![]() ![]() “We’re honored to welcome finetunes and Phonofile to The Orchard family. Those services include physical sales and distribution, global digital marketing, playlist promotion, interactive marketing, digital advertising, synch licensing, video services, performance/neighboring rights collections, and artist royalty processing and more. Labels distributed by finetunes and Phonofile will gain access to The Orchard’s services in over 25 territories worldwide. The specific numbers likely depend on my choice of learning rate, learning rate schedule, and batch size.The move effectively means Sony has acquired four independent European distributors in little over a year: the company swooped for both Essential Music & Marketing (UK) and Century Media (DE) in 2016 for a combined price of just under £16m ($21m). For short schedules (3 epochs for CIFAR-10 and >7 epochs for CIFAR-100), augmentation has a small test accuracy benefit. This stops being true for training without augmentation for >3 epochs for CIFAR-10 and >7 epochs for CIFAR-100. ![]() Results: Broadly, the longer the schedule, the better the final test accuracy. Warmup_steps = 5 decay_type = 'cosine' grad_norm_clip = 1 accum_steps = 8 base_lr = 0.03 I varied the total number of steps in the schedule, keeping the 5 initial warm up steps constant. For finetuning, I am using the default learning rate schedule: 5 iterations of a linear learning rate growth for as a warm up, followed by a (total steps - 5) steps of a cosine schedule. The default data preprocessing in the Colab involves a simple set of augmentations. #Finetunes distribution fullThe finetuning is applied to the full model including head, rather than the head alone. I am then finetuning on CIFAR-10 and CIFAR-100 with a new head of the appropriate number of classes. ![]() We have noticed this effect in our recent paper Exploring the Limits of Out-of-Distribution Detection where test accuracy corresponds well to models’ out-of-distribution capabilities.Įxperiment: I am using a single Vision Transformer, ViT-B_32, pretrained on ImageNet21k. I have been telling people on a one-by-one basis when a conversation got there, but thought writing it up might be useful for others. This is true for short finetuning schedules. Motivation: I have randomly noticed that the Vision Transformer pretrained on ImageNet21k, as shown in the Google Research GitHub repository and in their Colab, finetunes on CIFAR-10 and CIFAR-100 faster when I turn off the training set augmentation that is on by default. For longer finetuning, training without augmentation leads to slightly worse test accuracy. The benefit of augmentation for such prior development in a weaker network might here be overshadowed by the downside of potentially unnaturally broadening the class definitions. Speculatively, this could be due to the sufficiently strong prior the pretraining imparts on the network that doesn’t have to learned from the finetuning dataset directly. However, given a fixed compute budget, not using augmentation seem to lead to a higher test accuracy for shorter finetuning schedules (<3 epochs for CIFAR-10 and <7 epochs for CIFAR-100). Quick summary: The original Google Research GitHub repository has as ViT finetuning Colab which uses training set augmentation by default. Stanislav Fort ( Twitter, Scholar and GitHub) Finetuning on CIFAR-10 If you are willing to run longer finetuning, augmentation gives a slight accuracy boost. It might be worth turning it off for your experiments to speed things up and save compute. Despite this, the official GitHub repository ViT finetuning Colab uses augmentation by default. A Vision Transformer (ViT) pretrained on ImageNet21k finetunes significantly faster without training set augmentation for short optimization schedules on CIFAR-10 (<3 epochs) and CIFAR-100 (<7 epochs). ![]()
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