https://github.com/vovod/animal-classify
Animal Image Classification
https://github.com/vovod/animal-classify
animals efficientnet pytorch
Last synced: 20 days ago
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Animal Image Classification
- Host: GitHub
- URL: https://github.com/vovod/animal-classify
- Owner: vovod
- Created: 2024-05-02T02:18:10.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-13T15:05:31.000Z (9 months ago)
- Last Synced: 2025-05-13T16:27:42.582Z (9 months ago)
- Topics: animals, efficientnet, pytorch
- Language: Python
- Homepage:
- Size: 24.4 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Animal Image Classification
This project is a simple image classification project using EfficientNetV2 to classify images of animals. The dataset used in this project is the [Kaggle Animal Image Dataset](https://www.kaggle.com/datasets/iamsouravbanerjee/animal-image-dataset-90-different-animals). The dataset contains 90 different animals with 60 images per class.
## Requirements
- Run the following command to install the required packages:
`pip install -r requirements.txt`
- Cuda 12.1 is required to run the code on GPU.
## Usage
- Run the following command to train the model without pre-trained weights (EfficientNetV2):
`python train.py`
- Run the following command to train the model with pre-trained weights (EfficientNetV2):
`python pretrained.py`
- Run the following command to test the model after training:
`python test_predict.py`
## Results
The model trained without pre-trained weights achieved an best accuracy of 0.453 on the test set after 200 epochs.
The model trained with pre-trained weights achieved an best accuracy of 0.953 on the test set.