{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_siwalu","slug":"siwalu","name":"Siwalu","type":"product","url":"https://siwalusoftware.com","page_url":"https://unfragile.ai/siwalu","categories":["image-generation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_siwalu__cap_0","uri":"capability://image.visual.single.image.animal.breed.classification","name":"single-image animal breed classification","description":"Processes a single photograph through a pre-trained convolutional neural network (likely ResNet or EfficientNet-based architecture) to classify the animal species and specific breed in real-time. The model performs multi-label classification across dozens of animal breeds, returning confidence scores for each predicted breed. Inference is optimized for mobile/web deployment, suggesting model quantization or distillation techniques to reduce latency and memory footprint while maintaining accuracy.","intents":["I need to quickly identify what breed my pet is from a photo","I found an unknown animal and want to know what species and breed it is","I'm building a pet identification feature and need a lightweight inference engine"],"best_for":["Pet owners seeking casual breed identification without veterinary consultation","Mobile app developers needing embedded animal classification","Wildlife enthusiasts documenting species in the field"],"limitations":["No published accuracy metrics or confusion matrices across breed difficulty tiers","Likely struggles with mixed breeds, rare breeds, or animals with non-standard coloring","Single-image inference means no temporal consistency across multiple photos of same animal","Free tier probably uses lower-resolution input processing (224x224 or 256x256) vs premium models"],"requires":["JPEG or PNG image file (typical max 5-10MB)","Internet connection for cloud inference (unless edge model deployed)","Modern browser or mobile OS (iOS 12+, Android 8+)"],"input_types":["image/jpeg","image/png","image/webp"],"output_types":["structured data: {breed: string, confidence: float, species: string, alternatives: [{breed: string, confidence: float}]}"],"categories":["image-visual","classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_siwalu__cap_1","uri":"capability://image.visual.multi.category.animal.species.detection","name":"multi-category animal species detection","description":"Extends beyond single-species classification to detect and classify across multiple animal categories (dogs, cats, birds, reptiles, livestock, etc.) within a single inference pass. Uses a hierarchical classification approach where the model first identifies the broad animal category, then performs breed-specific classification within that category. This architecture reduces model size by avoiding training a single monolithic classifier across all possible breeds.","intents":["I want to identify any type of animal, not just dogs or cats","I need to know both the species and the specific breed in one request","I'm building a general-purpose pet identification app that handles diverse animal types"],"best_for":["General-purpose pet apps serving diverse user bases with mixed pet types","Wildlife documentation platforms needing broad animal coverage","Veterinary clinics handling multiple animal species"],"limitations":["Hierarchical classification adds latency compared to single-species models","Cross-category accuracy likely varies significantly (dogs/cats probably >85%, exotic birds/reptiles likely <70%)","No documented performance breakdown by animal category","Training data bias toward common domestic animals vs rare/exotic species"],"requires":["Image containing at least one identifiable animal","Sufficient lighting and focus for feature extraction"],"input_types":["image/jpeg","image/png"],"output_types":["structured data: {category: string, breed: string, confidence: float, alternatives: [{category: string, breed: string, confidence: float}]}"],"categories":["image-visual","classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_siwalu__cap_2","uri":"capability://tool.use.integration.free.tier.inference.with.rate.limiting","name":"free-tier inference with rate limiting","description":"Provides unlimited free API access to breed identification with server-side rate limiting and potential inference queue management to control computational costs. The free tier likely uses shared GPU/CPU resources with batch processing of requests, meaning individual requests may experience 1-5 second latency during peak hours. Monetization strategy appears to rely on premium features (batch processing, API SLAs, health data integration) rather than blocking free access.","intents":["I want to try animal breed identification without paying upfront","I'm prototyping a pet app and need free inference for MVP validation","I need occasional breed identification without committing to a subscription"],"best_for":["Solo developers and small teams prototyping pet-related applications","Non-technical pet owners wanting zero-friction breed lookup","Students and researchers exploring computer vision applications"],"limitations":["Rate limiting likely enforces 10-100 requests/day per free user (typical for freemium AI tools)","Inference latency higher than premium tier due to shared resource pooling","No SLA or uptime guarantees on free tier","Batch processing not available on free tier, requiring sequential single-image requests","No API key authentication, likely tied to browser session or IP address"],"requires":["No API key or payment method required","Web browser or mobile app with internet connectivity"],"input_types":["image/jpeg","image/png"],"output_types":["structured data: {breed: string, confidence: float}"],"categories":["tool-use-integration","freemium-model"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_siwalu__cap_3","uri":"capability://image.visual.mobile.optimized.inference.pipeline","name":"mobile-optimized inference pipeline","description":"Implements a lightweight inference engine suitable for deployment on mobile devices and low-bandwidth web environments, likely using model quantization (INT8 or FP16), pruning, or knowledge distillation to reduce model size from typical 100-500MB to 10-50MB. The architecture may support both cloud inference (for accuracy) and edge inference (for latency), with intelligent fallback logic. Input preprocessing is optimized for mobile cameras, including automatic orientation correction and aspect ratio handling.","intents":["I need breed identification to work smoothly on my mobile phone without lag","I'm building a mobile app and need lightweight inference that doesn't drain battery","I want to identify animals