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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
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