diff --git "a/README.md" "b/README.md"
new file mode 100644--- /dev/null
+++ "b/README.md"
@@ -0,0 +1,79 @@
+---
+license: openrail
+language:
+- en
+pipeline_tag: image-segmentation
+---
+
+
+
+
+
+
+### Description
+
+Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image.
+It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation.
+The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
+
+## Parameters
+
+```
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/mit-b4",
+ num_labels=2,
+ id2label=id2label,
+ label2id=label2id, )
+
+
+```
+
+## Usage
+
+
+
+```python
+
+from torch import nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Transforms
+_transform = A.Compose([
+ A.Resize(height = 512, width=512),
+ ToTensorV2(),
+])
+
+
+trans_image = _transform(image=np.array(image))
+outputs = model(trans_image['image'].float().unsqueeze(0))
+logits = outputs.logits.cpu()
+print(logits.shape)
+
+
+# First, rescale logits to original image size
+upsampled_logits = nn.functional.interpolate(logits,
+ size=image.size[::-1], # (height, width)
+ mode='bilinear',
+ align_corners=False)
+
+
+seg = upsampled_logits.argmax(dim=1)[0]
+color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
+palette = np.array([[0, 0, 0],[255, 255, 255]])
+for label, color in enumerate(palette):
+ color_seg[seg == label, :] = color
+# Convert to BGR
+color_seg = color_seg[..., ::-1]
+
+```
+
+
+
+
+
+#Metric
+Todo
+
+#Note
+
+This model was not built by using Huggingface based feature extractor, so automatic api could not work.
\ No newline at end of file