aws-samples / chip-wafer-classification-deep-learning
This project uses AWS machine learning and IoT tools to develop a deep learning defect classification model and use it for real-time defect detection on a device.
Summary
     |
|||
| Main Code: 3,823 LOC (32 files) = YAML (68%) + JS (15%) + PY (11%) + CSS (2%) + YML (1%) + HTML (<1%) + WEBMANIFEST (<1%) Secondary code: Test: 41 LOC (3); Generated: 19,148 LOC (1); Build & Deploy: 113 LOC (3); Other: 23,976 LOC (14); |
|||
| Duplication: 24% | |||
| File Size: 0% long (>1000 LOC), 28% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 53% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 100% simple (McCabe index <= 5) | |||
|
|
Logical Component Decomposition: primary (11 components) | ||
|
|
2 years, 8 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) | |||
Latest commit date: 2021-09-13
|
0
commits
(30 days)
0
contributors
(30 days) |
|