in the field with minimal data usage"],"best_for":["Mobile app developers targeting iOS and Android platforms","Users in low-bandwidth environments or with limited data plans","Applications requiring sub-2-second inference latency on mobile devices"],"limitations":["Model compression trades accuracy for speed — likely 5-15% accuracy loss vs full-size models","Mobile inference requires device with 2GB+ RAM and modern processor (A11 Bionic or Snapdragon 835+)","Edge inference not available on free tier, likely premium feature","Quantization artifacts may reduce confidence score reliability"],"requires":["iOS 12+ or Android 8+","Device with 2GB+ available RAM","Internet connection for cloud inference (edge model optional)"],"input_types":["image/jpeg","image/png","camera stream (real-time)"],"output_types":["structured data: {breed: string, confidence: float, processingTime: int}"],"categories":["image-visual","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_siwalu__cap_4","uri":"capability://image.visual.confidence.scoring.and.alternative.breed.suggestions","name":"confidence scoring and alternative breed suggestions","description":"Returns not just a single breed prediction but a ranked list of alternative breeds with confidence scores for each, enabling users to disambiguate between similar-looking breeds. The model outputs logits or probability distributions across all breed classes, which are then sorted and filtered to show top-N alternatives (typically 3-5). This approach helps users understand model uncertainty and make informed decisions when the top prediction is ambiguous.","intents":["I want to see multiple possible breeds ranked by likelihood, not just one answer","I need to understand how confident the AI is in its breed identification","I'm comparing my pet against similar breeds to determine the most likely match"],"best_for":["Pet owners with mixed-breed or ambiguous-looking animals","Veterinarians using AI as a diagnostic aid rather than definitive answer","Researchers studying model uncertainty and calibration"],"limitations":["Confidence scores may not be well-calibrated — high confidence doesn't guarantee accuracy","Alternative suggestions only useful if top-5 predictions include correct breed","No explanation of why certain breeds are ranked higher (black-box predictions)","Confidence threshold for filtering alternatives not documented"],"requires":["Model trained to output probability distributions across all breed classes"],"input_types":["image/jpeg","image/png"],"output_types":["structured data: {predictions: [{breed: string, confidence: float, rank: int}], topPrediction: {breed: string, confidence: float}}"],"categories":["image-visual","uncertainty-quantification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_siwalu__cap_5","uri":"capability://image.visual.real.time.camera.feed.breed.detection","name":"real-time camera feed breed detection","description":"Enables continuous breed identification from live camera streams rather than static images, processing video frames at 15-30 FPS with temporal smoothing to reduce jitter between frames. The implementation likely uses frame skipping (processing every Nth frame) and result caching to optimize inference frequency while maintaining responsive UI. Temporal filtering (e.g., exponential moving average of confidence scores) stabilizes predictions across frames, reducing false positives from single-frame artifacts.","intents":["I want to point my phone camera at an animal and see real-time breed identification","I need continuous breed detection while moving around or the animal is moving","I'm building an AR pet identification app with live camera overlay"],"best_for":["Mobile app developers building camera-first pet identification experiences","AR/VR applications overlaying breed information on live video","Field researchers documenting wildlife with continuous identification"],"limitations":["Real-time processing adds 200-500ms latency due to frame buffering and inference queueing","Frame skipping (processing every 3rd-5th frame) reduces detection responsiveness","Battery drain on mobile devices running continuous inference — likely 15-25% battery per hour","Temporal smoothing introduces lag in prediction updates (1-2 second delay before showing new breed)"],"requires":["Device with camera hardware","iOS 12+ or Android 8+","Continuous internet connection or edge model deployment"],"input_types":["camera stream (video/H.264 or video/VP9)","frame rate: 15-30 FPS"],"output_types":["structured data stream: {breed: string, confidence: float, timestamp: int, frameNumber: int}"],"categories":["image-visual","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"low","permissions":["JPEG or PNG image file (typical max 5-10MB)","Internet connection for cloud inference (unless edge model deployed)","Modern browser or mobile OS (iOS 12+, Android 8+)","Image containing at least one identifiable animal","Sufficient lighting and focus for feature extraction","No API key or payment method required","Web browser or mobile app with internet connectivity","iOS 12+ or Android 8+","Device with 2GB+ available RAM","Internet connection for cloud inference (edge model optional)"],"failure_modes":["No published accuracy metrics or confusion matrices across breed difficulty tiers","Likely struggles with mixed breeds, rare breeds, or animals with non-standard coloring","Single-image inference means no temporal consistency across multiple photos of same animal","Free tier probably uses lower-resolution input processing (224x224 or 256x256) vs premium models","Hierarchical classification adds latency compared to single-species models","Cross-category accuracy likely varies significantly (dogs/cats probably >85%, exotic birds/reptiles likely <70%)","No documented performance breakdown by animal category","Training data bias toward common domestic animals vs rare/exotic species","Rate limiting likely enforces 10-100 requests/day per free user (typical for freemium AI tools)","Inference latency higher than premium tier due to shared resource pooling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.096Z","last_scraped_at":"2026-04-05T13:23:42.559Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=siwalu","compare_url":"https://unfragile.ai/compare?artifact=siwalu"}},"signature":"K0yPQoySjtJHYDrd5jD0K64LEmFd5W6K13fqsNzipJNACZhsmuSZ8kxuwPNUUKu5f5eIzKsF5kQ3T/Nwu8icBw==","signedAt":"2026-06-21T21:24:07.732Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/siwalu","artifact":"https://unfragile.ai/siwalu","verify":"https://unfragile.ai/api/v1/verify?slug=siwalu","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"}}