semvec/pypi-packages huggingface dataset:
trefleapi 0.1.0
A simple wrapper for the trefle botanical RESTAPI
License: MIT
Requires Python: >=3.10
Keywords: trefle,botanical,plants,api

# Trefle-Api

*Trefle-API* is a simple python API wrapper for the [trefle](https://trefle.io./) botanic data API

```python
>>> from trefle_api import TrefleAPI
>>> Client = TrefleAPI(token="trefle_api_token")
>>> Query = Client.search("rose").in_("plants").filter(family="Rosaceae").sort_by(slug="asc")
>>> Query.get_json()
"{'data': [{'id': 265580, 'common_name': 'Field rose', 'slug': 'rosa-arvensis', 'scientific_name': 'Rosa arvensis'..."
>>> New_Query = Query.range(year=[2000, 2010]).exclude(common_name="null")
>>> New_Query.get_models()
"[[Plant(id=345576, name='', slug='physaria-purpurea', common_name='Rose bladderpod', scientific_name='Physaria purpurea', year=2002,..."
```

This package allows you to build simple to more complex queries to fetch data from the the [trefle](https://trefle.io./) RESTAPI <br>
Query tags :
 - `search()`
    <br> description : search a query  
    args : __q__ # **the string to use as query**
    returns : a copy of the `Query` class to allow chaining
 - `retrieve()`
    <br> description : retrieve an item based on it's slug or id  
    args : __slug__ or __id__ # a string representing the slug of a vegetable or it's id in the trefle api records  
    returns : a copy of the `Query` class to allow chaining

- `in_()`
 <br> description : select a category in which to search or retrieve  
 args __category__ # available categories are : 
  - kingdoms
  - subkingdoms
  - divisions
  - divisionclasses
  - division_orders
  - families
  - genus
  - plants
  - species
augshufflenet-pytorch 0.0.2
AugShuffleNet: Communicate More, Compute Less - Pytorch
License: MIT
Keywords: artificial intelligence,deep learning,shufflenet,convnet,computer vision

# AugShuffleNet: Communicate More, Compute Less

AugShuffle1_0x has slightly different channels than the paper due to constraints in of the default `r`.

See: <https://arxiv.org/abs/2203.06589>

## Usage
```
import torch
from augshufflenet_pytorch import AugShuffleNet0_5x, AugShuffleNet1_0x, AugShuffleNet1_5x, AugShuffleNet


model = AugShuffleNet0_5x(input_channels=3)
x = model(torch.randn(1, 3, 64, 64)) # [1, 192]

# Equivalent to 0_5x
model = AugShuffleNet(stages_repeats=[3, 7, 3], stages_out_channels=[24, 48, 96, 192], input_channels=3, r=0.375)
x = model(torch.randn(1, 3, 64, 64)) # [1, 192]
```

> NOTE: each of the int(out_channels * r) & out_channels putneeds to be divisible by 2

## Citation
```
@misc{ye_augshufflenet_2022,
	title = {{AugShuffleNet}: {Communicate} {More}, {Compute} {Less}},
	shorttitle = {{AugShuffleNet}},
	url = {http://arxiv.org/abs/2203.06589},
	doi = {10.48550/arXiv.2203.06589},
	abstract = {As a remarkable compact model, ShuffleNetV2 offers a good example to design efficient ConvNets but its limit is rarely noticed. In this paper, we rethink the design pattern of ShuffleNetV2 and find that the channel-wise redundancy problem still constrains the efficiency improvement of Shuffle block in the wider ShuffleNetV2. To resolve this issue, we propose another augmented variant of shuffle block in the form of bottleneck-like structure and more implicit short connections. To verify the effectiveness of this building block, we further build a more powerful and efficient model family, termed as AugShuffleNets. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShuffleNet consistently outperforms ShuffleNetV2 in terms of accuracy with less computational cost and fewer parameter count.},
	urldate = {2023-06-09},
	publisher = {arXiv},
	author = {Ye, Longqing},
	month = aug,
	year = {2022},
	note = {arXiv:2203.06589 [cs]},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}
```