# convnets
🚧 Under construction
Convolutional Neural Networks and utilities for Computer Vision.
- [Learn](learn) about convnets.
- Learn about popular [models](models).
## Models API
`convnets` offers implementations for the following models:
- [LeNet](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)
- [AlexNet](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)
- [VGG](https://arxiv.org/abs/1409.1556)
- [ResNet](https://arxiv.org/abs/1512.03385)
To instantiate a model you need to import the corresponding class and pass a valid `configuration` object to the constructor:
```python
from convnets.models import ResNet
r18_config = {
'l': [
{'r': 2, 'f': 64},
{'r': 2, 'f': 128},
{'r': 2, 'f': 256},
{'r': 2, 'f': 512}
],
'b': False
}
model = ResNet(r18_config)
```
Or you can use one of the predefined configurations, or variants:
```python
from convnets.models import ResNet, ResNetConfig
model = ResNet(ResNetConfig.r18)
```
You can find the implementation of each model and configuration examples in the [`convnets/models`](convnets/models) directory.
## Training API
If you want to train a model in your notebooks, you can use our [fit](convents/train/fit.py) function:
```python
form convnets.train import fit
hist = fit(model, dataloader, optimizer, criterion, metrics, max_epochs)
```
You can use any Pytorch model. You will need to define the Pytorch dataloader, optimizer and criterion. For the metrics, the function expects a dict with the name of the metric as key and the metric function as value. The metric function must receive the model output and the target and return a scalar value. You can find some examples in [`convnets/metrics`](convnets/metrics.py). The `max_epochs` parameter is the maximum number of epochs to train the model. The function will return a dict with the training history.
Additionally, we offer a [training script](train/train.py) that you can execute from the command line.
```bash
python scripts/train.py <path_to_config_file>
```
You will have to pass the path to a yaml file with the configuration for your training, including the model, optimizer, criterion, metrics, dataloader, etc. You can find some examples in the [`configs`](scripts/configs) directory (which are `timm` and `pytorch-lightning` compatible).
We also offer Pytorch Lightning interoperability.
