Model card for efficientnetv2 rw t.ra2 in1k A EfficientNet v2 image classification model. This is a timm specific variation of the architecture. 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): 13.6 GMACs: 1.9 Activations (M): 9.9 Image size: train = 224 x 224, test = 288 x 288 Papers: EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298 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
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