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@ -113,13 +113,18 @@ Samples from DIS dataset V2.0.
git clone https://github.com/xuebinqin/DIS.git git clone https://github.com/xuebinqin/DIS.git
``` ```
### (2) Configuring the environment: go to the root ```DIS``` folder and run ### (2) Configuring the environment: go to the ```DIS/ISNet``` folder and run
``` ```
conda env create -f pytorch18.yml conda env create -f pytorch18.yml
``` ```
Or you can check the ```requirements.txt``` to configure the dependancies. Or you can check the ```requirements.txt``` to configure the dependancies.
### (3) Train: ### (3) activate the conda environment by
```
conda activate pytorch18
```
### (4) Train:
(a) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```train_datasets``` and ```valid_datasets```, e.g., ```valid_datasets=[dataset_vd]``` <br> (a) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```train_datasets``` and ```valid_datasets```, e.g., ```valid_datasets=[dataset_vd]``` <br>
(b) Set the ```hypar["mode"]``` to ```"train"``` <br> (b) Set the ```hypar["mode"]``` to ```"train"``` <br>
(c) Create a new folder ```your_model_weights``` in the directory ```saved_models``` and set it as the ```hypar["model_path"] ="../saved_models/your_model_weights"``` and make sure ```hypar["valid_out_dir"]```(line 668) is set to ```""```, otherwise the prediction maps of the validation stage will be saved to that directory, which will slow the training speed down <br> (c) Create a new folder ```your_model_weights``` in the directory ```saved_models``` and set it as the ```hypar["model_path"] ="../saved_models/your_model_weights"``` and make sure ```hypar["valid_out_dir"]```(line 668) is set to ```""```, otherwise the prediction maps of the validation stage will be saved to that directory, which will slow the training speed down <br>
@ -128,12 +133,23 @@ Or you can check the ```requirements.txt``` to configure the dependancies.
python train_valid_inference_main.py python train_valid_inference_main.py
``` ```
### (4) Inference ### (5) Inference
(a). Download the pre-trained weights (for fair academic comparisons only, the optimized model for engineering or common use will be released soon) ```isnet.pth``` from [(Google Drive)](https://drive.google.com/file/d/1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn/view?usp=sharing) or [(Baidu Pan 提取码：xbfk)](https://pan.baidu.com/s/1-X2WutiBkWPt-oakuvZ10w?pwd=xbfk) and store ```isnet.pth``` in ```saved_models/IS-Net``` <br>
(b) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```valid_datasets```, e.g., ```valid_datasets=[dataset_te1, dataset_te2, dataset_te3, dataset_te4]``` <br> Download the pre-trained weights (for fair academic comparisons only, the optimized model for engineering or common use will be released soon) ```isnet.pth``` from [(Google Drive)](https://drive.google.com/file/d/1KyMpRjewZdyYfxHPYcd-ZbanIXtin0Sn/view?usp=sharing) or [(Baidu Pan 提取码：xbfk)](https://pan.baidu.com/s/1-X2WutiBkWPt-oakuvZ10w?pwd=xbfk) and store ```isnet.pth``` in ```saved_models/IS-Net``` <br>
(c) Set the ```hypar["mode"]``` to ```"valid"``` <br>
(d) Set the output directory of your predicted maps, e.g., ```hypar["valid_out_dir"] = "../DIS5K-Results-test"``` <br> ## I. Simple inference code for your own dataset without ground truth:
(e) Run (a) Open ```\ISNet\inference.py``` and configure your input and output directories
(b) Run
```
python inference.py
```
## II. Inference for dataset with/without ground truth
(a) Open ```train_valid_inference_main.py```, set the path of your to-be-inferenced ```valid_datasets```, e.g., ```valid_datasets=[dataset_te1, dataset_te2, dataset_te3, dataset_te4]``` <br>
(b) Set the ```hypar["mode"]``` to ```"valid"``` <br>
(c) Set the output directory of your predicted maps, e.g., ```hypar["valid_out_dir"] = "../DIS5K-Results-test"``` <br>
(d) Run
``` ```
python train_valid_inference_main.py python train_valid_inference_main.py
``` ```