Model card for convnext tiny.in12k ft in1k A ConvNeXt image classification model. Pretrained in timm on ImageNet 12k (a 11821 class subset of full ImageNet 22k) and fine tuned on ImageNet 1k by Ross Wightman. ImageNet 12k training done on TPUs thanks to support of the TRC program. Fine tuning performed on 8x GPU Lambda Labs cloud instances. Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 28.6 GMACs: 4.5 Activations (M): 13.4 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/huggingface/pytorch image models Dataset: ImageNet 1k Pretrain Dataset: ImageNet 12k 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 xxl…
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