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/ types & classes
Types & classes
40 in github.com/AhmedZgaren/Save
⨍
Functions
163
◇
Types & classes
40
↓ 6 callers
Class
CustomImageDataset
utils/utils.py:121
↓ 4 callers
Class
CotLayer
model/cotLayer.py:10
↓ 3 callers
Class
TrCount
SAVE implementation
model/trcount.py:9
↓ 1 callers
Class
LocalConvolution
model/aggregation_zeropad.py:199
↓ 1 callers
Class
RegNet
A regressor network to predict the final count multiple linear layers
model/regressor.py:7
↓ 1 callers
Class
Trans
model/transformer.py:14
↓ 1 callers
Class
Transformer
SAMM class definition
model/transformer.py:59
↓ 1 callers
Class
YOLOFeatures
YOLO (You Only Look Once) object detection model. Args: model (str, Path): Path to the model file to load or create. ta
model/backbone.py:28
Class
AggregationZeropad
model/aggregation_zeropad.py:112
Class
BaseModel
The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family.
utils/tasks.py:30
Class
Detect
YOLOv8 Detect head for detection models.
utils/tasks.py:491
Class
DetectionModel
YOLOv8 detection model.
utils/tasks.py:181
Class
HardMish
model/activations.py:115
Class
HardMishJit
model/activations_jit.py:85
Class
HardMishJitAutoFn
A memory efficient, jit scripted variant of Hard Mish Experimental, based on notes by Mish author Diganta Misra at https://github.com/digan
model/activations_me.py:180
Class
HardMishMe
model/activations_me.py:200
Class
HardSigmoid
model/activations.py:95
Class
HardSigmoidJit
model/activations_jit.py:54
Class
HardSigmoidJitAutoFn
model/activations_me.py:107
Class
HardSigmoidMe
model/activations_me.py:123
Class
HardSwish
model/activations.py:79
Class
HardSwishJit
model/activations_jit.py:68
Class
HardSwishJitAutoFn
A memory efficient, jit-scripted HardSwish activation
model/activations_me.py:143
Class
HardSwishMe
model/activations_me.py:160
Class
ImgAugTransform
utils/augmentation.py:6
Class
Mish
Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
model/activations.py:36
Class
MishJit
model/activations_jit.py:40
Class
MishJitAutoFn
Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681 A memory efficient, jit scripted variant of
model/activations_me.py:69
Class
MishMe
model/activations_me.py:88
Class
PositionalEncoding
model/transformer.py:80
Class
Sigmoid
model/activations.py:51
Class
Swish
model/activations.py:20
Class
SwishJit
model/activations_jit.py:32
Class
SwishJitAutoFn
torch.jit.script optimised Swish w/ memory-efficient checkpoint Inspired by conversation btw Jeremy Howard & Adam Pazske https://twitter.com/
model/activations_me.py:28
Class
SwishMe
model/activations_me.py:49
Class
Tanh
model/activations.py:65
Class
set_exportable
model/config.py:48
Class
set_layer_config
Layer config context manager that allows setting all layer config flags at once. If a flag arg is None, it will not change the current value.
model/config.py:82
Class
set_no_jit
model/config.py:29
Class
set_scriptable
model/config.py:67