description: Explore the YOLO-World Model for efficient, real-time open-vocabulary object detection using Ultralytics YOLOv8 advancements. Achieve top performance with minimal computation.
keywords: YOLO-World, Ultralytics, open-vocabulary detection, YOLOv8, real-time object detection, machine learning, computer vision, AI, deep learning, model training
The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://ultralytics.com) [YOLOv8](yolov8.md)-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 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](yolov8.md), 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](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), denoted by ✅ for supported modes and ❌ for unsupported modes.
!!! Note
All the YOLOv8-World weights have been directly migrated from the official [YOLO-World](https://github.com/AILab-CVC/YOLO-World) repository, highlighting their excellent contributions.
The YOLO-World models are easy to integrate into your Python applications. Ultralytics provides user-friendly Python API and CLI commands to streamline development.
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
=== "Python"
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:
The YOLO-World models provided by Ultralytics come pre-configured with [COCO dataset](../datasets/detect/coco.md) 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.
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:
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
=== "Persisting Models with Custom Vocabulary"
First load a YOLO-World model, set custom classes for it and save it:
# 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.
- **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.
`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](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/world/train_world.py).
We extend our gratitude to the [Tencent AILab Computer Vision Center](https://ai.tencent.com/) for their pioneering work in real-time open-vocabulary object detection with YOLO-World:
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](https://arxiv.org/pdf/2401.17270v2.pdf). The project's source code and additional resources can be accessed via their [GitHub repository](https://github.com/AILab-CVC/YOLO-World). We appreciate their commitment to advancing the field and sharing their valuable insights with the community.
### What is the YOLO-World Model and how does it improve open-vocabulary object detection?
The YOLO-World Model is an advanced, real-time object detection model based on Ultralytics YOLOv8, designed specifically for open-vocabulary detection tasks. It leverages vision-language modeling and pre-training on large datasets to detect a broad range of objects based on descriptive texts, significantly reducing computational demands while maintaining high performance. This makes it suitable for real-time applications across various industries needing immediate results.
### How do I train a custom YOLO-World Model using the Ultralytics API?
Training a custom YOLO-World Model is straightforward with Ultralytics' API. You can use pretrained weights and configuration files to start training on your dataset. Here is an example of training with Python:
```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
# Run inference with the YOLOv8s-worldv2 model on an image
results = model("path/to/image.jpg")
```
Refer to the [Training](../modes/train.md) page for more details.
### What are the main advantages of using YOLO-World for object detection?
YOLO-World offers several advantages:
- **Real-time detection**: Utilizes CNNs for high-speed inference.
- **Lower computational demand**: Efficiently processes images with minimal resources.
- **Open-vocabulary detection**: Detects objects without predefined categories, based on descriptive texts.
- **High performance**: Outperforms other models on standard benchmarks while running on a single NVIDIA V100 GPU.
### Can I customize the classes YOLO-World detects without retraining the model?
Yes, YOLO-World allows you to dynamically specify detection classes through custom prompts without retraining the model. Here's an example of how to set custom classes:
```python
from ultralytics import YOLOWorld
# Initialize a YOLO-World model
model = YOLOWorld("yolov8s-world.pt")
# Define custom classes
model.set_classes(["person", "bus"])
# Execute prediction on an image
results = model.predict("path/to/image.jpg")
results[0].show()
```
You can learn more about this feature on the [Predict Usage](#predict-usage) section.
### What datasets are supported for training YOLO-World from scratch?
YOLO-World supports various datasets for training, including Objects365, GQA, and Flickr30k for detection and grounding tasks. For validation, it supports datasets like LVIS minival. Detailed information about preparing and using these datasets can be found in the [Zero-shot Transfer on COCO Dataset](#zero-shot-transfer-on-coco-dataset) section.
