How do you ensure your computer vision data sets are diverse and high quality?

Here’s what I follow to ensure the quality of computer vision datasets: 1. Use tools like Labelbox, CVAT, or SageMaker Ground Truth to annotate and label datasets accurately. 2. Maintain consistency in annotations and labels to improve dataset quality. 3. Continuously test and evaluate computer vision models to assess performance and accuracy. 4. Follow tips and tricks to optimize the performance and accuracy of computer vision models. To build a career in AI and computer vision: 1. Start with fundamental concepts in AI and computer vision. 2. Select a specific area of computer vision that interests you. 3. Create projects and build a portfolio to showcase your skills. 4. Networking 🖥️👁️🗨️ #ComputerVision #AI #CareerAdvice

A good problem definition can go a long way. Aside from clear objectives, you should also define the range of all possible inputs. This will help you to address diversity later down the line.

Ensuring diverse, high-quality computer vision datasets is key. Incorporate a range of variations like age, gender, and ethnicity. Use data augmentation to expand datasets. Address biases to improve model fairness. Ensure annotations are accurate and relevant. While synthetic data is valuable, it must complement real data. Data should align with the specific problem domain. Periodic model testing on new data is vital. Tools like Labelbox aid in annotation. Always obtain consent when collecting personal data.

📖Data Annotation is really important for computer vision algorithms. How well the computer can understand images mostly depends on how well the data is annotated, but not all the time. There are moments when adjusting certain hyperparameters of the algorithm can create problems, and sometimes specific algorithms are not designed for that use case. 🌟 Data annotation can be automated and sped up using the latest algorithm from META known as Segment Anything (SAM). It reduces a lot of annotation time when dealing with large volumes of data.

CVAT is an open-source annotation tool designed specifically for computer vision tasks, including object detection and image segmentation. It can be deployed on your own servers or in the cloud, making it a cost-effective choice for organizations with specific annotation needs. However, if you are working within the AWS ecosystem, have complex labeling workflows, and require scalability for large datasets, SageMaker Ground Truth can be a good choice. Some tools, like Labelbox and Supervise.ly, offer semi-automated annotation features, where machine learning models assist annotators in speeding up the annotation process.

Data labeling is always a trade-off. Ideally, you'd want top experts to label the data. But their time is expensive and companies will always try to minimize annotation costs. And depending on the size of the dataset, it might not even be feasible because it would take way too long. It's very difficult to secure data quality at scale, don't underestimate it! High quality datasets are often the result of clear guidelines that were refined iteratively over a long period of time. If you can afford it, I would advise using multiple raters and inter-rater agreement strategies.

I would add that GANs (Generative Adversarial Networks) can be used for image augmentation. Of course this comes with its own difficulties such as training and turning the GAN.

Additionally, when dealing with limited data, employing cross-validation can be a valuable technique. By dividing your dataset into 'k' folds, you can iteratively use different subsets for training and validation. This ensures that you maximize the utility of your available data and obtain more robust insights into your computer vision model's performance. Cross-validation, combined with splitting and balancing, helps in achieving accurate and fair computer vision models, even with limited data. Leveraging tools like scikit-learn, pandas, or PyTorch for these operations further streamlines the process.

You can use bootstrapping practices to "create" new datasets from existing data. This method can be helpful for training models in data-poor environments.

Reviewing and monitoring datasets are crucial for robust computer vision models. Regular reviews safeguard against biases and inaccuracies that might infiltrate during data collection or annotation. Monitoring provides insights into the dataset's influence on model performance, adapting to the ever-evolving real-world scenarios. As the world changes, old data may become obsolete, or unforeseen patterns may arise. Continuous monitoring identifies these shifts, enabling prompt adjustments. In essence, these steps ensure models remain accurate, fair, and reliable, reflecting the dynamic nature of the world they interpret.

Diversity is a huge issue in AI. Unfortunately, most datasets have massive amounts of bias - mostly unintentional. It is critical that we consider this bias and seek to reduce and/or eliminate it as we move into an AI powered future. If we do not do this, the consequences may be dire indeed for us all...