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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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This model features: * ReLU activations * 3-layer stem of 3x3 convolutions with pooling * 2x2 average pool + 1x1 convolution shortcut downsample * grouped 3x3 bottleneck convolutions * Squeeze-and-Excitation channel attention Recipe details: * Bag-of-Tricks recipe. * SGD (w/ Nesterov) optimizer * Cosine LR schedule with warmup

Image classification
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A SE-ResNeXt-B image classification model with Squeeze-and-Excitation channel attention. This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample * grouped 3x3 bottleneck convolutions * Squeeze-and-Excitation channel attention Recipe details: * RandAugment `RACM` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in ResNet Strikes Back. * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup

Image classification
Input Image→ Output Not verified
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