{"task":"Instance Segmentation","dataset":"ARMBench","metric_names":["AP50","AP75"],"rows":[{"id":85916,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"RISE (VIT-B)","metrics":{"AP50":"86.37","AP75":"77.51"},"paper_url":"https://arxiv.org/abs/2407.01302v2","paper_title":"Robot Instance Segmentation with Few Annotations for Grasping","paper_date":"2024-07-01","code_links":[{"title":"mkimhi/RISE","url":"https://github.com/mkimhi/RISE"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85917,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"RISE (R101)","metrics":{"AP50":"84.74","AP75":"75.93"},"paper_url":"https://arxiv.org/abs/2407.01302v2","paper_title":"Robot Instance Segmentation with Few Annotations for Grasping","paper_date":"2024-07-01","code_links":[{"title":"mkimhi/RISE","url":"https://github.com/mkimhi/RISE"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85918,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"RISE (R50)","metrics":{"AP50":"83.53","AP75":"75.15"},"paper_url":"https://arxiv.org/abs/2407.01302v2","paper_title":"Robot Instance Segmentation with Few Annotations for Grasping","paper_date":"2024-07-01","code_links":[{"title":"mkimhi/RISE","url":"https://github.com/mkimhi/RISE"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85919,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"RoboLLM (VIT-B)","metrics":{"AP50":"82.0","AP75":"74"},"paper_url":"https://arxiv.org/abs/2310.10221v2","paper_title":"RoboLLM: Robotic Vision Tasks Grounded on Multimodal Large Language Models","paper_date":"2023-10-16","code_links":[{"title":"longkukuhi/armbench","url":"https://github.com/longkukuhi/armbench"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85920,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"Mask2Former","metrics":{"AP50":"81.2","AP75":"74.0"},"paper_url":"https://arxiv.org/abs/2407.01302v2","paper_title":"Robot Instance Segmentation with Few Annotations for Grasping","paper_date":"2024-07-01","code_links":[{"title":"mkimhi/RISE","url":"https://github.com/mkimhi/RISE"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85921,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"Deformable DETR","metrics":{"AP50":"77.03","AP75":"63.4"},"paper_url":"https://arxiv.org/abs/2407.01302v2","paper_title":"Robot Instance Segmentation with Few Annotations for Grasping","paper_date":"2024-07-01","code_links":[{"title":"mkimhi/RISE","url":"https://github.com/mkimhi/RISE"}],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":85922,"task":"Instance Segmentation","parent_task":null,"dataset":"ARMBench","model_name":"Mask R-CNN (Resnet50)","metrics":{"AP50":"72","AP75":"61"},"paper_url":"https://arxiv.org/abs/2303.16382v1","paper_title":"ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation","paper_date":"2023-03-29","code_links":[],"metrics_order":"[\"AP50\", \"AP75\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}