Update README.md

README.md

@@ -1,12 +1,20 @@
 ---
 license: mit
+pipeline_tag: image-segmentation
+tags:
+  - instance-segmentation
+  - maskformer
+  - germination
 ---
 # 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)
+- **Classes**: 
+  - `0`: Background
+  - `1`: Normal
+  - `2`: Abnormal
 - **Training Data**: 18 images, 31+ annotations per image
 - **Epochs**: 5
 - **Final Loss**: 1.655
@@ -21,8 +29,8 @@ from transformers import MaskFormerForInstanceSegmentation, MaskFormerImageProce
 import torch
 from PIL import Image
 
-processor = MaskFormerImageProcessor.from_pretrained("your-username/maskformer-germination")
-model = MaskFormerForInstanceSegmentation.from_pretrained("your-username/maskformer-germination")
+processor = MaskFormerImageProcessor.from_pretrained("Dreamy0/GermiNet-instance-segmentation-maskformer")
+model = MaskFormerForInstanceSegmentation.from_pretrained("Dreamy0/GermiNet-instance-segmentation-maskformer")
 model.eval()
 
 image = Image.open("path/to/image.jpg")
@@ -32,4 +40,4 @@ with torch.no_grad():
     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}")
+            print(f"Label: {label} ({model.config.id2label[label]}), Score: {score:.3f}, Mask shape: {mask.shape}")