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The model processes input images through patch-based tokenization and multi-head self-attention layers to extract gender-discriminative features, outputting probability scores for male/female categories. Leverages PyTorch's autograd system for inference and supports batch processing through HuggingFace's transformers pipeline API.","intents":["Classify the gender presentation of individuals in photos for content moderation or demographic analysis","Build automated gender-based filtering or routing systems for image datasets","Integrate gender detection into computer vision pipelines without training custom models","Batch-process large image collections to extract gender labels for downstream ML tasks"],"best_for":["Computer vision engineers building content moderation systems","Data scientists preparing labeled datasets for gender-aware ML models","Teams needing lightweight, pre-trained gender classification without custom training","Researchers studying gender representation in image datasets"],"limitations":["Binary classification only (male/female) — does not support non-binary or gender-neutral predictions","Performance degrades on non-frontal faces, extreme angles, or heavily occluded faces (glasses, masks)","No confidence thresholding built-in — requires post-processing to handle ambiguous predictions","Trained on potentially biased datasets — may exhibit disparate accuracy across demographic groups and skin tones","Inference latency ~100-200ms per image on CPU; requires GPU for real-time batch processing","No explainability features (attention maps, saliency) provided in base model"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.6+","HuggingFace transformers library 4.0+","Pillow or OpenCV for image preprocessing","4GB+ RAM for model loading (8GB+ recommended for batch inference)","GPU optional but strongly recommended for production throughput"],"input_types":["JPEG images","PNG images","RGB image tensors (3, H, W format)","Batch image arrays"],"output_types":["Logits (raw model outputs)","Probability scores (softmax normalized, 0-1 range)","Class labels (string: 'male' or 'female')","Confidence scores per class"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-rizvandwiki--gender-classification__cap_1","uri":"capability://tool.use.integration.huggingface.inference.api.endpoint.deployment.with.automatic.scaling","name":"huggingface inference api endpoint deployment with automatic scaling","description":"Model is hosted on HuggingFace's managed inference infrastructure, accessible via REST API without requiring local GPU hardware. Requests are routed through HuggingFace's load-balanced endpoints with automatic model caching, cold-start handling, and regional server selection (US region specified). The endpoint abstracts PyTorch/ONNX runtime details and handles concurrent request queuing.","intents":["Call the gender classification model from web applications or mobile backends without managing inference servers","Integrate gender classification into serverless architectures without provisioning GPUs","Scale gender classification to handle variable traffic without DevOps overhead","Access the model via simple HTTP POST requests from any programming language"],"best_for":["Startups and small teams without ML infrastructure expertise","Web applications requiring low-latency gender classification on-demand","Prototyping and MVP development with minimal DevOps burden","Teams avoiding GPU procurement and maintenance costs"],"limitations":["Network latency adds 50-200ms per request depending on geographic distance from US region","Rate limiting enforced by HuggingFace (free tier: ~5 requests/minute, paid tiers higher)","Cold-start latency ~2-5 seconds if model is unloaded from memory","No local caching of model weights — all inference depends on external API availability","Inference costs scale with request volume on paid HuggingFace Pro plans","No guarantee of sub-second SLA for free tier; paid tiers offer better latency guarantees"],"requires":["HuggingFace API token (free account sufficient for basic usage)","HTTPS-capable HTTP client (curl, requests library, fetch API)","Network connectivity to huggingface.co","Base64 encoding capability for image transmission in request body"],"input_types":["Base64-encoded image strings","Binary image data in multipart/form-data","Image URLs (if endpoint supports URL-based input)"],"output_types":["JSON object with class labels and confidence scores","HTTP status codes (200 for success, 429 for rate limit, 503 for service unavailable)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-rizvandwiki--gender-classification__cap_2","uri":"capability://data.processing.analysis.batch.image.classification.with.tensor.preprocessing.pipeline","name":"batch image classification with tensor preprocessing pipeline","description":"Supports processing multiple images in a single inference pass through PyTorch's batching mechanism. Images are automatically resized to ViT's expected input dimensions (typically 224x224 or 384x384), normalized using ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), and stacked into a single tensor. The model processes the batch through the ViT encoder in parallel, reducing per-image overhead and improving throughput.","intents":["Process thousands of images efficiently by batching requests to reduce per-image overhead","Extract gender labels for entire image datasets in a single job without sequential API calls","Maximize GPU utilization by processing multiple images in parallel","Reduce total inference time for large-scale gender classification tasks"],"best_for":["Data scientists preparing labeled datasets for gender-aware model training","Content moderation teams processing bulk image uploads","Researchers analyzing gender representation across image corpora","ETL pipelines requiring efficient batch gender classification"],"limitations":["Batch size is memory-constrained — typical GPU (8GB VRAM) supports ~32-64 images per batch","All images in a batch must be resized to same dimensions, losing aspect ratio information","ImageNet normalization assumes natural RGB images — may not work well on medical, infrared, or synthetic images","No built-in handling of corrupted or invalid image files — requires pre-validation","Batch processing introduces latency jitter — last image in batch waits for entire batch to complete"],"requires":["PyTorch 1.9+ with CUDA support (for GPU batching) or CPU fallback","Sufficient GPU VRAM for batch size (8GB minimum for batch_size=32)","Image preprocessing library (torchvision.transforms or PIL)","Numpy for tensor manipulation"],"input_types":["List of image file paths","List of PIL Image objects","Numpy arrays (H, W, 3) in uint8 format","PyTorch tensors (B, 3, H, W)"],"output_types":["Tensor of logits (B, 2)","Tensor of probabilities (B, 2)","List of class labels