Create README.md

README.md

@@ -0,0 +1,46 @@
+---
+license: wtfpl
+tags:
+- vision
+- image-segmentation
+widget:
+- src: https://images.unsplash.com/photo-1643310325061-2beef64926a5?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8cmFjb29uc3xlbnwwfHwwfHw%3D&w=1000&q=80
+  example_title: Person
+- src: https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg
+  example_title: Person
+datasets:
+- mattmdjaga/human_parsing_dataset
+---
+# Segformer B0 fine-tuned for clothes segmentation
+
+SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation.
+The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
+
+```python
+from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
+from PIL import Image
+import requests
+import matplotlib.pyplot as plt
+import torch.nn as nn
+
+extractor = AutoFeatureExtractor.from_pretrained("mattmdjaga/segformer_b0_clothes")
+model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b0_clothes")
+
+url = "https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80"
+
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = extractor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+logits = outputs.logits.cpu()
+
+upsampled_logits = nn.functional.interpolate(
+    logits,
+    size=image.size[::-1],
+    mode="bilinear",
+    align_corners=False,
+)
+
+pred_seg = upsampled_logits.argmax(dim=1)[0]
+plt.imshow(pred_seg)
+```