semvec/pypi-packages huggingface dataset:
objifier 0.1.1
Object Classifier
Requires Python: >=3.6

# OBJECT CLASSIFIER 

## Clone this repository
- for SSH
```
git clone git@github.com:1chimaruGin/Object-classifier.git
```

- for https
```
https://github.com/1chimaruGin/Object-classifier.git
```

## Requirements
```
pip install -U requirements.txt
```

## Dataset

- the dataset directory should be the following format.

```

# for example, dog vs cat classification
data/
    -train/
        dog/
            -*.jpg or *.png
        cat/
            -*.jpg or *.png
    -val/
        dog/
            -*.jpg or *.png
        cat/
            -*.jpg or *.png
```

## Usage with Argparse
```
cd Object-classifier/objifier
```
- Update number of classes and names in data.yaml

- For training model(ResNet)

```
$ python main.py -m [mode: train] -opt [optimizer: (default='SGD')]  -epochs [epochs: (default=25)] 
```
- For training model(EfficientNet)

```
$ python main.py -m [mode: train] -opt [optimizer]  -epochs [epochs] -backbone [backbone: efficientNet] -lvl [efficientNet level]
```
- For prediction
```
$ python main.py -m [mode: predict] -im [input image] - backbone [backbone: ResNet or efficientNe] -lvl [efficientNet level]
```

## Usage with YAML (via pip)

#### Create a YAML file as sample below:

- For training [train.yaml]
```
nc: 10
# names: ['mantled_howler', 'patas_monkey', 'bald_uakari', 'japanese_macaque', 'pygmy_marmoset', 
#       'white_headed_capuchin', 'silvery_marmoset', 'common_squirrel_monkey', 'black_headed_night_monkey','nilgiri_langur' ]

names: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

backbone: 'efficientNet'

efftlvl: 0

optimizer: 'Adam'

mode: 'train'

epoch: 2

load: False

output: 'output'

dataset_path: null

```
- For prediction [predict.yaml]

```
nc: 10
# names: ['mantled_howler', 'patas_monkey', 'bald_uakari', 'japanese_macaque', 'pygmy_marmoset', 
#       'white_headed_capuchin', 'silvery_marmoset', 'common_squirrel_monkey', 'black_headed_night_monkey','nilgiri_langur' ]

names: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

backbone: 'efficientNet'

efftlvl: 0

output: 'output'

image: 'baobao.jpg'

```
Botlabs.py 1.0.4
A simple API wrapper for discordbotlabs.org
Requires Python: >=3.6

![Version](https://img.shields.io/pypi/v/Botlabs.py) ![Requirements](https://img.shields.io/pypi/pyversions/Botlabs.py) ![Has wheel](https://img.shields.io/pypi/wheel/Botlabs.py) ![Docs website up](https://img.shields.io/website?down_color=red&down_message=Docs%20down&up_color=blue&up_message=Docs%20up&url=https%3A%2F%2Fpjotr07740.github.io%2Fbotlabs.py%2F) ![Contributors](https://img.shields.io/github/contributors/pjotr07740/botlabs.py) ![Last commit](https://img.shields.io/github/last-commit/pjotr07740/Botlabs.py) ![Live](https://img.shields.io/twitch/status/pjotr0707) ![Stable](https://img.shields.io/pypi/status/Botlabs.py) ![Discord](https://img.shields.io/discord/715558840959238154)

# botlabs.py
### About
botlabs.py is an API wrapper for discordlabs.org.

### Documentation
https://pjotr07740.github.io/botlabs.py/

### Install
```
pip install Botlabs.py
```
classification-library 0.0.3
A classification library using a novel audio-inspired algorithm.

# Classification Library

*A classification library using a [novel audio-inspired technique](https://youtu.be/dQw4w9WgXcQ).*

## Installation

Try:

```
pip install classification_library
```

Except:

```
pip install git+https://github.com/lol-cubes/classification-library
```

## Usage

```python
from classification_library import AudioClassifier
import numpy as np


X_train = np.array([1, 2, 3, 4])
y_train = np.array([1, 4, 9, 16])
X_test = np.array([5, 6])
y_test = np.array([25, 36])


model = AudioClassifier(alpha=10)
model.fit(X_train, y_train)

predictions = model.predict(X_test)
correct_predictions = 0
for y_true, y_pred in zip(y_test, predictions):
    if y_true == y_pred:
        correct_predictions += 1
print(f"Accuracy: {correct_predictions / len(y_test)}")
```