Add README.md

README.md

@@ -0,0 +1,63 @@
+---
+license: mit
+tags:
+  - yolo11
+  - ultralytics
+  - image-segmentation
+  - deep-learning
+  - satellite
+  - rso-detection
+datasets:
+  - custom
+library_name: ultralytics
+base_model: yolo11
+pipeline_tag: image-segmentation
+inference: true
+widget:
+  - src: "example_image.jpg"
+    example_title: "RSO Detection"
+model-index:
+  - name: best
+    results:
+      - task:
+          type: image-segmentation
+          name: Instance Segmentation
+        dataset:
+          name: RSO Detection Dataset
+          type: custom
+        metrics:
+          - name: Mean Average Precision (mAP@50)
+            type: mean_average_precision
+            value: 0.8750
+          - name: Mean Average Precision (mAP@50-95)
+            type: mean_average_precision
+            value: 0.6194
+fine-tuned-from: Ultralytics/YOLO11
+labels:
+  - streak
+metadata:
+  label2id:
+    streak: 0
+  id2label:
+    0: streak
+---
+
+# best
+
+## Model Information
+This is a YOLO11-based segmentation model for detecting Resident Space Objects (RSOs) in satellite imagery.
+
+## Classes
+- **streak**: Class 0
+
+## Usage
+```python
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(model="best")
+result = client.image_segmentation(image)
+```
+
+## Training Metrics
+- mAP@50: 0.8750
+- mAP@50-95: 0.6194