulary Detection tasks. This innovation enables the detection of any object within an image based on descriptive texts. By significantly lowering computational demands while preserving competitive performance, YOLO-World emerges as a versatile tool for numerous vision-based applications.

YOLO-World Model architecture overview

Overview

YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome Transformer models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-language modeling and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.

Key Features

  1. Real-time Solution: Harnessing the computational speed of CNNs, YOLO-World delivers a swift open-vocabulary detection solution, catering to industries in need of immediate results.

  2. Efficiency and Performance: YOLO-World slashes computational and resource requirements without sacrificing performance, offering a robust alternative to models like SAM but at a fraction of the computational cost, enabling real-time applications.

  3. Inference with Offline Vocabulary: YOLO-World introduces a "prompt-then-detect" strategy, employing an offline vocabulary to enhance efficiency further. This approach enables the use of custom prompts computed apriori, including captions or categories, to be encoded and stored as offline vocabulary embeddings, streamlining the detection process.

  4. Powered by YOLOv8: Built upon Ultralytics YOLOv8, YOLO-World leverages the latest advancements in real-time object detection to facilitate open-vocabulary detection with unparalleled accuracy and speed.

  5. Benchmark Excellence: YOLO-World outperforms existing open-vocabulary detectors, including MDETR and GLIP series, in terms of speed and efficiency on standard benchmarks, showcasing YOLOv8's superior capability on a single NVIDIA V100 GPU.

  6. Versatile Applications: YOLO-World's innovative approach unlocks new possibilities for a multitude of vision tasks, delivering speed improvements by orders of magnitude over existing methods.

Available Models, Supported Tasks, and Operating Modes

This section details the models available with their specific pre-trained weights, the tasks they support, and their compatibility with various operating modes such as Inference, Validation, Training, and Export, denoted by ✅ for supported modes and ❌ for unsupported modes.

Note

All the YOLOv8-World weights have been directly migrated from the official YOLO-World repository, highlighting their excellent contributions.

Model Type Pre-trained Weights Tasks Supported Inference Validation Training Export
YOLOv8s-world yolov8s-world.pt Object Detection
YOLOv8s-worldv2 yolov8s-worldv2.pt Object Detection
YOLOv8m-world yolov8m-world.pt Object Detection
YOLOv8m-worldv2 yolov8m-worldv2.pt Object Detection
YOLOv8l-world yolov8l-world.pt Object Detection
YOLOv8l-worldv2 yolov8l-worldv2.pt Object Detection
YOLOv8x-world yolov8x-world.pt Object Detection
YOLOv8x-worldv2 yolov8x-worldv2.pt Object Detection

Zero-shot Transfer on COCO Dataset

Model Type mAP mAP50 mAP75
yolov8s-world 37.4 52.0 40.6
yolov8s-worldv2 37.7 52.2 41.0
yolov8m-world 42.0 57.0 45.6
yolov8m-worldv2 43.0 58.4 46.8
yolov8l-world 45.7 61.3 49.8
yolov8l-worldv2 45.8 61.3 49.8
yolov8x-world 47.0 63.0 51.2
yolov8x-worldv2 47.1 62.8 51.4

Usage Examples

The YOLO-World models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development.

Train Usage

Tip

We strongly recommend to use yolov8-worldv2 model for custom training, because it supports deterministic training and also easy to export other formats i.e onnx/tensorrt.

Object detection is straightforward with the train method, as illustrated below:

Example

PyTorch pretrained *.pt models as well as configuration *.yaml files can be passed to the YOLOWorld() class to create a model instance in python:

from ultralytics import YOLOWorld

# Load a pretrained YOLOv8s-worldv2 model
model = YOLOWorld('yolov8s-worldv2.pt')

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)

# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')
# Load a pretrained YOLOv8s-worldv2 model and train it on the COCO8 example dataset for 100 epochs
yolo train model=yolov8s-worldv2.yaml data=coco8.yaml epochs=100 imgsz=640

Predict Usage

Object detection is straightforward with the predict method, as illustrated below:

Example

from ultralytics import YOLOWorld

# Initialize a YOLO-World model
model = YOLOWorld('yolov8s-world.pt')  # or select yolov8m/l-world.pt for different sizes

# Execute inference with the YOLOv8s-world model on the specified image
results = model.predict('path/to/image.jpg')

# Show results
results[0].show()
# Perform object detection using a YOLO-World model
yolo predict model=yolov8s-world.pt source=path/to/image.jpg imgsz=640

This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.

Val Usage

Model validation on a dataset is streamlined as follows:

Example

from ultralytics import YOLO

# Create a YOLO-World model
model = YOLO('yolov8s-world.pt')  # or select yolov8m/l-world.pt for different sizes

# Conduct model validation on the COCO8 example dataset
metrics = model.val(data='coco8.yaml')
# Validate a YOLO-World model on the COCO8 dataset with a specified image size
yolo val model=yolov8s-world.pt data=coco8.yaml imgsz=640

Note

The YOLO-World models provided by Ultralytics come pre-configured with COCO dataset categories as part of their offline vocabulary, enhancing efficiency for immediate application. This integration allows the YOLOv8-World models to directly recognize and predict the 80 standard categories defined in the COCO dataset without requiring additional setup or customization.

