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HRSAM: Efficiently Segment Anything in High-resolution Images
This repository contains the implementation for
HRSAM: Efficiently Segment Anything in High-resolution Images
Comparison of HRSAM and previous SOTA SegNext in high-precision interactive segmentation.
Installation
To install the required dependencies, follow the detailed instructions in the Installation.
Datasets
To prepare the datasets, follow the detailed instructions in the Datasets.
Pre-trained Models
We provide pre-trained models so that you can start testing immediately. You can download the pre-trained models and the corresponding configs from the following link:
The zip file contains the following directories:
- 'pretrain': the MAE-pretrained models
- 'work_dirs': contains the pretrained models and the corresponding configs
Training the Model
To train the model, execute the following command:
e.g.
bash tools/dist_train.sh configs/hrsam/hqseg44k/hrsam_plusplus_simaug_1024x1024_bs1_40k.py 4
Evaluating the Model
After training the model, you can evaluate it using the following command:
bash tools/dist_test_no_viz.sh configs/datasets_ext/eval_hqseq44k_val.py work_dirs/hrsam/hqseg44k/hrsam_plusplus_simaug_1024x1024_bs1_40k/iter_40000.pth 4
(the input resolution is 1024x1024) or
bash tools/dist_test_no_viz.sh configs/datasets_ext/eval_hqseq44k_val.py work_dirs/hrsam/hqseg44k/hrsam_plusplus_simaug_1024x1024_bs1_40k/iter_40000.pth 4 -c configs/eval_custom/simseg_ts2048.py
which will use 2048x2048 inputs in the evaluation.