physicl-community/pack-c1v-of-my-project-weqz04-1c95902b
Sports Ground Field Markings Detection — Eval Evaluation dataset of 3 renders staged in a sports ground environment (NFINITE-PUB_987-SPORTS_GROUND-LS-UN) to measure how well a custom computer-vision model detects ground/field markings (lines, boundaries). Renders at 1024x1024 with albedo, metric depth, relative depth, world normals (OpenGL linear) and material index passes, per-frame annotations enabled, and the environment's authored lighting. This dataset mirrors public… See the full description on the dataset page: https://huggingface.co/datasets/physicl-community/pack-c1v-of-my-project-weqz04-1c95902b.
Sports Ground Field Markings Detection — Eval
Evaluation dataset of 3 renders staged in a sports ground environment (NFINITE-PUB987-SPORTSGROUND-LS-UN) to measure how well a custom computer-vision model detects ground/field markings (lines, boundaries). Renders at 1024x1024 with albedo, metric depth, relative depth, world normals (OpenGL linear) and material index passes, per-frame annotations enabled, and the environment's authored lighting.
This dataset mirrors public data-pack render outputs from Physicl.
Each row represents one render view. The image column contains a stable URL to the primary render image uploaded under /data; image_path stores the relative repository path and data_commit_sha pins the Hugging Face dataset commit used by those URLs. Files are uploaded as downloaded unless optional PNG recompression is enabled by the sync operator.
Additional render passes are exposed as URL columns, including .exr passes such as depth_metric that the dataset viewer cannot preview but that are downloadable from /data. The metadata column is the Core renderOutput.metadata value serialized as JSON, without Hugging Face-specific enrichment.
Dataset repo: https://huggingface.co/datasets/physicl-community/pack-c1v-of-my-project-weqz04-1c95902b