To set the stage: what exactly “data curation” means in this context. For those of you who are new to the game, data curation involves selecting and organizing datasets for specific purposes or projects. In computer vision, this typically involves collecting images (or videos) that have been labeled with relevant information such as object classifications, bounding boxes, and annotations.

Now, let’s get into some of our favorite tips for data curation in the world of computer vision:

1. Start small don’t try to tackle a massive dataset right off the bat. Instead, focus on collecting a smaller set of high-quality images that are relevant to your project. This will not only save you time and resources but also help ensure that your model is trained on data that accurately reflects what it needs to learn.

2. Be selective don’t just grab any old image off the internet. Instead, look for images that have been carefully curated by experts in the field or that are part of a reputable dataset. This will help ensure that your model is trained on data that accurately reflects what it needs to learn.

3. Use annotations don’t rely solely on image recognition algorithms to label your data. Instead, use human annotators (or crowdsourcing platforms) to provide more accurate and detailed labels for each image. This will help ensure that your model is trained on data that accurately reflects what it needs to learn.

4. Clean up the mess don’t forget to clean up any noise or irrelevant information in your dataset. This can include removing duplicates, cropping images, and adjusting brightness/contrast levels. By doing so, you’ll help ensure that your model is trained on data that accurately reflects what it needs to learn.

5. Don’t forget about metadata don’t just focus on the image itself. Instead, pay attention to any relevant metadata such as camera settings or location information. This can provide valuable insights into how the images were captured and help ensure that your model is trained on data that accurately reflects what it needs to learn.

6. Keep track of everything don’t forget to keep detailed records of all the datasets you use, including where they came from, who curated them, and any relevant metadata. This will not only make it easier for others to replicate your results but also help ensure that your model is trained on data that accurately reflects what it needs to learn.

7. Have fun with it don’t forget to enjoy the process! Data curation can be a tedious and time-consuming task, but it can also be incredibly rewarding (especially when you see how well your model performs). So put on some music, grab a cup of coffee, and let the data curating begin!