AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻譯準備中
正在載入完整 README。 以下為已儲存的摘錄。
Groundlight makes it simple to build reliable visual applications. Read the full documentation here. Build a working computer vision system in just a few lines of python:檢視完整 README ↗
Groundlight makes it simple to build reliable visual applications. Read the full documentation here.
pip install groundlight
Build a working computer vision system in just a few lines of python:
from groundlight import Groundlight
gl = Groundlight()
det = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")
img = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object
image_query = gl.submit_image_query(detector=det, image=img)
print(f"The answer is {image_query.result}")
Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough confidence, that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence.
Some more resources you might like: