metadata

license: mit
title: monitoringInterface
sdk: gradio
emoji: 🚀
colorFrom: purple
colorTo: purple

monitoring-interface

Requirements

pip install -r requiremnets.txt

To install Detectron2, please follow here.

Dataset Preparation

We use Fiftyone library to load and visualize datasets.

BDD100k, COCO, KITTI and OpenImage can be loaded directly through Fiftyone Datasets Zoo.

For other datasets, such as NuScene can be loaded manually via the following simple pattern:

import fiftyone as fo

# A name for the dataset
name = "my-dataset"

# The directory containing the dataset to import
dataset_dir = "/path/to/dataset"

# The type of the dataset being imported
dataset_type = fo.types.COCODetectionDataset  # for example

dataset = fo.Dataset.from_dir(
    dataset_dir=dataset_dir,
    dataset_type=dataset_type,
    name=name,
)

The custom dataset folder should have the following structure:

 └── /path/to/dataset
     |
     ├── Data
     └── labels.json

Notice that the annotation file labels.json should be prepared in COCO format.

Interface demo

Three interfaces are provided:

interface.py: all-in-1 interface
interface_tabbed.py: tabbed interface
enlarge.py: interface for monitor interval enlargement

To run any of these interfaces, just execute python <script name.py>.

Please note that feature extraction for both training data and evaluation data can be a time-consuming process. However, if you are only interested in testing monitor construction, monitor evaluation, or monitoring demo, you can use the following settings to load a pretrained model along with the corresponding extracted features and monitors.

ID	Backbone	Clustering method for Monitors
KITTI	ResNet	KMeans(nb_clusters=[1, 4, 5, 6])