Model card for test resnet.r160 in1k A very small test ResNet image classification model for testing and sanity checks. Trained on ImageNet 1k by Ross Wightman. Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 0.5 GMACs: 0.1 Activations (M): 0.6 Image size: 160 x 160 Dataset: ImageNet 1k Papers: PyTorch Image Models: https://github.com/huggingface/pytorch image models Original: https://github.com/huggingface/pytorch image models Model Usage Image Classification Feature Map Extraction Image Embeddings Model Comparison By Top 1 model img size top1 top5 param count test convnext3.r160 in1k 192 54.558 79.356 0.47 test convnext2.r160 in1k 192 53.62 78.636 0.48 test convnext2.r160 in1k 160 53.51 78.526 0.48 test convnext3.r160 in1k 160 53.328 78.318 0.47 test convnext.r160 in1k 192 48.532 74.944 0.27 test nfnet.r160 in1k 192 48.298 73.446 0.38 test convnext.r160 in1k 160 47.764 74.152 0.27 test nfnet.r160 in1k 160 47.616 72.898 0.38 test efficientnet.r160 in1k 192 47.164 71.706 0.36 test efficientnet evos.r160 in1k 192 46.924 71.53 0.36 test byobnet.r160 in1k 192 46.688 71.668 0.46 test efficientnet evos.r160 in1k 160 46.498 71.006 0.36 test effi…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy