Today I'm excited to kick off our first-ever Computer Vision Tips blog! I hope you find it useful and I encourage you to let me know your thoughts either in the comments below or in the poll at the end of the blog.

How to Select the Best Model for Your Object Detection Project 🔎 

When chasing improved performance, it's all too easy to become fixated on hunting down the 'best' model among the latest, state-of-the-art offerings. But the reality is that many of today's object detection models are remarkably proficient and most likely will meet your needs. The true challenge often doesn't reside in the model, but rather within the dataset. The curation, quality of labels, and accessibility of the data often demand more of our attention and resources and usually yield more significant performance gains than the model itself.

That said, selecting the optimal computer vision model requires consideration of several key factors. In this blog, I'll share insights gleaned from my own experience and the wisdom of industry leaders and computer vision experts. We'll delve into the considerations for choosing the best-suited model for your object detection project.

The Big Picture

Object detection is a popular task in computer vision, with new models emerging rapidly. MS COCO is the standard dataset often used to evaluate these models, with mean average precision as a crucial accuracy metric. Generally, state-of-the-art methods fall into two categories: one-stage methods prioritizing fast inference speed (ideal for real-time applications), and two-stage methods which focus on accuracy, sometimes compromising on speed.

The one-stage methods include models like those from the YOLO family, with the latest and much-discussed versions being YOLOv8 and YOLO-NAS. On the other hand, two-stage methods, such as the R-CNN and its variants, propose regions of interest in the initial stage, followed by a classification stage.

It's important to highlight the increasing use of vision transformers in various computer vision tasks. Specifically for object detection, the Detection Transformer, or DETR, has been surpassing recent benchmarks in object detection.

Your Use Case is Your Guide

Your project requirements, particularly the balance between speed and accuracy, play a pivotal role when choosing a model. Let's examine two contrasting use cases to clarify:

🥑 Object Detection for a Retail Store: Let's say you're developing a solution for an autonomous retail store, to monitor stock levels and alert the store manager when it's time for a restock. The model should accurately and quickly count and track products on shelves. If it operates at only 5 FPS, seamlessly integrating an effective tracker and accurately counting objects will be challenging.

Given that your internet connection may be unreliable, the model should be able to run on a low-compute device. This requires the model to be not just compact but also have a low memory footprint when loaded. In this case, one-stage detectors like YOLO models would be a suitable choice.

🩻 Medical Imaging Analysis: While we still want speed, it's not as critical, provided it's reasonable. Real-time processing isn't a must-have, and there's a good chance you'll have the power of cloud computing resources. Instead, your spotlight should be on prediction accuracy and precision. For this, a large-scale, highly accurate object detection model like a two-stage model, or better yet, an instance segmentation model, might be your ideal companion.

A Deeper Look at Accuracy vs Latency

Typically, you'll find charts benchmarking models on accuracy and latency metrics. Models in the top left corner are generally preferred as they suggest a good balance of speed and accuracy. However, understanding these metrics requires context.

💯 Accuracy: Benchmark values, including the mAP metric, are often based on established datasets like COCO. But, remember, your custom dataset might differ significantly from COCO. Therefore, fine-tuning your model on your dataset could considerably affect the benchmark mAP value. A model's performance on the COCO dataset doesn't guarantee a similar result on your custom dataset. Starting a new project with a pre-trained model and fine-tuning it on your dataset will offer a realistic sense of the model's potential performance in your use case.

🚀 Latency: Note that reported latency values depend on the hardware and dataset used for benchmarking. If you plan to deploy on different hardware, performance, and latency might vary. Run a basic version of your application for preliminary testing to get a practical sense of the model's performance on your specific hardware.

Other Factors to Consider

💻️ Hardware Compatibility and Interoperability: As models are deployed across various platforms today - from the cloud to the edge, to web browsers - the ability to deploy your model on the intended platform is a major factor in choosing a model. Libraries housing models can significantly influence the ease of deploying the model on different hardware or runtimes. For instance, the Detectron2 library simplifies model conversion into various formats.

🧑‍💻 Ease of Use and Support: Assess how user-friendly the model is. Check the library's GitHub repository for the frequency of code updates and responsiveness to issues. The model and its associated library should ideally be installable via pip, and they should receive active support and regular updates.

🛠️ SDK: If your model is part of a larger framework, you may need a Software Development Kit (SDK) that facilitates its integration with your application.

🚨 License: Review the model's license to understand its permissible uses. Licenses vary from permissive (like MIT and Apache License) to restrictive. If in doubt, reach out to the creators for clarification.

Wrapping Up

Choosing the right model architecture for your object detection task isn't simply about chasing the most advanced, state-of-the-art model. Your use case, hardware, ease of use, support, and licensing should all factor into your decision. But perhaps most importantly, remember that prioritizing the construction of a high-quality dataset often trumps the quest for the 'perfect' model. Especially for real-world deployment projects, this comprehensive approach can set you up for success in your computer vision journey.

Share Your Thoughts

I'm interested to hear what factors you consider when choosing a model for object detection. Perhaps there's something I've overlooked, and I'm always eager to learn. Please feel free to share your thoughts in the comment section below this blog. 👇️