Camera
Datasets
All datasets matching “Camera”CameraBench
📷 CameraBench: Towards Understanding Camera Motions in Any Video
SfMs and VLMs performance on CameraBench: Generative VLMs (evaluated with VQAScore) trail classical SfM/SLAM in pure geometry, yet they outperform discriminative VLMs that rely on CLIPScore/ITMScore and—even better—capture scene‑aware semantic cues missed by SfM
After simple supervised fine‑tuning (SFT) on ≈1,400 extra annotated clips, our 7B Qwen2.5‑VL doubles its AP, outperforming the current best… See the full description on the dataset page: https://huggingface.co/datasets/syCen/CameraBench.IDLE-OO-Camera-Traps
Dataset Card for IDLE-OO Camera Traps
IDLE-OO Camera Traps is a 5-dataset benchmark of camera trap images from the Labeled Information Library of Alexandria: Biology and Conservation (LILA BC) with a total of 2,586 images for species classification. Each of the 5 benchmarks is balanced to have the same number of images for each species within it (between 310 and 1120 images), representing between 16 and 39 species.
Supported Tasks and Leaderboards
Image… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/IDLE-OO-Camera-Traps.CameraBenchProcamera_pizza_additionalcamera-movement-human-preference-324k
Rapidata Camera Movement Benchmark
Built by Rapidata.
This dataset contains 324,044 human responses, collected with the
Rapidata Python SDK, comparing how well 15 image-to-video models and world models
execute a described camera movement from a single still image. Each row is a head-to-head comparison between
two models' clips generated from the same still and the same instruction, judged by human annotators who
watched a reference animation of the requested movement.
The task… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/camera-movement-human-preference-324k.Robocasa_Camera_Space_Eef
RoboCasa Camera-Space EEF (ACE-Ego-0)
LeRobot v2.0 demonstrations for the 24 GR1 RoboCasa TableTop tasks used by
ACE-Ego-0 SFT. Actions and states
are stored in the observation camera frame (end-effector position + rot6d, plus
gripper and waist).
This dataset does not include unused conversion caches (experiments/) or
legacy per-dataset index files. Training reads only the files listed below and
the shipped merged q99 statistics. The training code never recomputes norms.… See the full description on the dataset page: https://huggingface.co/datasets/ACERobotics/Robocasa_Camera_Space_Eef.