Model card for convnext small.fb in22k ft in1k A ConvNeXt image classification model. Pretrained on ImageNet 22k and fine tuned on ImageNet 1k by paper authors. Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 50.2 GMACs: 8.7 Activations (M): 21.6 Image size: train = 224 x 224, test = 288 x 288 Papers: A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 Original: https://github.com/facebookresearch/ConvNeXt Dataset: ImageNet 1k Pretrain Dataset: ImageNet 22k Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison Explore the dataset and runtime metrics of this model in timm model results. All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP. model top1 top5 img size param count gmacs macts samples per sec batch size convnextv2 huge.fcmae ft in22k in1k 512 88.848 98.742 512 660.29 600.81 413.07 28.58 48 convnextv2 huge.fcmae ft in22k in1k 384 88.668 98.738 384 660.29 337.96 232.35 50.56 64 convnext xxlarge.clip laion2b soup ft in1k 88.612 98.704 256 846.47 198.09 124.45 122.45 256 convnext large mlp.clip laion2b soup ft in12k in1k 384 88.312 98.578 384 200.13 101.11 126.74 196.84 256 con…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy