Model card for efficientnet b3.ra2 in1k A EfficientNet image classification model. Trained on ImageNet 1k in timm using recipe template described below. Recipe details: RandAugment RA2 recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as B recipe in ResNet Strikes Back. RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging Step (exponential decay w/ staircase) LR schedule with warmup Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 12.2 GMACs: 1.6 Activations (M): 21.5 Image size: train = 288 x 288, test = 320 x 320 Papers: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946 ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 Dataset: ImageNet 1k Original: https://github.com/huggingface/pytorch image models Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. Citation
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy