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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS
| Date | Stars |
|---|---|
| 2026-07-24 | 1580 |
| 2026-07-25 | 1580 |
| 2026-07-28 | 1580 |
| 2026-07-30 | 1580 |
| 2026-08-06 | 1580 |
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# (Generic) EfficientNets for PyTorch ```diff -- **NOTE** This repo is not being maintained -- ``` Please use [`timm`](https://github.com/huggingface/pytorch-image-models) instead. It includes all of these model definitions (compatible weights) and much much more. A 'generic' implementation of EfficientNet, MixNet, MobileNetV3, etc. that covers most of the compute/parameter efficient architectures derived from the MobileNet V1/V2 block sequence, including those found via automated neural architecture search. All models are implemented by GenEfficientNet or MobileNetV3 classes, with string based architecture definitions to configure the block layouts (idea from [here](https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_models.py)) ## What's New ### Aug 19, 2020 * Add updated PyTorch trained EfficientNet-B3 weights trained by myself with `timm` (82.1 top-1) * Add PyTorch trained EfficientNet-Lite0 contributed by [@hal-314](https://github.com/hal-314) (75.5 top-1) * Update ONNX and Caffe2 export / utility scripts to work with latest PyTorch / ONNX * ONNX runtime based validation script added * activations (mostly) brought in sync with `timm` equivalents ### April 5, 2020 * Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite * 3.5M param MobileNet-V2 100 @ 73% * 4.5M param MobileNet-V2 110d @ 75% * 6.1M param MobileNet-V2 140 @ 76.5% * 5.8M param MobileNet-V2 120d @ 77.3% ### March 23, 2020 * Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite) * Add PyTorch trained MobileNet-V3 Large weights with 75.77% top-1 * IMPORTANT CHANGE (if training from scratch) - weight init changed to better match Tensorflow impl, set `fix_group_fanout=False` in `initialize_weight_goog` for old behavior ### Feb 12, 2020 * Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) * Port new EfficientNet-B8 (RandAugment) weights from TF TPU, these are different than the B8 AdvProp, different input normalization. * Add RandAugment PyTorch trained EfficientNet-ES (EdgeTPU-Small) weights with 78.1 top-1. Trained by [Andrew Lavin](https://github.com/andravin) ### Jan 22, 2020 * Update weights for EfficientNet B0, B2, B3 and MixNet-XL with latest RandAugment trained weights. Trained with (https://github.com/rwightman/pytorch-image-models) * Fix torchscript compatibility for PyTorch 1.4, add torchscript support for MixedConv2d using ModuleDict * Test models, torchscript, onnx export with PyTorch 1.4 -- no issues ### Nov 22, 2019 * New top-1 high! Ported official TF EfficientNet AdvProp (https://arxiv.org/abs/1911.09665) weights and B8 model spec. Created a new set of `ap` models since they use a different preprocessing (Inception mean/std) from the original EfficientNet base/AA/RA weights. ### Nov 15, 2019 * Ported official TF MobileNet-V3 float32 large/small/minimalistic weights * Modifications to MobileNet-V3 model and components to support some additional config needed for differences between TF MobileNet-V3 and mine ### Oct 30, 2019 * Many of the models will now work with torch.jit.script, MixNet being the biggest exception * Improved interface for enabling torchscript or ONNX export compatible modes (via config) * Add JIT optimized mem-efficient Swish/Mish autograd.fn in addition to memory-efficient autgrad.fn * Activation factory to select best version of activation by name or override one globally * Add pretrained checkpoint load helper that handles input conv and classifier changes ### Oct 27, 2019 * Add CondConv EfficientNet variants ported from https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv * Add RandAug weights for TF EfficientNet B5 and B7 from https://github.com/t
Excerpt of 22,111 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c58132c119eefe9f, topic:pytorch, readme:autograd
matched fp:c58132c119eefe9f, topic:onnx
