![]() Support for GLIP and Grounding DINO fine-tuning, the only algorithm library that supports Grounding DINO fine-tuning Users can utilize this benchmark to quickly evaluate the robustness of their algorithms in various scenarios.ģ. It can be used to assess the performance differences of Transformer models like DINO and CNN-based algorithms under different scenarios and data volumes. RF100 consists of a dataset collection of 100 real-world datasets, including 7 domains. Comprehensive Performance Comparison between CNN and Transformer (3) Algorithms such as DINO support AMP/Checkpoint/FrozenBN, which can effectively reduce memory usage.Ģ. (2) Based on CO-DETR, MMDet released a model with a COCO performance of 64.1 mAP. (1) Supported four updated and stronger SOTA Transformer models: DDQ, CO-DETR, AlignDETR, and H-DINO. Detection Transformer SOTA Model Collection The newly released RTMDet also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.Īpart from MMDetection, we also released MMEngine for model training and MMCV for computer vision research, which are heavily depended on by this toolbox. The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward. The training speed is faster than or comparable to other codebases, including Detectron2, maskrcnn-benchmark and SimpleDet. The toolbox directly supports multiple detection tasks such as object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection.Īll basic bbox and mask operations run on GPUs. ![]() We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. MMDetection is an open source object detection toolbox based on PyTorch.
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