Set prompts

YOLO-World prompt class names overview

The YOLO-World framework allows for the dynamic specification of classes through custom prompts, empowering users to tailor the model to their specific needs without retraining. This feature is particularly useful for adapting the model to new domains or specific tasks that were not originally part of the training data. By setting custom prompts, users can essentially guide the model's focus towards objects of interest, enhancing the relevance and accuracy of the detection results.

For instance, if your application only requires detecting 'person' and 'bus' objects, you can specify these classes directly:

Example

from ultralytics import YOLO

# Initialize a YOLO-World model
model = YOLO('yolov8s-world.pt')  # or choose yolov8m/l-world.pt

# Define custom classes
model.set_classes(["person", "bus"])

# Execute prediction for specified categories on an image
results = model.predict('path/to/image.jpg')

# Show results
results[0].show()

You can also save a model after setting custom classes. By doing this you create a version of the YOLO-World model that is specialized for your specific use case. This process embeds your custom class definitions directly into the model file, making the model ready to use with your specified classes without further adjustments. Follow these steps to save and load your custom YOLOv8 model:

Example

First load a YOLO-World model, set custom classes for it and save it:

from ultralytics import YOLO

# Initialize a YOLO-World model
model = YOLO('yolov8s-world.pt')  # or select yolov8m/l-world.pt

# Define custom classes
model.set_classes(["person", "bus"])

# Save the model with the defined offline vocabulary
model.save("custom_yolov8s.pt")

After saving, the custom_yolov8s.pt model behaves like any other pre-trained YOLOv8 model but with a key difference: it is now optimized to detect only the classes you have defined. This customization can significantly improve detection performance and efficiency for your specific application scenarios.

from ultralytics import YOLO

# Load your custom model
model = YOLO('custom_yolov8s.pt')

# Run inference to detect your custom classes
results = model.predict('path/to/image.jpg')

# Show results
results[0].show()

Benefits of Saving with Custom Vocabulary

  • Efficiency: Streamlines the detection process by focusing on relevant objects, reducing computational overhead and speeding up inference.
  • Flexibility: Allows for easy adaptation of the model to new or niche detection tasks without the need for extensive retraining or data collection.
  • Simplicity: Simplifies deployment by eliminating the need to repeatedly specify custom classes at runtime, making the model directly usable with its embedded vocabulary.
  • Performance: Enhances detection accuracy for specified classes by focusing the model's attention and resources on recognizing the defined objects.

This approach provides a powerful means of customizing state-of-the-art object detection models for specific tasks, making advanced AI more accessible and applicable to a broader range of practical applications.

Reproduce official results from scratch(Experimental)

Prepare datasets

  • Train data
Dataset Type Samples Boxes Annotation Files
Objects365v1 Detection 609k 9621k objects365_train.json
GQA Grounding 621k 3681k final_mixed_train_no_coco.json
Flickr30k Grounding 149k 641k final_flickr_separateGT_train.json
  • Val data
Dataset Type Annotation Files
LVIS minival Detection minival.txt

Launch training from scratch

Note

WorldTrainerFromScratch is highly customized to allow training yolo-world models on both detection datasets and grounding datasets simultaneously. More details please checkout ultralytics.model.yolo.world.train_world.py.

Example

from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
from ultralytics import YOLOWorld

data = dict(
    train=dict(
        yolo_data=["Objects365.yaml"],
        grounding_data=[
            dict(
                img_path="../datasets/flickr30k/images",
                json_file="../datasets/flickr30k/final_flickr_separateGT_train.json",
            ),
            dict(
                img_path="../datasets/GQA/images",
                json_file="../datasets/GQA/final_mixed_train_no_coco.json",
            ),
        ],
    ),
    val=dict(yolo_data=["lvis.yaml"]),
)
model = YOLOWorld("yolov8s-worldv2.yaml")
model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch)

Citations and Acknowledgements

We extend our gratitude to the Tencent AILab Computer Vision Center for their pioneering work in real-time open-vocabulary object detection with YOLO-World:

@article{cheng2024yolow,
title={YOLO-World: Real-Time Open-Vocabulary Object Detection},
author={Cheng, Tianheng and Song, Lin and Ge, Yixiao and Liu, Wenyu and Wang, Xinggang and Shan, Ying},
journal={arXiv preprint arXiv:2401.17270},
year={2024}
}

For further reading, the original YOLO-World paper is available on arXiv. The project's source code and additional resources can be accessed via their GitHub repository. We appreciate their commitment to advancing the field and sharing their valuable insights with the community.



Created 2024-02-14, Updated 2024-04-02
Authors: Burhan-Q (1), Laughing-q (4), glenn-jocher (1)

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