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.gitattributes

@@ -35,3 +35,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 example.png filter=lfs diff=lfs merge=lfs -text
 no-background.png filter=lfs diff=lfs merge=lfs -text
+examples/example1.png filter=lfs diff=lfs merge=lfs -text
+examples/no-background1.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -2,7 +2,10 @@
 license: apache-2.0
 tags:
   - segmentation
-  - background remover
+  - remove background
+  - background
+  - background-removal
+  - Pytorch
 pretty_name: Open Remove Background Model
 datasets:
   - schirrmacher/humans
@@ -10,6 +13,8 @@ datasets:
 
 # Open Remove Background Model (ormbg)
 
+[>>> DEMO <<<](https://huggingface.co/spaces/schirrmacher/ormbg)
+
 This model is a **fully open-source background remover** optimized for images with humans. It is based on [Highly Accurate Dichotomous Image Segmentation research](https://github.com/xuebinqin/DIS).
 
 This model is similar to [RMBG-1.4](https://huggingface.co/briaai/RMBG-1.4), but with open training data/process and commercially free to use.

utils/inference.py

@@ -1,3 +1,4 @@
+import os
 import torch
 import argparse
 import numpy as np
@@ -14,19 +15,19 @@ def parse_args():
     parser.add_argument(
         "--input",
         type=str,
-        default="example.png",
+        default=os.path.join("examples", "example1.png"),
         help="Path to the input image file.",
     )
     parser.add_argument(
         "--output",
         type=str,
-        default="no-background.png",
+        default=os.path.join("examples", "no-background1.png"),
         help="Path to the output image file.",
     )
     parser.add_argument(
         "--model-path",
         type=str,
-        default="models/ormbg.pth",
+        default=os.path.join("models", "ormbg.pth"),
         help="Path to the model file.",
     )
     return parser.parse_args()