Metadata-Version: 2.1
Name: LeiCV
Version: 0.0.1
Summary: LeiCV for Image Classification.
Project-URL: Homepage, https://github.com/Letty51/Letty51.github.io
Author-email: L <123641640@qq.com>
License: MIT License
        
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License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# PyTorch Image Classification

Classifies an image as containing either a dog or a cat (using Kaggle's <a href="https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data">public dataset</a>), but could easily be extended to other image classification problems.

### Dependencies:
- PyTorch / Torchvision
- Numpy
- PIL
- CUDA

## Data

The data directory structure I used was:

* project
  * data
    * train
      * dogs
      * cats
    * validation
      * dogs
      * cats
    * test
      * test

## Performance
The result of the notebook in this repo produced a log loss score on Kaggle's hidden dataset of 0.04988 -- further gains can probably be achieved by creating an ensemble of classifiers using this approach. 
