semvec/pypi-packages huggingface dataset:
pycounts-ts-mds 0.2.0
A short test package that counts the words in a given file.
License: MIT
Requires Python: >=3.11

# pycounts_ts_mds

[![codecov](https://codecov.io/github/SimplyTim/pycounts_ts_mds/graph/badge.svg?token=5WRCGL2RGR)](https://codecov.io/github/SimplyTim/pycounts_ts_mds)

A short test package that counts the words in a given file.

## Installation

```bash
$ pip install pycounts_ts_mds
```

## Usage

`pycounts_ts_mds` can be used to count words in a text file and plot results
as follows:

```python
from pycounts_ts_mds.pycounts_ts_mds import count_words
from pycounts_ts_mds.pycounts_ts_mds import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycounts_ts_mds` was created by Timothy Singh. It is licensed under the terms of the MIT license.

## Credits

`pycounts_ts_mds` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
torchimageclass 0.0.2
A library for image classification using PyTorch.
License: MIT
Requires Python: >=3.9
Keywords: pytorch, deep-learning, image-classification, machine-learning, computer-vision, classification, torch, TorchImageClass, neural-networks, cnn, transfer-learning

# TorchImageClass

**TorchImageClass** é uma biblioteca em Python que fornece um conjunto de ferramentas e utilitários para **classificação de imagens** usando [PyTorch](https://pytorch.org/). Ela visa facilitar o **treinamento**, **validação** e **inferência** de redes neurais voltadas a tarefas de classificação.

---

## Sumário

- [Motivação](#motivação)
- [Principais Recursos](#principais-recursos)
- [Instalação](#instalação)
- [Estrutura de Pastas do Dataset](#estrutura-de-pastas-do-dataset)
- [Como usar](#como-usar)

---

## Motivação

Modelos de classificação de imagens são amplamente usados em aplicações de visão computacional, desde identificação de objetos até detecção de doenças em exames de imagem. O **TorchImageClass** simplifica esse processo, fornecendo:

- **Arquiteturas** pré-definidas (ResNet, etc.) ou personalizáveis.  
- **Data loaders** e **transforms** para imagens.  
- **Treinamento** com otimização, métricas e *early stopping*.  
- **Práticas de *logging* e salvamento de modelos** (checkpoints).  
- **Inferência** em lote ou em imagens individuais.

---

## Principais Recursos

1. **Fácil inicialização de Modelos**  
   - Classes prontas para ResNet, VGG, etc., com poucas linhas de código.

2. **Treinamento Automatizado**  
   - Loop de treino e validação com métricas integradas (acurácia, precisão, recall).

3. **Data Augmentation Simplificada**  
   - Integração direta com `torchvision.transforms`.

4. **Suporte a GPU/CPU**  
   - Reconhecimento automático de dispositivo (CUDA/ROCm ou CPU).

5. **Exportação de Modelos**  
   - Salva pesos em formatos `.pth` ou `.pt`.

---

## Instalação

Para instalar via [PyPI](https://pypi.org/):

```bash
pip install torchimageclass
```

## Estrutura de Pastas do Dataset

Para que o torchimageclass funcione corretamente, é esperado que sua pasta principal (```base_dir```) tenha a seguinte organização:

```bash
data/
├── train/
│   ├── class_1/
│   │   ├── img1.jpg
│   │   ├── img2.jpg
│   │   └── ...
│   └── class_2/
│       ├── img1.jpg
│       ├── img2.jpg
│       └── ...
└── test/
    ├── class_1/
    │   ├── imgX.jpg
    │   └── ...
    └── class_2/
        ├── imgY.jpg
        └── ...

```

O exemploo acima mostra apenas duas classes dentro das pastas ```class_1``` e ```class_2```, mas pode conter quantas classes forém necessárias para seu projeto.

## Como usar

```bash
from TorchImageClass import train_model

model, history = train_model(
    base_dir="data/images for prediction of treated tuberculosis",
    num_epochs=20,
    image_size=(224,224),
    batch_size=8,
    num_classes=2,
    patience=5,
    model_save_path="best_model.pth"
)

print("Final History:", history)
print("Trained model:", model)
pycounts-jd25 0.2.0
Calculate word counts in a text file and plot word counts
License: MIT
Requires Python: >=3.10

# pycounts_jd25

[![codecov](https://codecov.io/github/jdaduica/pycounts_jd25/graph/badge.svg?token=01X3OJTTOQ)](https://codecov.io/github/jdaduica/pycounts_jd25)

Calculate word counts in a text file!

Author:Julian Daduica

Year: 2025

## Installation

```bash
$ pip install pycounts_jd25
```

## Usage

`pycounts_jd25` can be used to count words in a text file and plot results
as follows:

```python
from pycounts_jd25.pycounts_jd25 import count_words
from pycounts_jd25.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## Contributing

Interested in contributing? Check out the contributing guidelines. 
Please note that this project is released with a Code of Conduct. 
By contributing to this project, you agree to abide by its terms.

## License

`pycounts_jd25` was created by Julian Daduica. It is licensed under the terms
of the MIT license.

## Credits

`pycounts_jd25` was created with 
[`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and 
the `py-pkgs-cookiecutter` 
[template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
bf25-pycounts 0.3.1
Calculate word counts in a text file!
License: MIT
Requires Python: >=3.9

# bf25_pycounts
[![codecov](https://codecov.io/github/bfrizzell01/bf25_pycounts/graph/badge.svg?token=DJ8A1JHO4X)](https://codecov.io/github/bfrizzell01/bf25_pycounts)
[![Documentation Status](https://readthedocs.org/projects/bf25-pycounts/badge/?version=latest)](https://bf25-pycounts.readthedocs.io/en/latest/?badge=latest)


Calculate word counts in a text file!

Author: Benjamin Frizzell

## Installation

```bash
$ pip install bf25_pycounts
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`bf25_pycounts` was created by Benjamin Frizzell. It is licensed under the terms of the MIT license.

## Credits

`bf25_pycounts` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).