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@ -111,8 +111,6 @@ Or you can check the ```requirements.txt``` to configure the dependancies.
python train_valid_inference_main.py python train_valid_inference_main.py
``` ```
<br>
### (4) Inference ### (4) 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> (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> (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>
@ -121,7 +119,7 @@ python train_valid_inference_main.py
(e) Run (e) Run
``` ```
python train_valid_inference_main.py python train_valid_inference_main.py
```<br> ```
### (5) Use of our Human Correction Efforts(HCE) metric ### (5) Use of our Human Correction Efforts(HCE) metric
Set the ground truth directory ```gt_root``` and the prediction directory ```pred_root```. To reduce the time costs for computing HCE, the skeletion of the DIS5K dataset can be pre-computed and stored in ```gt_ske_root```. If ```gt_ske_root=""```, the HCE code will compute the skeleton online which usually takes a lot for time for large size ground truth. Then, run ```python hce_metric_main.py```. Other metrics are evaluated based on the [SOCToolbox](https://github.com/mczhuge/SOCToolbox). Set the ground truth directory ```gt_root``` and the prediction directory ```pred_root```. To reduce the time costs for computing HCE, the skeletion of the DIS5K dataset can be pre-computed and stored in ```gt_ske_root```. If ```gt_ske_root=""```, the HCE code will compute the skeleton online which usually takes a lot for time for large size ground truth. Then, run ```python hce_metric_main.py```. Other metrics are evaluated based on the [SOCToolbox](https://github.com/mczhuge/SOCToolbox).