hasu234/image_classifier

This is the model for https://github.com/hasu234/SDPDSSample. this repository.

This classifier can classify between ``dog`, `berry`, `flower` and `bird`` images. [image]

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