Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+# MaskFormer-Germination
+Fine-tuned MaskFormer for germination instance segmentation.
+
+## Details
+- **Base Model**: `facebook/maskformer-swin-tiny-coco`
+- **Classes**: Normal (1), Abnormal (2) (mapped to COCO 134-class IDs)
+- **Training Data**: 18 images, 31+ annotations per image
+- **Epochs**: 5
+- **Final Loss**: 1.655
+- **Batch Size**: 2
+- **Learning Rate**: 5e-5
+- **Steps**: 45
+- **Runtime**: ~26 minutes
+
+## Usage
+```python
+from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProcessor
+import torch
+from PIL import Image
+
+processor = MaskFormerImageProcessor.from_pretrained("your-username/maskformer-germination")
+model = MaskFormerForInstanceSegmentation.from_pretrained("your-username/maskformer-germination")
+model.eval()
+
+image = Image.open("path/to/image.jpg")
+inputs = processor(images=image, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    results = processor.post_process_instance_segmentation(outputs, target_sizes=[(image.height, image.width)])[0]
+    for score, label, mask in zip(results["scores"], results["labels"], results["masks"]):
+        if score > 0.5 and label in [1, 2]:
+            print(f"Label: {label}, Score: {score:.3f}, Mask shape: {mask.shape}")