Model card for resnet10t.c3 in1k A ResNet T image classification model. This model features: ReLU activations tiered 3 layer stem of 3x3 convolutions with pooling 2x2 average pool + 1x1 convolution shortcut downsample Trained on ImageNet 1k in timm using recipe template described below. Recipe details: Based on ResNet Strikes Back C recipes SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping). Cosine LR schedule with warmup Model Details Model Type: Image classification / feature backbone Model Stats: Params (M): 5.4 GMACs: 0.7 Activations (M): 1.5 Image size: train = 176 x 176, test = 224 x 224 Papers: ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 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. model img size top1 top5 param count gmacs macts img/sec seresnexta…
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