Repository for the Bosch's Traffic Sign Recognition challenge
The entire pipleine has been integrated in our UI, however if there is a need all the deep learning modules can be called individually as follows:
Usage:
- Augmentations
We can get the augmentations config file (aug.json) from the UI, or you can manually provide it
from augmentations import *
import json
import os
path_to_aug_json = "aug.json"
with open(path_to_aug_json) as f:
data = json.load(f)
train_data_root = "Train_dummy/"
new_train_data_root = "Train_dummy_augmented/"
os.makedirs(new_train_data_root,exist_ok=True)
generate_augented_dataset(data, train_data_root, new_train_data_root)
- Training our model
For training the model and logging the results for our UI and the MLFlow server
new_train_data_root = "Train_dummy_augmented/"
test_data_root = "Test_dataset_48_classes"
run_id = 'a56e04bce6fc41949f03f187221be156'
model_path,run_id = train_classifier(new_train_data_root, test_data_root, run_id)
- Model evaluation
Computing evaluation metrics and logging them
from model_eval import *
test_data_root = "Test_dummy/"
model_path = "latest_model"
model_eval_fns(test_data_root, model_path, classes, run_id)
- Model visulalization
Model visulalization techniques such as occlusion sensitivity maps, activation maps,etc
from model_viz import *
test_data_root = "Train_dummy/"
model_path = "final_model_test.h5"
run_id = 'a56e04bce6fc41949f03f187221be156'
viz_classes = ['2','5']
visualize_main(test_data_root, model_path, run_id, viz_classes)
Our classes (including 5 addditional classes) are
0 : "Speed limit 20km/h",
1 : "Speed limit 30km/h",
2 : "Speed limit 50km/h",
3 : "Speed limit 60km/h",
4 : "Speed limit 70km/h",
5 : "Speed limit 80km/h",
6 : "End of speed limit 80km/h",
7 : "Speed limit 100km/h",
8 : "Speed limit 120km/h",
9 : "No passing",
10 : "No passing veh over 3.5 tons",
11 : "Right-of-way at intersection",
12 : "Priority road",
13 : "Yield",
14 : "Stop",
15 : "No vehicles",
16 : "Veh over 3.5 tons prohibited",
17 : "No entry",
18 : "General caution",
19 : "Dangerous curve left",
20 : "Dangerous curve right",
21 : "Double curve",
22 : "Bumpy road",
23 : "Slippery road",
24 : "Road narrows on the right",
25 : "Road work",
26 : "Traffic signals",
27 : "Pedestrians",
28 : "Children crossing",
29 : "Bicycles crossing",
30 : "Beware of ice/snow",
31 : "Wild animals crossing",
32 : "End speed passing limits",
33 : "Turn right ahead",
34 : "Turn left ahead",
35 : "Ahead only",
36 : "Go straight or right",
37 : "Go straight or left",
38 : "Keep right",
39 : "Keep left",
40 : "Roundabout mandatory",
41 : "End of no passing",
42 : "End no passing veh over 3.5 tons",
43 : "Parking",
44 : "No parking",
45 : "speed bump hump",
46 : "No Right",
47 : "Priority to",
Apart from our own custom UI, we are also leveraging MLflow which is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
To access the MLFlow UI, you can run in the root folder
mlflow ui
To start an mlflow-server manually with a GCP backend and SQLite database
mlflow server \
--backend-store-uri sqlite:///mlflow.db \
--default-artifact-root gs://mlflow_artifacts_storage/artifacts/ \
--host 0.0.0.0
For local run folder with SQLite database
mlflow server --backend-store-uri sqlite:///mydb.sqlite \
--default-artifact-root ./mlruns \
--port 8000
Use mlflow.set_tracking_uri("https://35.198.166.98:5000") in code to log to a remote mlflow server