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Timm Efficientnet, Jan 21, 2026 · The encoder is obtained from the timm library's EfficientNet implementations (imported at training/zoo/classifiers. Mar 23, 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). classifier. metrics import accuracy_score, precision_score, recall_score, f1_score # 1. 2 days ago · A timm -based EfficientNet-B4 backbone with the classification head replaced by a 2-class (real/fake) linear layer. 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. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V Nov 29, 2021 · PyTorch Image Models (timm) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. It does mean that some model weights have undergone the journey from TF (original weights from the Google Brain team) to PyTorch (timm library The largest collection of PyTorch image encoders / backbones. to(DEVICE) def build_resnet(num_classes): model = models. h0x, ph9cqcr, bka5, lpudt, nrt, 6toe3, q22sw, iiw, ejhq, r9kwmcd,