Discover the Best Object Detection Models for 2023

Discover the Best Object Detection Models for 2023

Table of Contents

  1. Introduction
  2. Methodology for Comparing Object Detectors
  3. Choosing the Right Object Detector
  4. Object Detection Models for Real-Time Processing
    • Yellow V8
    • Yellow V7
    • Yellow V6 V3
    • RTM Det
  5. Transformer-Based Object Detection
    • RTD TR
    • DTA
  6. Grounding Dyno: A Multi-Modal Model
  7. Considering the Community Support
  8. Conclusion
  9. Resources

📚 Choosing the Right Object Detector in 2023

In the fast-paced world of computer vision, new object detection models are released every year, making it challenging to determine which model to use for your project. In this article, we will explore the top object detectors to consider in 2023. Before diving into the models, let's take a look at the methodology we used to compare them.

Methodology for Comparing Object Detectors

To ensure a fair comparison, we grouped the models based on their specific tasks. For example, if You require real-time processing on edge devices, you would prioritize models capable of running efficiently in such scenarios. On the other HAND, if prediction quality is crucial, such as in medical image analysis, accuracy becomes the primary concern. Additionally, we considered zero-shot object detectors that combine information from text and images, allowing you to detect objects without training on specific classes.

To evaluate the models, we used the Mean Average Precision (mAP) on the COCO dataset, as it is the industry standard for benchmarking object detectors. However, it's important to note that mAP on COCO is just an indication of a model's capability, and the performance may vary when applied to custom datasets.

Object Detection Models for Real-Time Processing

🟡 Yellow V8

Yellow V8, the latest installment of the Yellow architecture released by Ultralytics, offers multiple versions ranging from Nano to Extra Large. With mAP scores between 37.3 and 53.9 on the COCO validation dataset, Yellow V8 proves to be accurate and efficient for real-time object detection tasks.

🟡 Yellow V7

Yellow V7, introduced in CVPR 2023, holds the third position in the real-time object detection category on Papers with Code leaderboard. While it achieves an impressive mAP of 56.8, it's important to consider that this score was obtained using a 1280 input resolution. When considering versions trained on a 640 input, its score drops to 53.1.

🟡 Yellow V6 V3

Yellow V6 V3 claims the top spot in real-time object detection with an mAP of 57.2 on the COCO validation dataset. However, similar to Yellow V7, this score was achieved with a 1280 input resolution. When considering the 640 input versions, Yellow V6 V3's mAP drops to 52.8.

RTM Det

RTM Det, initially not on our list, was recommended by the community. With varying sizes available, its largest version achieves a respectable mAP of 52.8 on the COCO validation dataset. Additionally, RTM Det demonstrates high speeds, reaching over 300 FPS on an RTX 3090 in a TensorRT environment.

Transformer-Based Object Detection

RTD TR

RTD TR is the first transformer-based model on our list, and it marks the gradual shift from convolutional neural networks to transformers in computer vision. While it may not be as fast as other architectures on our list, with FPS ranging from 74 to 114 on an Nvidia T4 in a TensorRT environment, its accuracy is impressive, achieving an mAP of 54.8 with a 640 input resolution on COCO validation dataset.

DTA

DTA, part of the Transformers Package, offers various options for transformer-based object detection. With an mAP of 63.5, DTA currently holds the 13th position on the Papers with Code object detection leaderboard. Although we couldn't find specific speed benchmarks for this model, it performs well on Tesla V100 GPUs, with FPS ranging from 4 to 13 depending on the backbone.

Grounding Dyno: A Multi-Modal Model

Grounding Dyno stands out as a multi-modal model, trained on both text and images. It allows you to detect objects without the need for training on specific classes. For example, you can prompt the model with a text description and it will identify objects accordingly. Grounding Dyno outperforms other models in zero-shot object detection, achieving state-of-the-art results on both the Object Detection in the Wild and COCO datasets.

Considering the Community Support

Apart from the technical aspects, community support plays a vital role in choosing the right object detector. A strong community ensures better documentation, tutorials, and a network of individuals facing similar challenges. When evaluating models, consider factors such as the number of stars, forks, contributors, active pull requests, and issues on the project's repository.

In conclusion, selecting the ideal object detector involves analyzing multiple factors such as accuracy, speed, license, and community support. By considering the specific requirements of your project and referring to this list of top models, you can make an informed decision. Remember to explore the available resources and consult with the model authors for additional guidance.

🔍 Resources:


Highlights

  • The top object detectors for 2023 include Yellow V8, Yellow V7, Yellow V6 V3, RTM Det, RTD TR, DTA, and Grounding Dyno.
  • Object detection models need to be chosen based on the specific requirements of the project, such as real-time processing or high prediction quality.
  • Mean Average Precision (mAP) on the COCO dataset is a commonly used metric to evaluate the performance of object detectors.
  • Yellow V8 offers accuracy and efficiency for real-time object detection tasks.
  • Transformer-based models like RTD TR and DTA are gaining popularity in computer vision tasks.
  • Grounding Dyno, a multi-modal model, allows for zero-shot object detection without specific class training.
  • Community support, including documentation and active contributors, is an important factor to consider when selecting an object detector.

Frequently Asked Questions

Q: Can I use these models in real-time applications? A: Yes, models like Yellow V8 and RTM Det are designed for real-time object detection tasks and offer efficient performance.

Q: Are there any pre-trained weights available for these models? A: Yes, most of the models mentioned in this article have pre-trained weights available, which can be used for transfer learning or as a starting point for your project.

Q: Can I extend these models to detect custom objects/classes? A: Yes, you can fine-tune these models on your own dataset with custom objects/classes to adapt them to your specific application.

Q: Are there any limitations or licensing restrictions with these models? A: It's important to check the license associated with each model to ensure compliance with your project requirements. Models distributed under MIT or Apache licenses are generally safe to use in closed-sourced projects.

Q: How can I get started with training and using these models? A: The Ultralytics GitHub repository provides notebooks and examples demonstrating the training and usage of the models discussed in this article.

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