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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.
MogaNet + Top-Down
| Backbone | Pretrain | Input Size | Params | FLOPs | Epoch | AP | AR | Config | Download |
|---|---|---|---|---|---|---|---|---|---|
| MogaNet-XT | ImageNet-1K | 256x192 | 5.6M | 1.84G | 210 | 72.1 | 77.7 | config | log / model |
| MogaNet-XT | ImageNet-1K | 384x288 | 5.6M | 4.15G | 210 | 74.7 | 79.9 | config | log / model |
| MogaNet-T | ImageNet-1K | 256x192 | 8.1M | 2.15G | 210 | 73.2 | 78.8 | config | log / model |
| MogaNet-T | ImageNet-1K | 384x288 | 8.1M | 4.85G | 210 | 75.7 | 80.9 | config | log / model |
| MogaNet-S | ImageNet-1K | 256x192 | 29.0M | 5.99G | 210 | 74.8 | 80.1 | config | log / model |
| MogaNet-S | ImageNet-1K | 384x288 | 29.0M | 13.48G | 210 | 76.4 | 81.4 | config | log / model |
| MogaNet-B | ImageNet-1K | 256x192 | 47.4M | 10.85G | 210 | 75.3 | 80.7 | config | log / model |
| MogaNet-B | ImageNet-1K | 384x288 | 47.4M | 24.42G | 210 | 77.3 | 82.2 | config | log / model |
MetaFormers + Top-Down
| Backbone | Input Size | Params | FLOPs | AP | AP50 | AP75 | AR | ARM | ARL | Config | Download |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Swin-T | 256x192 | 32.8M | 6.1G | 72.4 | 90.1 | 80.6 | 78.2 | 74.0 | 84.3 | config | model | log |
| Swin-B | 256x192 | 93.0M | 18.6G | 73.7 | 90.4 | 82.0 | 79.8 | 74.9 | 85.7 | config | model | log |
| Swin-B | 384x288 | 93.0M | 40.1G | 75.9 | 91.0 | 83.2 | 78.8 | 76.5 | 87.5 | config | model | log |
| Swin-L | 256x192 | 203.4M | 40.3G | 74.3 | 90.6 | 82.1 | 79.8 | 75.5 | 86.2 | config | model | log |
| Swin-L | 384x288 | 203.4M | 86.9G | 76.3 | 91.2 | 83.0 | 81.4 | 77.0 | 87.9 | config | model | log |
| ConvNeXt-T | 256x192 | 33.0M | 5.5G | 73.2 | 90.0 | 80.9 | 78.8 | 74.5 | 85.1 | config | log | model |
| ConvNeXt-T | 384x288 | 33.0M | 12.5G | 75.3 | 90.4 | 82.1 | 80.5 | 76.1 | 86.8 | config | log | model |
| ConvNeXt-S | 256x192 | 54.7M | 9.7G | 73.7 | 90.3 | 81.9 | 79.3 | 75.0 | 85.5 | config | log | model |
| ConvNeXt-S | 384x288 | 54.7M | 21.8G | 75.8 | 90.7 | 83.1 | 81.0 | 76.8 | 87.1 | config | log | model |
| UniFormer-S | 256x192 | 25.2M | 4.7G | 74.0 | 90.3 | 82.2 | 79.5 | 66.8 | 76.7 | config | log | model |
| UniFormer-S | 384x288 | 25.2M | 11.1G | 75.9 | 90.6 | 83.4 | 81.4 | 68.6 | 79.0 | config | log | model |
| UniFormer-B | 256x192 | 53.5M | 9.2G | 75.0 | 90.6 | 83.0 | 80.4 | 67.8 | 77.7 | config | log | model |
| UniFormer-B | 384x288 | 53.5M | 14.8G | 76.7 | 90.8 | 84.0 | 81.4 | 69.3 | 79.7 | config | log | model |