aws-samples / end-2-end-3d-ml
This repository features Amazon SageMaker Ground Truth and explains how to ingest raw 3D point cloud data, label it, train a 3D object detection model using Amazon SageMaker, and deploy the model to an Amazon SageMaker Endpoint
Summary
  |
|||
| Main Code: 1,761 LOC (9 files) = PY (80%) + YAML (19%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 35 LOC (2); Other: 1,048 LOC (5); |
|||
| Duplication: 34% | |||
| File Size: 0% long (>1000 LOC), 9% short (<= 200 LOC) | |||
| Unit Size: 35% long (>100 LOC), 14% short (<= 10 LOC) | |||
| Conditional Complexity: 16% complex (McCabe index > 50), 26% simple (McCabe index <= 5) | |||
|
|
Logical Component Decomposition: primary (6 components) | ||
|
|
2 months old
|
||
|
|
0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
||
| Goals: Keep the system simple and easy to change (4) | |||
|
Features of interest:
TODOs
3 files |
|||
Latest commit date: 2022-01-20
|
1
commits
(30 days)
1
contributors
(30 days) |
|