https://github.com/notjoemartinez/apdl_quality_control
Computer Vision Quality control for the Advanced Particle Detector Laboratory at Texas Tech University
https://github.com/notjoemartinez/apdl_quality_control
Last synced: 3 months ago
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Computer Vision Quality control for the Advanced Particle Detector Laboratory at Texas Tech University
- Host: GitHub
- URL: https://github.com/notjoemartinez/apdl_quality_control
- Owner: NotJoeMartinez
- Created: 2021-01-25T22:20:10.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-01-24T01:48:59.000Z (over 2 years ago)
- Last Synced: 2026-01-26T11:30:50.527Z (6 months ago)
- Language: Python
- Homepage:
- Size: 697 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# APDL_Quality_Control
Deep learning applications for quality control in particle detector construction:
https://arxiv.org/abs/2203.08969
### Radom Samples
### Installation
It's important to specify this version of python as Tenserflow has not been working on the latest release
```bash
git clone https://github.com/NotJoeMartinez/APDL_Quality_Control
cd APDL_Quality_Control
virtualenv env --python=python3.9.2
source env/bin/activate
pip install -r requirements.txt
```
If you still get an error installing tenserflow deactivate and remove
```bash
pip install --upgrade tensorflow
```
### augment_imgs.py
- Augments the original data
- Handles splitting the dataset up into training and testing.
- Randomly applies a rotation function to data
### train_model.py
- Trains model on the augmented dataset
- Creates a plot of the training history
- Writes some metadata to a csv
### test_model.py
- Tests trained models against the testing dataset
- Plots data into confusion matrix along with ac couple other visualizations
- Creates a markdown file with impeded plots and other metadata
### transfer_learning.py
- Trains model using transfer learning model "mobilenet_v3_small_100_224"
- Not fully tested to current workflow (Mon Sep 27 07:37:26 CDT 2021)
# APDL_QC_API
Flask based API for interacting with the Tenserflow models
### Usage
```
curl -X POST -F 'image=@imgs/[PATH/TO/IMAGE].jpg' http://127.0.0.1:5000/get-labels
{
"All Predictions": {
"Broken Wire": "42%",
"Glue": " 1%",
"Good": "12%",
"No Wires": " 0%",
"One Third Wire": "37%",
"Two Third Wires": " 5%",
"Unknown Debris": " 3%"
},
"Image Size": [
640,
480
],
"Predicted Label": "Broken Wire",
"Prediction Confidence": "42%"
}
```
### Installation
```bash
pip install -r requirements.txt
export FLASK_APP=APDL_QC_API/__init__.py
export FLASK_ENV=development
flask run
```