README.md

---
license: mit
tags:
  - yolo11
  - ultralytics
  - image-segmentation
  - deep-learning
datasets:
  - custom
library_name: ultralytics
base_model: yolo11
inference:
  library: ultralytics
  parameters:
    threshold: 0.5
    iou_threshold: 0.5
    format: pytorch
model-index:
  - name: rayh/astro-seg
    results:
      - task:
          type: image-segmentation
          name: Instance Segmentation
        dataset:
          name: Custom Dataset
          type: custom
        metrics:
          - name: Mean Average Precision (mAP@50)
            type: mean_average_precision
            value: 0.5899
          - name: Mean Average Precision (mAP@50-95)
            type: mean_average_precision
            value: 0.2497          
fine-tuned-from: Ultralytics/YOLO11
labels:
  - normal
  - glint
metadata:
  label2id:
    normal: 0
    glint: 1
  id2label:
    0: normal
    1: glint
---

# rayh/astro-seg

## Model Information
This is a YOLO-based segmentation model exported to ONNX and uploaded for use with Hugging Face Inference API.

## Example Visualizations
![MaskR_curve.png](./MaskR_curve.png)
![BoxP_curve.png](./BoxP_curve.png)
![results.png](./results.png)
![MaskF1_curve.png](./MaskF1_curve.png)
![MaskP_curve.png](./MaskP_curve.png)
![BoxR_curve.png](./BoxR_curve.png)
![MaskPR_curve.png](./MaskPR_curve.png)
![confusion_matrix_normalized.png](./confusion_matrix_normalized.png)
![confusion_matrix.png](./confusion_matrix.png)
![BoxPR_curve.png](./BoxPR_curve.png)
![BoxF1_curve.png](./BoxF1_curve.png)

## Validation Files