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ultralytics 8.0.136 refactor and simplify package (#3748)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Laughing
2023-07-16 23:47:45 +08:00
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parent 8ebe94d1e9
commit 620f3eb218
383 changed files with 4213 additions and 4646 deletions
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5
docs/reference/nn/autobackend.md
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---
description: Ensure class names match filenames for easy imports. Use AutoBackend to automatically rename and refactor model files.
keywords: AutoBackend, ultralytics, nn, autobackend, check class names, neural network
---
## AutoBackend
---
### ::: ultralytics.nn.autobackend.AutoBackend

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docs/reference/nn/modules/block.md
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---
description: Explore ultralytics.nn.modules.block to build powerful YOLO object detection models. Master DFL, HGStem, SPP, CSP components and more.
keywords: Ultralytics, NN Modules, Blocks, DFL, HGStem, SPP, C1, C2f, C3x, C3TR, GhostBottleneck, BottleneckCSP, Computer Vision
---
## DFL
---
### ::: ultralytics.nn.modules.block.DFL

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docs/reference/nn/modules/conv.md
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---
description: Explore convolutional neural network modules & techniques such as LightConv, DWConv, ConvTranspose, GhostConv, CBAM & autopad with Ultralytics Docs.
keywords: Ultralytics, Convolutional Neural Network, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad
---
## Conv
---
### ::: ultralytics.nn.modules.conv.Conv

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docs/reference/nn/modules/head.md
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description: 'Learn about Ultralytics YOLO modules: Segment, Classify, and RTDETRDecoder. Optimize object detection and classification in your project.'
keywords: Ultralytics, YOLO, object detection, pose estimation, RTDETRDecoder, modules, classes, documentation
---
## Detect
---
### ::: ultralytics.nn.modules.head.Detect

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docs/reference/nn/modules/transformer.md
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description: Explore the Ultralytics nn modules pages on Transformer and MLP blocks, LayerNorm2d, and Deformable Transformer Decoder Layer.
keywords: Ultralytics, NN Modules, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer, examples, code snippets, tutorials
---
## TransformerEncoderLayer
---
### ::: ultralytics.nn.modules.transformer.TransformerEncoderLayer

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docs/reference/nn/modules/utils.md
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description: 'Learn about Ultralytics NN modules: get_clones, linear_init_, and multi_scale_deformable_attn_pytorch. Code examples and usage tips.'
keywords: Ultralytics, NN Utils, Docs, PyTorch, bias initialization, linear initialization, multi-scale deformable attention
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## _get_clones
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### ::: ultralytics.nn.modules.utils._get_clones

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docs/reference/nn/tasks.md
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description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
keywords: neural network, deep learning, computer vision, object detection, image segmentation, image classification, model ensemble, PyTorch
---
## BaseModel
---
### ::: ultralytics.nn.tasks.BaseModel
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### ::: ultralytics.nn.tasks.Ensemble
<br><br>
## temporary_modules
---
### ::: ultralytics.nn.tasks.temporary_modules
<br><br>
## torch_safe_load
---
### ::: ultralytics.nn.tasks.torch_safe_load