For machine learning to work, data are necessary. Models cannot be trained without data, and no insights can be gained without data. Thankfully, there are a number of locations where you may locate free datasets for machine learning synthesis.
The more data you have available during training, the better, yet data alone is insufficient. Making sure the datasets are of a high standard and applicable to the task at hand is equally crucial. Verify that the datasets aren’t excessively large at the outset. If the data is more than what the project needs in terms of rows or columns, you should probably spend some time cleaning it up.
In the world of computer vision, the proverb “a picture is worth a thousand words” is especially true. Face recognition software is being utilized increasingly frequently for security reasons as autonomous vehicles gain in popularity. Databases with images and videos are also used by the medical imaging technology sector to accurately diagnose patient problems through the machine learning process.
Millions of colored pictures may be found in the ImageNet collection, making it ideal for developing image categorization algorithms. This dataset can be used to train machine learning models for commercial applications, despite the fact that academic research uses it more frequently.
both CIFAR-10 and CIFAR-100
The CIFAR datasets are a collection of compact picture datasets that are frequently utilized in computer vision research. In comparison to the CIFAR-100 dataset, which has 100 classes of images, the CIFAR-10 dataset only comprises 10 classes. The datasets available here are ideal for developing and evaluating image classification methods.
The Coco Dataset
A comprehensive dataset for object detection, segmentation, and captioning is called the Coco Dataset. Machine learning models for object detection and segmentation can be trained and tested using this dataset.