Dependencies

Python 3.9

Setting up conda Environment

Clone the repository by running

git clone https://github.com/hasu234/SDPDSSample.git

Change current directory to SDPDSSample

cd SDPDSSample

Create a conda environmet

conda create -n myenv python=3.9

Activate the environment

conda activate myenv

Install the required library from requirment.txt by running

pip install -r requirmen.txt

Or, create a conda environment from environment.yml by running

conda env create -f environment.yml

Trining your data

To train your own dataset Make sure you have a data folder having the same folder hiararchy like below

├── dataset
|   ├── train
│   │   ├── class1
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class2
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class3
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class4
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
|   ├── test
│   │   ├── class1
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class2
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class3
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg
│   │   ├── class4
│   │   │   ├──image1.jpg
│   │   │   ├──image2.jpg

or make some chainges on train.py according to to your dataset directory.

Make sure you are in the project directory and run the train.py script with the folder directory of your dataset

python train.py /path/to/dataset_directory

Running inference

To run the inference on your test data make sure you downloaded the pretrained model from this link.
Then run the infer.py script from terminal specifying the test image location and downloaded pretrained model location

python infer.py path/to/image.jpg path/to/model.pth

Running on Docker

Clone the repository by running

git clone https://github.com/hasu234/SDPDSSample.git

Change current directory to SDPDSSample

cd SDPDSSample

Before Building the docker image transfer the files (model, test image, dataset) to current working directory if you don't want to deal with docker volume

Build the Docker image by running

docker build -t sdpdsample .

Run the docker image

docker run -d sdpdsample

if the container failed to run in background, run it on foreground using docker run -it sdpdsample then exit to get the running container id

Get the container id

docker ps

Getting inside the container

docker exec -it <container id> bash

You will get a Linux like command-line interface

Running the project

# for training your data
python train.py /path/to/dataset_directory

# for running inference
python infer.py path/to/image.jpg path/to/model.pth

Downloads last month: -; Downloads are not tracked for this model. How to track