Polish up README.

.gitignore

@@ -1,142 +0,0 @@
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README.md

@@ -17,8 +17,8 @@ license: mit
 ### Performance:
 |    Dataset    |                Method               | maxFm | wFmeasure | MAE  | Smeasure | meanEm | HCE  | maxEm | meanFm | adpEm | adpFm | mBA  | maxBIoU | meanBIoU |
 |     :------:    | :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |
-| DIS-VD | BiRefNet_HR-general-epoch_130 |  .925 |    .894   | .026 |   .927   |  .952  | 811  |  .960 |  .909  |  .944 |  .888 | .828 |   .837  |   .817   |
-| DIS-VD | BiRefNet-general-epoch_244 |  .907 |    .875   | .033 |   .911   |  .943  | 1069 |  .953 |  .892  |  .944 |  .879 | .000 |   .000  |   .000   |
+| DIS-VD | **BiRefNet_HR**-general-epoch_130 |  .925 |    .894   | .026 |   .927   |  .952  | 811  |  .960 |  .909  |  .944 |  .888 | .828 |   .837  |   .817   |
+| DIS-VD | [**BiRefNet**-general-epoch_244](https://huggingface.co/ZhengPeng7/BiRefNet) |  .907 |    .875   | .033 |   .911   |  .943  | 1069 |  .953 |  .892  |  .944 |  .879 | .000 |   .000  |   .000   |
 
 <h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>
 

birefnet.py

@@ -52,7 +52,7 @@ class Config():
             }[self.task]
         ][1]    # choose 0 to skip
         self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4)     # DIS needs high lr to converge faster. Adapt the lr linearly
-        self.size = 1024 * 2
+        self.size = 1024
         self.num_workers = max(4, self.batch_size)          # will be decrease to min(it, batch_size) at the initialization of the data_loader
 
         # Backbone settings

handler.py

@@ -88,6 +88,7 @@ elif usage in ['General-HR']:
 else:
     resolution = (1024, 1024) 
 
+half_precision = True
 
 class EndpointHandler():
     def __init__(self, path=''):
@@ -96,6 +97,8 @@ class EndpointHandler():
         )
         self.birefnet.to(device)
         self.birefnet.eval()
+        if half_precision:
+            self.birefnet.half()
 
     def __call__(self, data: Dict[str, Any]):
         """
@@ -125,7 +128,7 @@ class EndpointHandler():
 
         # Prediction
         with torch.no_grad():
-            preds = self.birefnet(image_proc.to(device))[-1].sigmoid().cpu()
+            preds = self.birefnet(image_proc.to(device).half() if half_precision else image_proc.to(device))[-1].sigmoid().cpu()
         pred = preds[0].squeeze()
 
         # Show Results