Collection of Vision Benchmarks ( Image Datasets with their brief Description)

 

ImageNet

• Description: A large-scale image dataset organized according to the WordNet hierarchy.

• Size: Over 14 million images labeled across 21,000+ categories.

• Use: Widely used for training and benchmarking deep learning models, especially for large-scale image classification.

• Famous Benchmark: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC), which uses a subset of 1,000 classes.

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CIFAR-100

• Description: A small image classification dataset with 60,000 32x32 color images in 100 classes (600 images per class).

• Split: 50,000 training and 10,000 test images.

• Hierarchy: Also organized into 20 superclasses (each containing 5 fine classes).

• Use: Suitable for testing model performance on small, diverse datasets.

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VTAB (Visual Task Adaptation Benchmark)

• Description: A benchmark suite designed to evaluate the generalization of pre-trained models across 19 real-world vision tasks.

• Categories: Tasks are grouped into:

  • Natural (e.g., CIFAR, DTD),

  • Specialized (e.g., satellite images),

  • Structured (e.g., depth estimation, keypoint detection).

• Use: Tests how well models transfer to new domains and tasks with minimal fine-tuning.

ImageNet-21k

• Description: A larger version of the standard ImageNet dataset.

• Classes: ~21,000 classes based on the full WordNet hierarchy.

• Images: ~14 million labeled images.

• Use: Commonly used for pretraining models before fine-tuning on downstream tasks (e.g., ImageNet-1k, CIFAR, VTAB).

• Note: Much more fine-grained and hierarchical than ImageNet-1k.

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JFT-300M

• Description: A massive, proprietary dataset created by Google.

• Images: ~300 million images.

• Labels: ~18,000 noisy labels from an internal Google label space.

• Use: Used to pretrain large-scale vision models (e.g., Vision Transformers) that achieve state-of-the-art performance after fine-tuning.

• Not Public: This dataset is not publicly available.

Oxford-IIIT Pet Dataset (Oxford Pets)

• Description: A dataset of 7,349 images of cats and dogs across 37 pet breeds.

• Tasks Supported:

  • Image classification (breed or species: cat/dog)

  • Object detection (head bounding boxes)

  • Semantic segmentation (pixel-level pet masks)

• Variability: Includes pets in diverse poses, scales, and lighting conditions.

• Applications: Fine-grained classification, segmentation, and transfer learning.

It’s commonly used for training and evaluating models on fine-grained visual recognition and segmentation tasks.