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PKU-DAVIS-SOD
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The PKU-DAVIS-SOD dataset is a large-scale multimodal neuromorphic object detection dataset with some challenging scenarios (e.g., low-light and high-speed motion blur) included. This dataset is recorded using DAVIS346 whose resolution is of 346 * 260. Our PKU-DAVIS-SOD dataset contains 3 traffic scenarios by considering velocity distribution, light condition, category diversity and object scale, etc. We use the DAVIS346 to record 220 sequences including RGB frames and DVS events. In each sequence, we collect approximately 1 min as the raw data pool with 25 FPS of RGB frames. Manual annotations in the recordings are provided at a frequency of 25 Hz. As a result, this dataset has 276k labeled timestamps and 1080.1k labels in total. Compared to other similar datasets, it has an overwhelming advantage in aspect of scale.

2022-07-22 5 8
PKU-Vidar-DVS-Dataset
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PKU-Vidar-DVS dataset is a large-scale multimodal neuromorphic object detection dataset with temporally continuous labels. This dataset is recorded using our hybrid camera system, which includes a Vidar (resolution 400*250) and a DAVIS346. This dataset contains 9 indoor and outdoor challenging scenarios by considering velocity distribution, illumination change, category diversity, and object scale, etc. We use the hybrid camera system to record 490 sequences including Vidar spikes and DVS events. In each sequence, we collect approximately 5 seconds as the raw data pool. Manual annotations in the recordings are provided at a frequency of 50 Hz. As a result, this dataset has 103.3k labeled timestamps and 229.5k labels in total. It is the first work to build a neuromorphic multimodal object detection dataset involving high-speed and low-light scenarios.

2022-07-17 5 30
Landsat_subset
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新疆地区近30年的Landsat卫星图,数据来自于USGS (https://www.usgs.gov/)

2022-07-06 2 0
duh-sentiment
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2021-04-28 0 6