Skip to main content

YOLO-based model for atom and bond detection in molecular images.

Project description

ABD-model: Atom and Bond Detection Model

This repository contains a deep learning-based model designed for detecting atoms and bonds in molecular images. It can be used for various applications in molecular chemistry and image analysis.

Overview

The ABD-model is a YOLO-based deep learning model designed to detect atoms (e.g., C, O, H, N) and bond types (single, double, triple) in molecular structure images. The model can extract useful data from 2D molecular representations, aiding in further computational chemistry analysis.

Features

  • Atom Detection: Detects atoms like Carbon (C), Oxygen (O), Nitrogen (N), and Hydrogen (H) in molecular images.
  • Bond Detection: Identifies single, double, and triple bonds between atoms.
  • Versatility: Works with a wide variety of molecular structure images.
  • YOLO-based: Uses a YOLO (You Only Look Once) model for fast and accurate detection of atoms and bonds.
  • Open-source: Easy to integrate into your own projects and customize.

Requirements

To run this model, you need to install the following dependencies:

  • Python 3.x
  • PyTorch
  • OpenCV (for image processing)
  • NumPy
  • Any other libraries listed in requirements.txt

To install the dependencies, run:

pip install -r requirements.txt

Installation

  1. Clone this repository:

    git clone https://github.com/Safi-ullah-majid/ABD-model.git
    cd ABD-model
    
  2. Install dependencies:

    pip install -r requirements.txt
    
  3. Download the model file ABD.pt and place it in the correct directory.

Usage

To make predictions using the model, run the predict.py script:

python predict.py --input_path path/to/image.png

Ensure that:

  • The input image is in .png format.
  • The model (ABD.pt) is loaded correctly.
  • The input image is a valid .png file representing a molecular structure.

License

This repository is licensed under the MIT License - see the LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

abd_model-0.3.0.tar.gz (6.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ABD_model-0.3.0-py3-none-any.whl (6.3 MB view details)

Uploaded Python 3

File details

Details for the file abd_model-0.3.0.tar.gz.

File metadata

  • Download URL: abd_model-0.3.0.tar.gz
  • Upload date:
  • Size: 6.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.21

File hashes

Hashes for abd_model-0.3.0.tar.gz
Algorithm Hash digest
SHA256 540e600c1e1e85fafb03fbeea0c9a528600bb00b5b3fbeda5ecb07dd60d9105d
MD5 f02d08f457a7c99e734a2fb5f3573846
BLAKE2b-256 4abc3354bb9a16449325628a4118d385f03290ad8916e30796003192fcd8490d

See more details on using hashes here.

File details

Details for the file ABD_model-0.3.0-py3-none-any.whl.

File metadata

  • Download URL: ABD_model-0.3.0-py3-none-any.whl
  • Upload date:
  • Size: 6.3 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.21

File hashes

Hashes for ABD_model-0.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 407615a569c8b5682d844da3349aafe0e159ccfb3d22cc1e7c0469f6ab68b894
MD5 db20d3a4f9c63b65f168b29ae7e94ace
BLAKE2b-256 d77256f4c7b41d40748b3b3f200dfe081a24dcb0a99732a74fd0c6bd2fe039a6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page