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Types & classes40 in github.com/AhmedZgaren/Save

↓ 6 callersClassCustomImageDataset
utils/utils.py:121
↓ 4 callersClassCotLayer
model/cotLayer.py:10
↓ 3 callersClassTrCount
SAVE implementation
model/trcount.py:9
↓ 1 callersClassLocalConvolution
model/aggregation_zeropad.py:199
↓ 1 callersClassRegNet
A regressor network to predict the final count multiple linear layers
model/regressor.py:7
↓ 1 callersClassTrans
model/transformer.py:14
↓ 1 callersClassTransformer
SAMM class definition
model/transformer.py:59
↓ 1 callersClassYOLOFeatures
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
ClassAggregationZeropad
model/aggregation_zeropad.py:112
ClassBaseModel
The BaseModel class serves as a base class for all the models in the Ultralytics YOLO family.
utils/tasks.py:30
ClassDetect
YOLOv8 Detect head for detection models.
utils/tasks.py:491
ClassDetectionModel
YOLOv8 detection model.
utils/tasks.py:181
ClassHardMish
model/activations.py:115
ClassHardMishJit
model/activations_jit.py:85
ClassHardMishJitAutoFn
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
ClassHardMishMe
model/activations_me.py:200
ClassHardSigmoid
model/activations.py:95
ClassHardSigmoidJit
model/activations_jit.py:54
ClassHardSigmoidJitAutoFn
model/activations_me.py:107
ClassHardSigmoidMe
model/activations_me.py:123
ClassHardSwish
model/activations.py:79
ClassHardSwishJit
model/activations_jit.py:68
ClassHardSwishJitAutoFn
A memory efficient, jit-scripted HardSwish activation
model/activations_me.py:143
ClassHardSwishMe
model/activations_me.py:160
ClassImgAugTransform
utils/augmentation.py:6
ClassMish
Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
model/activations.py:36
ClassMishJit
model/activations_jit.py:40
ClassMishJitAutoFn
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
ClassMishMe
model/activations_me.py:88
ClassPositionalEncoding
model/transformer.py:80
ClassSigmoid
model/activations.py:51
ClassSwish
model/activations.py:20
ClassSwishJit
model/activations_jit.py:32
ClassSwishJitAutoFn
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
ClassSwishMe
model/activations_me.py:49
ClassTanh
model/activations.py:65
Classset_exportable
model/config.py:48
Classset_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
Classset_no_jit
model/config.py:29
Classset_scriptable
model/config.py:67