This repository contains reimplementation of state-of-the-art Image Classification models:

The goal of this implementation is to be simple, flat structured,
highly extensible, and easy to integrate into your own projects.

At current moment, you can easily:

• Build any of the above models in nn.Module class
• Use models for classification or feature extraction
• Train/Test models with your in-house data

Upcoming features: In the next few days, you will be able to:

• Local installation / pip installation
• Evaluate models within training
• Load pre-trained weights for models
• Finetune models on your own dataset

1. About Models
   • Model Details
2. Requesties
3. Installation
4. Usage
   • Example: Train from bash
   • Example: Customize in python
5. Contributing
6. LICENSE

We collect models that bring huge impacts to Image Classification task, and re-implement it with flat structured code. Although most of these models have several types where their layers altered such as ResNet18/30/50, we only implement one of those in order to remain code legibility.

All Params are calculated with input size=(224, 224, 3) and classes=1000. Top-1 Acc. were evaluated by pre-trained model on ImageNet dataset

• keras v2.2.4

• pytorch v1.4
• pytorch-lightning [doc]
• opencv

! Will update soon

python train.py -m VGG16 -f PATH_TO_TRAINDATA
"""
option: -m : neural network structure
        -f : path to train data folder, structure showed as below:
                train_folder
                ├─ Class1 (e.g. dog)
                |   ├─ image1.jpg
                |   └─ image2.jpg
                └─ Class2 (e.g. cat)
                    ├─ image1.jpg
                    └─ image2.jpg
"""        

Load a model in python:

from .models.SOME_MODEL import SOME_MODEL

If you want us to add other models, feel free to create an issue, or submit a pull request. If you find a bug or having any question, create an issue and we'll solve it ASAP.

WTFPL licensed, just do what the fuck you want to public, cheers.