Update README.md

README.md

@@ -2,65 +2,54 @@
 license: apache-2.0
 ---
 
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 # BEN - Background Erase Network
 
 BEN is a deep learning model designed to automatically remove backgrounds from images, producing both a mask and a foreground image.
 
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 # BEN SOA Benchmarks on Disk 5k Eval 
 
-BEN_Base + BEN_Refiner (commerical model please contanct us for more information):  \n
-MAE-0.0283 \n
-DICE-0.8976 \n
-IOU-0.8430 \n
-BER-0.0542 \n
-ACC-0.9725 \n
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-
-BEN_Base: \n
-MAE-0.0331 \n
-DICE-0.8743  \n
-IOU-0.8301 \n
-BER-0.0560 \n
-ACC-0.9700 \n
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-MVANet (old SOA): \n
-MAE-0.0353 \n
-DICE-0.8676 \n
-IOU-0.8104 \n
-BER-0.0639 \n
-ACC-0.9660 \n
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-
-
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+### BEN_Base + BEN_Refiner (commercial model please contact us for more information):
+- MAE: 0.0283
+- DICE: 0.8976
+- IOU: 0.8430
+- BER: 0.0542
+- ACC: 0.9725
+
+### BEN_Base:
+- MAE: 0.0331
+- DICE: 0.8743
+- IOU: 0.8301
+- BER: 0.0560
+- ACC: 0.9700
+
+### MVANet (old SOA):
+- MAE: 0.0353
+- DICE: 0.8676
+- IOU: 0.8104
+- BER: 0.0639
+- ACC: 0.9660
 
 ## Features
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 - Background removal from images
 - Generates both binary mask and foreground image
 - CUDA support for GPU acceleration
 - Simple API for easy integration
 
 ## Installation
-- Clone Repo
-- Install requirements.txt
+1. Clone Repo
+2. Install requirements.txt
 
 ## Quick Start Code
+```python
 from BEN import BEN_Base
 from PIL import Image
 import torch
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-
 model = BEN_Base().to(device).eval()
 model.loadcheckpoints("./BEN/BEN_Base.pth")
 
 image = Image.open("./image2.jpg")
 mask, foreground = model.inference(image)
-
 mask.save("./mask.png")
-foreground.save("./foreground.png")
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+foreground.save("./foreground.png")