vistrans
Implementations of transformers based models for different vision tasks
Installation
To install this package, run one of the following:
Usage Tracking
0.0.3
0.0.2
2 / 8 versions selected
Downloads (Last 6 months): 0
Description
VisTrans
Implementations of transformers based models for different vision tasks
Install
1) Install from PyPI
pip install vistrans
2) Install from Anaconda
conda install -c nachiket273 vistrans
## Version 0.003 (06/30/2021)
Minor fixes to fix issues with existing models.
## Version 0.002 (04/17/2021)
Pretrained Pytorch Bottleneck Transformers for Visual Recognition including following
* botnet50
* botnet101
* botnet152
Implementation based off Official Tensorflow Implementation
## Usage
pip install vistrans
1) List Pretrained Models.
```Python
from vistrans import BotNet
BotNet.list_pretrained()
2) Create Pretrained Models.
from vistrans import BotNet
model = BotNet.create_pretrained(name, img_size, in_ch, num_classes,
n_heads, pos_enc_type)
3) Create Custom Model
from vistrans import BotNet
model = BotNet.create_model(layers, img_size, in_ch, num_classes, groups,
norm_layer, n_heads, pos_enc_type)
## Version 0.001 (03/04/2021)
Pretrained Pytorch Vision Transformer Models including following
* vits16224
* vitb16224
* vitb16384
* vitb32384
* vitl16224
* vitl16384
* vitl32384
Implementation based off official jax repository and timm's implementation
## Usage
1) List Pretrained Models.
from vistrans import VisionTransformer
VisionTransformer.list_pretrained()
2) Create Pretrained Models.
from vistrans import VisionTransformer
model = VisionTransformer.create_pretrained(name, img_size, in_ch, num_classes)
3) Create Custom Model
from vistrans import VisionTransformer
model = VisionTransformer.create_model(img_size, patch_size, in_ch, num_classes,
embed_dim, depth, num_heads, mlp_ratio,
drop_rate, attention_drop_rate, hybrid,
norm_layer, bias)
About
Summary
Implementations of transformers based models for different vision tasks
Last Updated
Jun 30, 2021 at 17:52
License
MIT
Total Downloads
38
Version Downloads
20
Supported Platforms
linux-64
macOS-64
win-64