Top 10 Pre-Labeled Image Datasets for Computer Vision

Are you tired of spending hours labeling your image datasets for machine learning? Do you want to jumpstart your computer vision projects with pre-labeled data? Look no further! We've compiled a list of the top 10 pre-labeled image datasets for computer vision that will save you time and effort.

1. ImageNet

ImageNet is a massive dataset of over 14 million images labeled with over 21,000 categories. It's one of the most widely used datasets for computer vision research and has been used to train many state-of-the-art models. ImageNet is a great choice for projects that require a large and diverse set of labeled images.

2. COCO

COCO (Common Objects in Context) is a dataset of over 330,000 images labeled with over 2.5 million object instances. It's a popular choice for object detection and segmentation tasks and has been used to train many popular models like Mask R-CNN and YOLO.

3. Open Images

Open Images is a dataset of over 9 million images labeled with over 36 million bounding boxes and segmentation masks. It's a great choice for projects that require a large and diverse set of labeled images, and it's also one of the few datasets that includes segmentation masks.

4. Pascal VOC

Pascal VOC (Visual Object Classes) is a dataset of over 20,000 images labeled with over 25,000 object instances. It's a popular choice for object detection and segmentation tasks and has been used to train many popular models like Faster R-CNN and SSD.

5. SUN

SUN (Scene Understanding) is a dataset of over 130,000 images labeled with over 900 scene categories. It's a great choice for projects that require a large and diverse set of labeled images for scene recognition tasks.

6. CIFAR-10

CIFAR-10 is a dataset of over 60,000 images labeled with 10 different classes. It's a popular choice for image classification tasks and has been used to train many popular models like ResNet and VGG.

7. MNIST

MNIST (Modified National Institute of Standards and Technology) is a dataset of over 70,000 images labeled with 10 different digits. It's a popular choice for image classification tasks and has been used to train many popular models like LeNet and MLP.

8. Fashion-MNIST

Fashion-MNIST is a dataset of over 70,000 images labeled with 10 different fashion categories. It's a popular choice for image classification tasks and has been used to train many popular models like CNN and DCGAN.

9. Caltech-101

Caltech-101 is a dataset of over 9,000 images labeled with over 100 object categories. It's a popular choice for object recognition tasks and has been used to train many popular models like HOG and SIFT.

10. Oxford Flowers

Oxford Flowers is a dataset of over 8,000 images labeled with 102 different flower categories. It's a popular choice for image classification tasks and has been used to train many popular models like AlexNet and GoogLeNet.

Conclusion

Pre-labeled image datasets are a great way to jumpstart your computer vision projects and save time and effort. The datasets we've listed here are some of the most widely used and popular datasets in the computer vision community. Whether you're working on object detection, segmentation, or classification tasks, there's a dataset here that will fit your needs. So what are you waiting for? Start exploring these pre-labeled image datasets and take your computer vision projects to the next level!

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