Below you can find links to models on HuggingFace and performance metrics on our holdout dataset. If you want to use one in a pipeline, just run:
from tcd_pipeline import Pipeline
pipeline = Pipeline(<model name>)
pipeline.predict(<your image>)and the model type should be automagically determined. Weights will be downloaded/cached if you don't have them on your system already.
If you train a model with our dataset and would like to integrate it into the pipeline, please let us know and we'd be happy to add it to the zoo.
We currently provide a trained Mask-RCNN model with a ResNet50 backbone - in the future we will provide alternative backbone sizes and hopefully some newer architectures.
| Model Architecture | Model Tag | mAP50 |
|---|---|---|
| Mask-RCNN Resnet34 | restor/tcd-mask-rcnn-r34 |
TBD |
| Mask-RCNN Resnet50 | restor/tcd-mask-rcnn-r50 |
43.22 |
| Mask-RCNN Resnet101 | restor/tcd-mask-rcnn-r101 |
TBD |
| Model Architecture | Model Tag | Accuracy | F1 | IoU |
|---|---|---|---|---|
| U-Net ResNet34 | restor/tcd-unet-r34 |
0.883 | 0.871 | 0.838 |
| U-Net ResNet50 | restor/tcd-unet-r50 |
0.881 | 0.880 | 0.849 |
| U-Net ResNet101 | restor/tcd-unet-r101 |
0.900 | 0.886 | 0.856 |
| Segformer mit-b0 | restor/tcd-segformer-mit-b0 |
0.892 | 0.882 | 0.865 |
| Segformer mit-b1 | restor/tcd-segformer-mit-b1 |
0.897 | 0.891 | 0.870 |
| Segformer mit-b2 | restor/tcd-segformer-mit-b2 |
0.889 | 0.898 | 0.871 |
| Segformer mit-b3 | restor/tcd-segformer-mit-b3 |
0.884 | 0.901 | 0.875 |
| Segformer mit-b4 | restor/tcd-segformer-mit-b4 |
0.891 | 0.901 | 0.875 |
| Segformer mit-b5 | restor/tcd-segformer-mit-b5 |
0.890 | 0.902 | 0.876 |