Model card for resnext26ts.ra2 in1k A ResNeXt image classification model. This model features a tiered 3 layer stem and SiLU activations. Trained on ImageNet 1k by Ross Wightman in timm . This model architecture is implemented using timm 's flexible BYOBNet (Bring Your Own Blocks Network). BYOBNet allows configuration of: block / stage layout stem layout output stride (dilation) activation and norm layers channel and spatial / self attention layers ...and also includes timm features common to many other architectures, including: stochastic depth gradient checkpointing layer wise LR decay per stage feature extraction Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 10.3 GMACs: 2.4 Activations (M): 10.5 Image size: train = 256 x 256, test = 288 x 288 Papers: Aggregated Residual Transformations for Deep Neural Networks: https://arxiv.org/abs/1611.05431 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