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animal-detection-using-yolov5

https://github.com/harshavardhan856/animal-detection-using-yolov5

Science Score: 44.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: Harshavardhan856
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 12.6 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

Animal Detection Model

This repository contains an animal detection model capable of detecting 80 different types of animals. The model is built using YOLOv5.

Prerequisites

Before running the detection model, ensure you have the following prerequisites installed:

  • Python (version X.X.X)

Installation

For macOS:

  1. Clone the Repository: Clone this repository to your local machine using the following command: bash git clone https://github.com/your-username/animal-detection.git

  2. Navigate to the Repository: Change into the cloned repository directory: bash cd animal-detection

  3. Install Requirements: Install the required dependencies by running the following command: bash pip install -r requirements.txt

For Windows:

  1. Clone the Repository: Clone this repository to your local machine using the following command: bash git clone https://github.com/your-username/animal-detection.git

  2. Navigate to the Repository: Change into the cloned repository directory: bash cd animal-detection

  3. Install Requirements: Install the required dependencies by running the following command: bash pip install -r requirements.txt

For Linux:

  1. Clone the Repository: Clone this repository to your local machine using the following command: bash git clone https://github.com/your-username/animal-detection.git

  2. Navigate to the Repository: Change into the cloned repository directory: bash cd animal-detection

  3. Install Requirements: Install the required dependencies by running the following command: bash pip install -r requirements.txt

Usage

Follow these steps to run the animal detection model:

  1. Download Pre-trained Model: Download the pre-trained YOLOv5 model from here and place it in the repository directory.

  2. Run the Model: Open your preferred code editor or terminal and execute the following command: bash python detect.py --weights "path/to/your/model.pt" --img 640 --conf 0.25 --source "path/to/your/video.mp4" Replace "path/to/your/model.pt" with the path to your pre-trained YOLOv5 model file, and "path/to/your/video.mp4" with the path to the video file you want to detect animals in.

  3. View Results: Once the script finishes running, the detected animals will be annotated in the video. You can view the output video to see the detections.

Supported Animals

The model is capable of detecting the following 80 animals:

  • Bear
  • Brown bear
  • Bull
  • Butterfly
  • Camel
  • Canary
  • Caterpillar
  • Cattle
  • Centipede
  • Cheetah
  • Chicken
  • Crab
  • Crocodile
  • Deer
  • Duck
  • Eagle
  • Elephant
  • Fish
  • Fox
  • Frog
  • Giraffe
  • Goat
  • Goldfish
  • Goose
  • Hamster
  • Harbor seal
  • Hedgehog
  • Hippopotamus
  • Horse
  • Jaguar
  • Jellyfish
  • Kangaroo
  • Koala
  • Ladybug
  • Leopard
  • Lion
  • Lizard
  • Lynx
  • Magpie
  • Monkey
  • Moths and butterflies
  • Mouse
  • Mule
  • Ostrich
  • Otter
  • Owl
  • Panda
  • Parrot
  • Penguin
  • Pig
  • Polar bear
  • Rabbit
  • Raccoon
  • Raven
  • Red panda
  • Rhinoceros
  • Scorpion
  • Sea lion
  • Sea turtle
  • Seahorse
  • Shark
  • Sheep
  • Shrimp
  • Snail
  • Snake
  • Sparrow
  • Spider
  • Squid
  • Squirrel
  • Starfish
  • Swan
  • Tick
  • Tiger
  • Tortoise
  • Turkey
  • Turtle
  • Whale
  • Woodpecker
  • Worm
  • Zebra

Feel free to customize the paths and instructions as needed for your specific setup. Additionally, provide any additional information or troubleshooting tips that users may find helpful.

Owner

  • Name: Harshavardhan
  • Login: Harshavardhan856
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
pyproject.toml pypi
requirements.txt pypi
  • Pillow >=9.4.0
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
  • wheel >=0.38.0
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==19.10.0
  • pip ==23.3
  • werkzeug >=3.0.1