semvec/pypi-packages huggingface dataset:
deepsort 0.0.5
Packaged version of the DeepSort repository
License: MIT
Requires Python: >=3.6
Keywords: machine-learning,deep-learning,pytorch,vision,image-classification,object-detection,object-tracking,tracker,bytetrack

<div align="center">
<h2>
  DeepSort-Pip: Packaged version of the DeepSort repository  
</h2>
<h4>
    <img width="500" alt="teaser" src="doc/uav_video.gif">
</h4>
</div>

## <div align="center">Overview</div>

This repo is a packaged version of the [DeepSort](https://github.com/nwojke/deep_sort) algorithm.
### Installation
```
pip install deepsort
```

### Detection Model + DeepSort 
```python
from deepsort.tracker import DeepSortTracker

tracker = DeepSortTracker(args)
for image in images:
   dets = detector(image)
   online_targets = tracker.update(dets)
```
### Citation
```bibtex
@inproceedings{Wojke2017simple,
  title={Simple Online and Realtime Tracking with a Deep Association Metric},
  author={Wojke, Nicolai and Bewley, Alex and Paulus, Dietrich},
  booktitle={2017 IEEE International Conference on Image Processing (ICIP)},
  year={2017},
  pages={3645--3649},
  organization={IEEE},
  doi={10.1109/ICIP.2017.8296962}
}

@inproceedings{Wojke2018deep,
  title={Deep Cosine Metric Learning for Person Re-identification},
  author={Wojke, Nicolai and Bewley, Alex},
  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2018},
  pages={748--756},
  organization={IEEE},
  doi={10.1109/WACV.2018.00087}
}
```
deduck 1.0.2
Hi! explain your problem to me... quack! quack!
License: MIT


# deduck

Hi! explain your problem to me... quack! quack!

## Authors

- [@ArticOff](https://www.github.com/ArticOff)


## Installation

Install info with pip

```bash
# Linux/macOS
python3 -m pip install -U deduck

# Windows
py -3 -m pip install -U deduck
```
    
## Usage/Examples

1. Open a terminal

2. Write this 
```bash
python -m duck
```

3. That's all !

## Feedback

If you have any feedback, please reach out to us on [our discord](https://articoff.github.io/discord)

## Links

- [Official discord server](https://articoff.github.io/discord)
- [CodeSec Community](https://articoff.github.io/codesec)
- [Source](https://github.com/ArticOff/deduck)
hyperoptim 0.0.1
Hyperparameter Optimization using Genetic Algorithms.
License: MIT
Requires Python: >=3
Keywords: Hyperparameter optimization using Genetic Algorithms.

[![Downloads](https://pepy.tech/badge/complex)](https://pepy.tech/project/complex)
[![Downloads](https://pepy.tech/badge/complex/month)](https://pepy.tech/project/complex/month)
[![Downloads](https://pepy.tech/badge/complex/week)](https://pepy.tech/project/complex/week)

# Hyperoptim
> Hyperparameter Optimization Using Genetic Algorithm.

## Installation

OS X , Windows & Linux:

```sh
pip install hyperoptim
```
## Usage example
Use for find best hyperparameter

```python
from hyperoptim import GASearch, Hparams
import tensorflow as tf
from tensorflow import keras

(img_train, label_train), (img_test, label_test) = keras.datasets.fashion_mnist.load_data()

# define hyperparameter space
ht = Hparams()
hp_units = ht.Int('units', min_value=32, max_value=512, step=32)
hp_learning_rate = ht.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
hp_activation = ht.Choice('activation', values=['relu', 'sigmoid', 'tanh'])

# create the list of hyperparameter
params = [hp_units, hp_learning_rate, hp_activation]

# define model 
params = [hp_units, hp_learning_rate, hp_activation]
def model_builder(params):
    model = keras.Sequential()
    model.add(keras.layers.Flatten(input_shape=(28, 28)))
    # here params[0] refer to hp_units and params[2] refer to hp_activation
    model.add(keras.layers.Dense(units=params[0], activation=params[2]))
    model.add(keras.layers.Dense(10))
    model.compile(optimizer=keras.optimizers.Adam(learning_rate=params[1]),
                    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                    metrics=['accuracy'])
    return model

# intialize the GASearch
tuner = GASearch(model_builder=model_builder, params=params, objective='val_accuracy', weights=(1.0,), max_epochs=10, directory='my_dir', project_name='intro_to_kt')

# run the search                  
tuner.search(img_train, label_train, epochs=2, validation_split=0.2)

# Get the optimal hyperparameters
best_hps=tuner.get_best_hyperparameters()[0]

# Build the model with the optimal hyperparameters and train it on the data for 50 epochs
model = tuner.build(best_hps)
history = model.fit(img_train, label_train, epochs=2, validation_split=0.2)

val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))

eval_result = model.evaluate(img_test, label_test)
print("[test loss, test accuracy]:", eval_result)
```

## Development setup
For local development setup

```sh
git clone https://github.com/deepak7376/hyperoptim
cd hyperoptim
pip install -r requirements.txt
```

## Meta
Deepak Yadav

Distributed under the MIT license. See ``LICENSE`` for more information.
[https://github.com/deepak7376/hypertune/blob/master/LICENSE](https://github.com/deepak7376)

## References
None