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The model uses compound scaling (uniform scaling of depth, width, and resolution) to balance accuracy and computational efficiency, enabling deployment on resource-constrained devices. 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The timm model includes metadata for input normalization (ImageNet mean/std) and class label mappings to ImageNet-1K, enabling end-to-end inference without manual preprocessing. Safetensors format ensures secure, reproducible weight serialization without pickle vulnerabilities.","intents":["I need to deploy this model on iOS/Android without PyTorch runtime","I want to run inference in a browser using ONNX.js or TensorFlow.js","I need to integrate this into a C++ production service","I'm building a multi-framework inference engine and need standardized model formats"],"best_for":["mobile app developers targeting iOS/Android","web developers building client-side ML features","systems engineers deploying models across heterogeneous hardware","teams standardizing on ONNX for model interoperability"],"limitations":["ONNX export requires manual operator mapping for custom layers — standard EfficientNet-B0 exports cleanly but custom variants may fail","TorchScript export loses some dynamic control flow — not suitable for models with conditional branches","CoreML export is Apple-only and requires additional conversion steps (torch→ONNX→CoreML)","Exported models lose timm-specific utilities (augmentation, feature extraction) — must be reimplemented in target framework","No built-in quantization during export — requires separate post-training quantization step for mobile deployment"],"requires":["PyTorch 1.9+","timm library","ONNX and onnx-simplifier for ONNX export","CoreML Tools 5.0+ for iOS export","TensorFlow 2.x for TF SavedModel export (via ONNX conversion)","Target framework runtime (ONNX Runtime, TensorFlow Lite, Core ML, etc.)"],"input_types":["PyTorch model (timm.create_model('efficientnet_b0.ra_in1k'))","sample input tensor (torch.Tensor with shape [1, 3, 224, 224])"],"output_types":["ONNX model (.onnx file, ~21MB)","TorchScript module (.pt file)","CoreML model (.mlmodel file)","TensorFlow SavedModel (directory with saved_model.pb)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-timm--efficientnet_b0.ra_in1k__cap_4","uri":"capability://safety.moderation.adversarial.robustness.evaluation","name":"adversarial-robustness-evaluation","description":"Assesses model vulnerability to adversarial perturbations (small, imperceptible input changes that fool the classifier) using standard attack methods (FGSM, PGD, C&W). 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Evaluation requires computing gradients with respect to inputs, which timm models support natively through PyTorch's autograd system.","intents":["I need to evaluate if this model is safe for security-critical applications (fraud detection, biometric auth)","I want to benchmark adversarial robustness before and after fine-tuning","I'm building a robust image classifier and need to understand failure modes","I need to compare robustness across different model architectures"],"best_for":["security engineers assessing model vulnerabilities","researchers studying adversarial robustness","teams deploying models in adversarial environments (autonomous vehicles, surveillance)","practitioners implementing adversarial training"],"limitations":["Standard ImageNet training provides no adversarial robustness — model is vulnerable to imperceptible perturbations (ε=8/255)","Adversarial robustness evaluation is computationally expensive — PGD attack with 20 steps requires 20× forward+backward passes 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