Omkar2118/datasets huggingface dataset:
PS-Plant dataset
Full name: PS-Plant dataset

Automated leaf segmentation is a challenging area in computer vision. Recent advances in machine learning approaches allowed to achieve better results than traditional image processing techniques; however, training such systems often require large annotated data sets. To contribute with annotated data sets and help to overcome this bottleneck in plant phenotyping research, here we provide a novel photometric stereo (PS) data set with annotated leaf masks. This data set forms part of the work done in the BBSRC Tools and Resources Development project BB/N02334X/1.

Source: [A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth](https://academic.oup.com/gigascience/article/8/5/giz056/5498634)
Image Source: [PS-Plant training dataset description](https://datashare.ed.ac.uk/bitstream/handle/10283/3280/PS-Plant%20training%20data%20set%20description.pdf?sequence=3&isAllowed=y)
Real SVBRDF

A total of 80 real material samples were captured in a dark room. For each material, multiple captures were collected at different distances from the camera (between 250 and 650 mm) to observe both macro- and micro-level details. The dataset is mostly comprised of planar specimens but also includes non-planar objects such as mugs, globes, crumpled paper, etc. As shown above, it contains a rich diversity of materials, including diffuse or specular wrapping papers, fabrics, anisotropic metals, plastics, rugs, ceramic and wood flooring samples, etc. Each capture set includes 12 LDR (8 bpp) RGB-D images at 4K pixel resolution. Each set is captured at 50% and 100% of maximum light intensity. In total, we captured 462 such image sets (combinations of light intensities, distances to the camera, and material sample).

Introducing paper: Deep SVBRDF Estimation on Real Materials