metadata
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: 0.875
- 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
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