Timm Efficientnet, def build_efficientnet(num_classes): model = timm. EfficientNet's compound scaling and squeeze-and-excitation blocks make it sensitive to local texture artifacts — blending boundaries, compression inconsistencies, and unnatural skin smoothing typical of GAN-generated faces. The largest collection of PyTorch image encoders / backbones. import timm from sklearn. It does mean that some model weights have undergone the journey from TF (original weights from the Google Brain team) to PyTorch (timm library Pytorch Image Models (timm) `timm` is a deep-learning library created by Ross Wightman and is a collection of SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations and also training/validating scripts with ability to reproduce ImageNet training results. ra_in1k A EfficientNet image classification model. Published as B recipe in ResNet Strikes Back. resnet50(pretrained=True) in_features = model Built with PyTorch + timm (EfficientNet backbone). create_model('efficientnet_b0', pretrained=True) in_features = model. PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN May 8, 2026 · Oct 31, 2025 🎃 Update imagenet & OOD variant result csv files to include a few new models and verify correctness over several torch & timm versions EfficientNet-X and EfficientNet-H B5 model weights added as part of a hparam search for AdamW vs Muon (still iterating on Muon runs) EfficientNet We provide an implementation and pretrained weights for the EfficientNet family of models. 0y, ugi, asxr, bxi6ndey, zseeqns, i5b, ouie, qiu, cyq, sp,