![]() Choose to make your file accessible by the link and share it with others.If you have any issues locating or applying the option to Fine-tune Radio Resolution For Free, get in touch with our professional support team.Explore the top toolbar and text the available features to modify, annotate, certify and optimize your file.If your document contains many pages, experiment with the view of your file for smoother navigation.Upload your document through the drag and drop area or use any other way of importing it.Here's how you can easily Fine-tune Radio Resolution For Free with DocHub: In addition, DocHub integrates with different Google Workspace apps as well as solutions, making document exporting and importing a breeze. You can access the essential features for handling document-based workflows, like certifying, importing text, etc., even with a free plan. It's straightforward to navigate, use, and make edits to the document whenever you need it. _base_ = # Model config model = dict ( backbone = dict ( frozen_stages = 2, init_cfg = dict ( type = 'Pretrained', checkpoint = '', prefix = 'backbone', )), head = dict ( num_classes = 10 ), ) # Dataset config img_norm_cfg = dict ( mean =, std =, to_rgb = False, ) train_pipeline = ), dict ( type = 'ToTensor', keys = ), dict ( type = 'Collect', keys = ), ] test_pipeline = ), dict ( type = 'Collect', keys = ), ] data = dict ( train = dict ( pipeline = train_pipeline ), val = dict ( pipeline = test_pipeline ), test = dict ( pipeline = test_pipeline ), ) # Training schedule config # lr is set for a batch size of 128 optimizer = dict ( type = 'SGD', lr = 0.01, momentum = 0.9, weight_decay = 0.Are you having a hard time choosing a reliable solution to Fine-tune Radio Resolution For Free? DocHub is designed to make this or any other process built around documents more streamlined. Requires smaller learning rate and less training epochs. The fine-tuning hyper parameters vary from the default schedule. Img_norm_cfg = dict ( mean =, std =, to_rgb = False, ) train_pipeline = ), dict ( type = 'ToTensor', keys = ), dict ( type = 'Collect', keys = ), ] test_pipeline = ), dict ( type = 'Collect', keys = ), ] data = dict ( train = dict ( pipeline = train_pipeline ), val = dict ( pipeline = test_pipeline ), test = dict ( pipeline = test_pipeline ), ) Modify training schedule ¶ To inherit all above configs, put the following code at the config file. Training schedules, the new config needs to inherit Simply inherit configs/_base_/datasets/cifar10_bs16.py. To use the CIFAR-10 dataset, the new config can also To fine-tune a ResNet-50 model, the newĬonfig needs to inherit configs/_base_/models/resnet50.py to build the basic ![]() ![]() To reuse the common parts among different configs, we support inheritingĬonfigs from multiple existing configs. To take the fine-tuning on the CIFAR-10 dataset, we need to modify five parts in theĬonfigs/tutorial/resnet50_finetune_cifar.py to store our configs. Modify the configs as will be discussed in this tutorial.Īssume we have a ResNet-50 model pre-trained on the ImageNet-2012 dataset and want There are two steps to fine-tune a model on a new dataset.Īdd support for the new dataset following Tutorial 3: Customize Dataset. ![]() This tutorial provides instructions for users to use the models provided in the Model Zoo for other datasets to obtain better performance. Tutorial 2: Fine-tune Models ¶Ĭlassification models pre-trained on the ImageNet dataset have been demonstrated to be effective for other datasets and other downstream tasks. Check the installation tutorial, migration tutorial and changelog for more details. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. ![]()
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