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In-Depth Analysis of the Best Object Detection Models for 2024
Object detection is an essential and fast-evolving area within computer vision, with dozens of new models emerging annually. Selecting the appropriate model for your project can be challenging, so we have thoroughly reviewed the papers and crunched the numbers. Below is an in-depth analysis of the top object detection models for 2024.
To ensure a comprehensive evaluation, we categorize the models by task and assess them using various key metrics:
Different projects have varying requirements, so we categorize models based on the specific tasks they are best suited for:
1. Real-Time Video Stream Processing
These projects necessitate models that can operate on edge devices without substantial cloud GPU resources, often accepting a trade-off between accuracy and performance.
2. Detection Requiring High Prediction Quality
3. Zero-Shot Object Detectors
1. Mean Average Precision (mAP): Despite its limitations in transparency and nuance, mAP remains the industry standard for evaluating object detection models. It provides a general indication of a model's capability when measured on the COCO dataset. However, it is important to note that the mAP scores can vary considerably when fine-tuning your model on custom datasets.
2. Paper Availability: The presence of a supporting published paper can enhance a model's credibility and ease of adoption.
3. Licensing: The ease of deployment and the type of license (e.g., MIT, Apache 2.0) are critical, especially for commercial applications.
4. Popularity: By examining GitHub activity, such as stars and issues, we can gauge a model's popularity and the level of support it receives.
5. Implementation: We assess whether the model's inference and training processes are packaged for ease of use. This includes considering if the model can be easily installed and utilized with minimal setup or if it requires installation from source, which often involves managing dependencies, ensuring dataset format compatibility, and other potential complications.
Note on speed
While speed is an important factor, accurately assessing and comparing it is challenging due to variations in hardware, runtimes, and configurations. As a result, we mention speed but do not heavily weigh it in our comparisons. For precise speed comparisons, models should ideally be tested in standardized environments, which are not available to us at this time.
Table comparison of the real-time object detection models. 1 test size: 640; 2 test size: 1280
The YOLO (You Only Look Once) series remains one of the most popular and widely adopted real-time object detection frameworks in the field of computer vision. Known for its speed and accuracy, the YOLO series has evolved significantly over the years.
The latest iterations, including YOLOv10, YOLOv9, YOLOv8, and YOLOv7, demonstrate robust performance on the COCO dataset, with varying degrees of mean Average Precision (mAP) and other metrics.
RTMDet
RTMDet stands out as a significant contender in the object detection landscape, achieving an impressive mAP range of 41.0 - 52.8. RTMDet is particularly praised for its packaging convenience from MMDet, making it easily deployable in notebook environments. The key difference between RTMDet and YOLO models lies in its balanced approach to speed and accuracy, catering to applications where these two factors are equally critical.
RT-DETR
RT-DETR is the only Transformer-based architecture on the list, bringing a unique approach to object detection. With an mAP of 46.5 - 54.8, it offers competitive accuracy. However, the use of Transformer architecture makes RT-DETR inherently slower compared to the CNN-based YOLO models and RTMDet. Despite this, it has a solid support base with 1.5k stars and 162 forks. The slower speed is compensated by the model's advanced capabilities in handling complex detection tasks, making it a valuable tool for applications that prioritize accuracy and model sophistication over real-time performance.
The YOLO series remains a go-to choice for many due to its proven track record and widespread community support. While YOLOv9 has slightly higher performance than YOLOv10, the authors of YOLOv10 claim that, compared to YOLOv9-C, YOLOv10-B achieves a 46% reduction in latency while maintaining the same performance level. Given the strengths of both models, it is hard to recommend one over the other definitively, as each has its unique advantages depending on the specific requirements of a project.
In this section, we will explore the best object detection models, prioritizing accuracy and performance over speed considerations.
The transformer-based models (Swin-L) such as Co-DETR and DETA are setting new benchmarks in object detection accuracy, though they may not always be the fastest. These models are ideal for applications where accuracy is the top priority. Co-DETR, with its high mAP and robust community support, and DETA, with its significant engagement and modification levels, are leading the way. Swin-L is also anticipated to be a top performer, with details to follow.
For projects where speed is less of a concern, these models offer superior performance and the flexibility of transformer-based architectures, providing a glimpse into the future of object detection technology.
Zero-shot object detectors are models that can detect objects without having been explicitly trained on those specific classes. These models leverage the power of both textual and visual data to identify objects, making them extremely versatile and powerful for a wide range of applications.
Here, we examine some of the leading zero-shot object detection models based on their performance and community support.
1 Inference via the API, the weights are not available.
Zero-shot object detectors like Grounding Dino 1.5 Pro and OWLv2 are pushing the boundaries of what is possible in object detection without explicit class training. For projects requiring the best accuracy in zero-shot inference, we recommend OWLv2 over Grounding Dino 1.5 Pro due to its HuggingFace implementation and the availability of weights. If your project is more focused on speed, YOLO-world allows for real-time zero-shot inference, offering a practical solution for time-sensitive applications.
In 2024, the field of object detection offers a variety of models tailored for different needs. The YOLO series remains highly popular for real-time detection due to its speed and accuracy. YOLOv10 stands out with reduced latency while maintaining performance, and YOLOv9 offers slightly higher accuracy.
In zero-shot object detection, Grounding Dino 1.5 Pro leads in accuracy but requires API-based inference. OWLv2, with its HuggingFace implementation and available weights, is more accessible for high-accuracy tasks. Grounding Dino and YOLO-world provide additional options, with YOLO-world excelling in real-time inference.
Choosing the right model depends on your project's specific requirements for speed, accuracy, and ease of deployment. The advancements in object detection continue to enhance the capabilities of computer vision applications.
