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A collection of log.json and model.pth for 2D human pose estimation experiments of MogaNet on COCO (download). You can also download all released files from Baidu Cloud (z8mf) at MogaNet/COCO_Pose.
We perform top-down pose estimation experiments based on with ImageNet-1K pre-trained MogaNet variants fine-tuning 210 epochs in MogaNet/pose_estimation. We also provide results of popular architectures (Swin, ConvNeXt, and Uniformer) for comparison.
A collection of log.json and model.pth for semantic segmentation experiments of MogaNet on ADE20K (download). You can also download all released files from Baidu Cloud (z8mf) at MogaNet/ADE20K_Segmentation.
We perform semantic segmentation experiments based on Semantic FPN with ImageNet-1K pre-trained MogaNet variants fine-tuning 80K iterations in MogaNet/segmentation.
We perform semantic segmentation experiments based on UperNet with ImageNet-1K pre-trained MogaNet variants fine-tuning 160K iterations in MogaNet/segmentation.
A collection of log.json and model.pth for object detection and instance segmentation experiments of MogaNet on COCO2017 (download). You can also download all files from Baidu Cloud (z8mf) at MogaNet/COCO_Detection.
We preform object detection experiments based on RetinaNet with ImageNet-1K pre-trained MogaNet variants for 1x training setting in MogaNet/detection.
We perform detection and instance segmentation experiments based on Mask R-CNN and Cascade Mask R-CNN with ImageNet-1K pre-trained MogaNet variants for 1x or MS 3x training settings in MogaNet/detection.
A collection of args.yaml, summary.csv, and model.pth.tar for image classification experiments of MogaNet on ImageNet-1K (download). You can download all files from Baidu Cloud: MogaNet (z8mf) at MogaNet/Classification_MogaNet.
We reproduce the results of MogaNet for 300-epoch training according to the DeiT setting on ImageNet-1K in TRAINING.md. Refer to OpenMixup for more image classification results.
The best top-1 accuracy of image classification of 3 trials is reported for all experiments. Note that we report the classification accuracy of EMA weights for MogaNet-S, MogaNet-B, and MogaNet-L (please evaluate their EMA models).
To evaluate the pre-trained weights, use validate.py with scripts for the classification performance.