A dynamic global pool implementation that allows selecting from average pooling, max pooling, average + max, or concat([average, max]) at model creation.
Activation factory added along with new activations: select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export), hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish, context mgr for setting exportable/scriptable/no_jit states, Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface, Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this, Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call, Merged a number of execellent contributions in the ResNet model family over the past month, BlurPool2D and resnetblur models initiated by, TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by, ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (, 200 pretrained models in total now with updated results csv in results folder, Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. Minh-Thang Luong Now everyone will know the secrets :(, those secrets arent so novel to begin with. Re-ran batch validation on all models across all sets.
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Here are some example training hparams to get you started. Abstract: We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. These include the Facebook WSL, SSL, SWSL ResNe(Xt) and the Google Noisy Student EfficientNet models. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as good as possible.
Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Facebook used hashtags and photos from Instagram as extra data for an image at classifier and got a huge boost. The Google models do not appear to have any restriction beyond the Apache 2.0 license (and ImageNet concerns). Idea adapted from original DPN implementation when I ported (, SGDR: Stochastic Gradient Descent with Warm Restarts (, SplitBachNorm - allows splitting batch norm layers between clean and augmented (auxiliary batch norm) data. In either case, you should contact Facebook or Google with any questions. I say that jokingly, because obviously there are some real insights into how to leverage a large amount of unlabeled training data here. See initial (WIP) documentation at, DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions, VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1. Many of their additions are the reason why I maintain my own set of models, instead of using others' via PIP: Model validation results can be found in the documentation and in the results tables.
I was wondering if that translates into good resistance to adversarial attack techniques, but it seems like the answer is not really, if I'm reading this right.
Qizhe Xie Some cleanup still needed. • Our guidance learning belongs to the semi-supervised noisy data learning which leverages a teacher–student training strategy to take full use of the whole data space (noisy set and clean set) and the student network trades off the noisy ground truths and soften labels by guidance knowledge. I'd say that if it requires 320M more images, it isn't simple. They compare quite favourably to EfficientNet-Lite, Add EfficientNet-Lite models w/ weights ported from, Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Is this the first time unlabeled data has actually improved ImageNet SOTA? PyTorch image models, scripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2, MNASNet, Single-Path NAS, FBNet, and more. Interesting results there. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images... Work fast with our official CLI. Use Git or checkout with SVN using the web URL. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. on ImageNet Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. I hadn't heard of Imagenet-A, -C, and -P before. Press question mark to learn the rest of the keyboard shortcuts, ' Testing Robustness Against Unforeseen Adversaries '. Code is available at https://github.com/google-research/noisystudent.
Get the latest machine learning methods with code. See documentation for some basics and training hparams for some train examples that produce SOTA ImageNet results. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Title:Self-training with Noisy Student improves ImageNet classification. Learn more. The site may not work properly if you don't, If you do not update your browser, we suggest you visit, Press J to jump to the feed. New/updated weights from training experiments, EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp), RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver), CSPResNet50 - 79.6 top-1 (76.6 from official ver), Some fixes for using pretrained weights with, CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet, (Modified Aligned) Xception41/65/71 (a proper port of TF models), EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by, Add 'real' labels for ImageNet and ImageNet-Renditions test set, see, Train script and loader/transform tweaks to punch through more aug arguments, README and documentation overhaul. It's best to seek legal advice if you intend to use the pretrained weights in a commercial product. All global pooling is adaptive average by default and compatible with pretrained weights.
Universal feature extraction, new models, new weights, new test sets. Badges are live and will be dynamically Please let me know if I missed anything. We iterate this process by putting back the student as the teacher. Trained by, MNASNet B1, A1 (Squeeze-Excite), and Small -, MobileNet-V3 (MBConvNet w/ Efficient Head) -, 'Bag of Tricks' / Gluon C, D, E, S variations -, Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 -, Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts -, Squeeze-and-Excitation Networks (SEResNet) -, All models have a common default configuration interface and API for, doing a forward pass on just the features -, these makes it easy to write consistent network wrappers that work with any of the models, All models support multi-scale feature map extraction (feature pyramids) via create_model (see, feature map channel counts, reduction level (stride) can be queried AFTER model creation via the, All models have a consistent pretrained weight loader that adapts last linear if necessary, and from 3 to 1 channel input if desired, NVIDIA DDP w/ a single GPU per process, multiple processes with APEX present (AMP mixed-precision optional), PyTorch DistributedDataParallel w/ multi-gpu, single process (AMP disabled as it crashes when enabled), PyTorch w/ single GPU single process (AMP optional). rwightman.github.io/pytorch-image-models/, download the GitHub extension for Visual Studio, Add missing leaky_relu layer factory defn, update Apex/Native loss sc….
80.5 (top-1) ResNet-50-D, 77.1 ResNet-34-D, 72.7 …, Fix some attributions, add copyrights to some file docstrings, Simpler approach to loading entrypoints in hubconf works properly, Update requirements so PyTorch 1.4 is min, add separate sotabench req…, Missed moving some seresnet -> legacy in sotabench. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. A full version of the list below with source links can be found in the documentation. They are adaptable for other datasets and use cases with a little hacking. paper.
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