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torch.autograd
Automatic differentiation package - torch.autograd torch.autograd.backward(variables, grad_variables, retain_variables=False) Variable API 兼容性 In-place operations on Variables ... -
9.4 锚框
9.4 锚框 9.4.1 生成多个锚框 9.4.2 交并比 9.4.3 标注训练集的锚框 9.4.4 输出预测边界框 小结 9.4 锚框 目标检测算法通常会在输入图像中采样大量的区域，然后判断这些区域中是否包含我们感兴趣的目标，并调整区域边缘从而更准确地预测目标的真实边界框（ground-truth bounding box）。不同的模型使... -
torchvision.models
torchvision.models 译者署名 torchvision.models torchvision.models 模块的 子模块中包含以下模型结构。 AlexNet VGG ResNet SqueezeNet DenseNet 可以通过调用构造函数来构造具有随机权重的模型： import torchvision . m... -
3.13 丢弃法
3.13 丢弃法 3.13.1 方法 3.13.2 从零开始实现 3.13.2.1 定义模型参数 3.13.2.2 定义模型 3.13.2.3 训练和测试模型 3.13.3 简洁实现 小结 参考文献 3.13 丢弃法 除了前一节介绍的权重衰减以外，深度学习模型常常使用丢弃法（dropout）[1] 来应对过拟合问题。丢弃法有一些不同的... -
Custom ItemList
Custom ItemList Customizing datasets in fastai Links with the data block API Creating a custom ItemBase subclassBasic attributes Example: ImageTuple Creating a custom Item... -
Data
Vision data Helper functions get_grid [source] clip_remove_empty [source] bb_pad [source] TransformBlock s for visionImageBlock [source] MaskBlock [source] Point... -
Pretrained models
OpenSearch-provided pretrained models Supported pretrained models Sentence transformers Sparse encoding models Cross-encoder models Prerequisites Step 1: Register a model grou... -
Pretrained models
OpenSearch-provided pretrained models Supported pretrained models Sentence transformers Sparse encoding models Cross-encoder models Prerequisites Step 1: Register a model grou... -
torch.autograd
Automatic differentiation package - torch.autograd torch.autograd.backward(variables, grad_variables, retain_variables=False) Variable API 兼容性 In-place operations on Variables ...