Architecture | ResNet, FPN, Focal Loss |
---|---|
Max Iter | 90000 |
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Architecture | ResNet, FPN, Focal Loss |
---|---|
Max Iter | 540000 |
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TensorMask is a method for dense object segmentation which treats dense instance segmentation as a prediction task over 4D tensors, explicitly capturing this geometry and enabling novel operators on 4D tensors.
First install Detectron2 following the documentation and
setup the dataset. Then compile the TensorMask-specific op (swap_align2nat
):
pip install -e /path/to/detectron2/projects/TensorMask
To train a model, run:
python /path/to/detectron2/projects/TensorMask/train_net.py --config-file <config.yaml>
For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs, one should execute:
python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_1x.yaml --num-gpus 8
Model evaluation can be done similarly (6x schedule with scale augmentation):
python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_6x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
@InProceedings{chen2019tensormask,
title={Tensormask: A Foundation for Dense Object Segmentation},
author={Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
journal={The International Conference on Computer Vision (ICCV)},
year={2019}
}
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
COCO minival | TensorMask (R50, 6x) | box AP | 41.4 | # 61 |
COCO minival | TensorMask (R50, 1x) | box AP | 37.6 | # 98 |
BENCHMARK | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK |
---|---|---|---|---|
COCO minival | TensorMask (R50, 6x) | mask AP | 35.8 | # 48 |
COCO minival | TensorMask (R50, 1x) | mask AP | 32.4 | # 55 |