with confidence scores","Pandas DataFrame with image paths and predictions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-rizvandwiki--gender-classification__cap_3","uri":"capability://safety.moderation.model.weight.distribution.via.safetensors.format.with.integrity.verification","name":"model weight distribution via safetensors format with integrity verification","description":"Model weights are distributed using the safetensors format, a safer alternative to pickle-based PyTorch checkpoints. Safetensors uses a simple JSON header + binary tensor layout, enabling fast deserialization, built-in integrity checking via SHA256 hashing, and protection against arbitrary code execution during model loading. HuggingFace's transformers library automatically detects and loads safetensors files with zero configuration.","intents":["Download and load the gender classification model safely without risk of code injection","Verify model integrity and authenticity before using in production systems","Reduce model loading time compared to pickle-based PyTorch checkpoints","Ensure reproducible model behavior across different environments and Python versions"],"best_for":["Security-conscious teams deploying models in production environments","Organizations with strict supply-chain security requirements","Developers building automated model update pipelines","Teams requiring fast model loading in latency-sensitive applications"],"limitations":["Safetensors format is relatively new — older PyTorch code may not recognize it without library updates","No compression support in safetensors — file sizes identical to uncompressed PyTorch checkpoints","Integrity verification is optional — requires explicit hash checking in application code","Limited debugging capability compared to pickle (no custom unpickling hooks)"],"requires":["HuggingFace transformers 4.25.0+","safetensors library 0.3.0+","PyTorch 1.9+ (for tensor compatibility)","SHA256 hashing capability for integrity verification (built into Python hashlib)"],"input_types":[".safetensors files","HuggingFace model card URLs pointing to safetensors checkpoints"],"output_types":["PyTorch state_dict (dictionary of tensor names to weight tensors)","Loaded model ready for inference"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-rizvandwiki--gender-classification__cap_4","uri":"capability://tool.use.integration.multi.framework.model.compatibility.via.onnx.export.and.tensorflow.conversion","name":"multi-framework model compatibility via onnx export and tensorflow conversion","description":"The model can be exported to ONNX (Open Neural Network Exchange) format for deployment in non-PyTorch environments, and converted to TensorFlow SavedModel format for TensorFlow Lite mobile inference. The export process traces the ViT architecture and converts PyTorch operations to framework-agnostic ONNX ops, enabling deployment on edge devices, mobile phones, and non-Python runtimes (C++, Java, JavaScript).","intents":["Deploy gender classification on mobile devices (iOS, Android) using TensorFlow Lite","Run inference in web browsers using ONNX.js or WebAssembly","Integrate the model into C++ or Java applications without PyTorch dependency","Optimize inference for edge devices with quantization and pruning"],"best_for":["Mobile app developers building on-device gender classification features","Web developers embedding gender classification in browser-based applications","Embedded systems engineers deploying on IoT devices or edge servers","Teams requiring cross-platform model deployment without PyTorch"],"limitations":["ONNX export may lose some PyTorch-specific operations — requires testing to verify numerical equivalence","TensorFlow conversion introduces framework-specific quirks and potential precision loss","Mobile quantization (int8, float16) reduces accuracy by 1-3% depending on quantization method","ONNX.js browser inference is slower than native implementations due to JavaScript overhead","No automatic fallback if ONNX operations are unsupported on target platform","Model size increases slightly after ONNX export due to operation metadata"],"requires":["PyTorch 1.9+ for ONNX export","ONNX library 1.10+","TensorFlow 2.6+ for TensorFlow conversion (optional)","TensorFlow Lite converter for mobile optimization (optional)","ONNX Runtime or TensorFlow Lite Runtime on target deployment platform"],"input_types":["PyTorch model checkpoint (.pt or .safetensors)","HuggingFace model identifier"],"output_types":["ONNX model file (.onnx)","TensorFlow SavedModel directory","TensorFlow Lite model (.tflite)","Quantized model variants (int8, float16)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.6+","HuggingFace transformers library 4.0+","Pillow or OpenCV for image preprocessing","4GB+ RAM for model loading (8GB+ recommended for batch inference)","GPU optional but strongly recommended for production throughput","HuggingFace API token (free account sufficient for basic usage)","HTTPS-capable HTTP client (curl, requests library, fetch API)","Network connectivity to huggingface.co","Base64 encoding capability for image transmission in request body"],"failure_modes":["Binary classification only (male/female) — does not support non-binary or gender-neutral predictions","Performance degrades on non-frontal faces, extreme angles, or heavily occluded faces (glasses, masks)","No confidence thresholding built-in — requires post-processing to handle ambiguous predictions","Trained on potentially biased datasets — may exhibit disparate accuracy across demographic groups and skin tones","Inference latency ~100-200ms per image on CPU; requires GPU for real-time batch processing","No explainability features (attention maps, saliency) provided in base model","Network latency adds 50-200ms per request depending on geographic distance from US region","Rate limiting enforced by HuggingFace (free tier: ~5 requests/minute, paid tiers higher)","Cold-start latency ~2-5 seconds if model is unloaded from memory","No local caching of model weights — all inference depends on external API availability","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7063785027530605,"quality":0.35,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:59.355Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1195698,"model_likes":60}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rizvandwiki--gender-classification","compare_url":"https://unfragile.ai/compare?artifact=rizvandwiki--gender-classification"}},"signature":"hwsyzXxhCOHn6tX5jraZcR6i+0O7pMWFVtK6B8XCPDoOne37CwKe1977B0Z9exnZEv2996kRp4RJbwR5G/BfDg==","signedAt":"2026-06-21T18:46:34.379Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rizvandwiki--gender-classification","artifact":"https://unfragile.ai/rizvandwiki--gender-classification","verify":"https://unfragile.ai/api/v1/verify?slug=rizvandwiki--gender-classification","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}