{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-dima806--facial_emotions_image_detection","slug":"dima806--facial_emotions_image_detection","name":"facial_emotions_image_detection","type":"model","url":"https://huggingface.co/dima806/facial_emotions_image_detection","page_url":"https://unfragile.ai/dima806--facial_emotions_image_detection","categories":["image-generation"],"tags":["transformers","pytorch","safetensors","vit","image-classification","base_model:google/vit-base-patch16-224-in21k","base_model:finetune:google/vit-base-patch16-224-in21k","license:apache-2.0","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-dima806--facial_emotions_image_detection__cap_0","uri":"capability://image.visual.multi.class.facial.emotion.classification.from.images","name":"multi-class facial emotion classification from images","description":"Classifies facial expressions in images into discrete emotion categories using a Vision Transformer (ViT) architecture fine-tuned on google/vit-base-patch16-224-in21k. The model processes 224x224 pixel image patches through a transformer encoder with 12 attention layers, extracting learned emotion-specific features from facial regions. Inference runs locally via PyTorch or through HuggingFace Inference API endpoints, returning per-emotion confidence scores for each detected face region.","intents":["I need to automatically detect whether people in images are happy, sad, angry, or showing other emotions","I want to analyze sentiment from user-uploaded photos without sending data to external APIs","I need to batch-process hundreds of images to categorize emotional expressions for research or content moderation"],"best_for":["computer vision engineers building emotion-aware applications","content moderation teams analyzing user-generated images","researchers studying facial expression datasets","developers prototyping emotion-responsive UIs or chatbots"],"limitations":["Requires clear, frontal facial views — performance degrades significantly on profile angles, occluded faces, or low-resolution images","No multi-face tracking or temporal consistency across video frames — each image is classified independently","Fixed input size of 224x224 pixels may lose detail in high-resolution images or compress small faces","Emotion categories are discrete classes, not continuous valence/arousal scores","No confidence threshold filtering built-in — all predictions returned regardless of certainty"],"requires":["Python 3.7+","PyTorch 1.9+ or transformers library 4.0+","PIL/Pillow for image preprocessing","GPU recommended for batch inference (CPU inference ~500ms per image)","HuggingFace account for API-based inference (optional, for serverless deployment)"],"input_types":["JPEG/PNG images","numpy arrays (H, W, 3) with pixel values 0-255","PIL Image objects","image URLs (via HuggingFace Inference API)"],"output_types":["JSON with emotion class labels and confidence scores (e.g., {happy: 0.87, sad: 0.08, ...})","logits tensor for downstream processing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dima806--facial_emotions_image_detection__cap_1","uri":"capability://tool.use.integration.local.inference.with.huggingface.transformers.integration","name":"local inference with huggingface transformers integration","description":"Enables on-device model loading and inference through the HuggingFace transformers library using PyTorch backend, with automatic model weight downloading and caching. Supports both CPU and GPU execution paths, with optional quantization (int8/fp16) for memory-constrained environments. Model weights are stored in safetensors format for secure, fast deserialization without arbitrary code execution risks.","intents":["I want to run emotion detection without sending images to cloud APIs for privacy or latency reasons","I need to deploy this model in an offline environment or edge device with limited bandwidth","I want to integrate emotion detection into a Python application with minimal boilerplate code"],"best_for":["privacy-conscious teams processing sensitive facial data","edge/mobile developers deploying on-device inference","researchers experimenting with model behavior locally before production","startups avoiding per-inference API costs at scale"],"limitations":["First inference requires ~500MB download of model weights (one-time, then cached locally)","CPU inference latency ~500-1000ms per image; GPU required for real-time batch processing","No built-in batching optimization — requires manual batch stacking for efficiency","Model size (345MB) may be prohibitive on mobile/embedded devices without quantization","Requires PyTorch installation which adds ~500MB+ to application size"],"requires":["Python 3.7+","transformers library 4.0+","torch 1.9+","~500MB disk space for model weights","2GB+ RAM for inference (4GB+ recommended for batching)"],"input_types":["PIL Image objects","numpy arrays (H, W, 3)","file paths to images","raw image bytes"],"output_types":["torch.Tensor logits","Python dict with class labels and confidence scores"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dima806--facial_emotions_image_detection__cap_2","uri":"capability://tool.use.integration.huggingface.inference.api.endpoint.deployment","name":"huggingface inference api endpoint deployment","description":"Exposes the emotion detection model as a serverless HTTP endpoint via HuggingFace Inference API, handling model serving, auto-scaling, and request batching on HuggingFace infrastructure. Requests are sent as multipart form data or base64-encoded images, with responses returned as JSON containing emotion class probabilities. Supports both free tier (rate-limited, shared hardware) and paid tier (dedicated endpoints with SLA).","intents":["I want to call emotion detection from a web app or mobile app without managing servers","I need a REST API endpoint for emotion detection that auto-scales with traffic","I want to avoid GPU infrastructure costs by using HuggingFace's shared inference hardware"],"best_for":["web developers building emotion-aware features without backend infrastructure","mobile app developers needing cloud-based inference","startups with variable traffic patterns requiring auto-scaling","teams prototyping emotion detection features before committing to dedicated infrastructure"],"limitations":["Free tier has strict rate limits (~5 requests/minute) and cold-start latency (model loads on first request, ~10-30s)","Paid tier requires per-request pricing or monthly subscription (~$9-100/month depending on usage)","Images sent over HTTP — requires HTTPS for production (HuggingFace provides this, but adds network latency)","No built-in request batching from client side — each image is a separate API call","Response latency ~500ms-2s depending on queue depth and hardware availability"],"requires":["HuggingFace account (free or paid)","API token for authentication","HTTP client library (requests, fetch, etc.)","Internet connectivity"],"input_types":["multipart form data with image file","base64-encoded image string in JSON","image URL (HuggingFace downloads and processes)"],"output_types":["JSON response with emotion labels and confidence scores","HTTP status codes for error handling"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dima806--facial_emotions_image_detection__cap_3","uri":"capability://data.processing.analysis.batch.emotion.classification.with.confidence.scoring","name":"batch emotion classification with confidence scoring","description":"Processes multiple images in a single batch operation, returning per-image emotion predictions with confidence scores for each emotion class. Batching is handled at the PyTorch level, stacking images into a single tensor and processing through the ViT encoder in parallel. Confidence scores are softmax-normalized probabilities across all emotion classes, enabling threshold-based filtering or ranking.","intents":["I need to classify emotions in 1000+ images efficiently without making individual API calls","I want to filter images by emotion confidence — only keep predictions above 80% certainty","I need to rank images by emotion strength (e.g., find the 'happiest' images in a dataset)"],"best_for":["data scientists processing emotion datasets for research","content moderation teams batch-analyzing user uploads","media companies tagging image libraries by emotional content","developers building emotion-based image search or recommendation systems"],"limitations":["Batch size limited by GPU memory — typical max 32-64 images per batch on consumer GPUs","No built-in image preprocessing pipeline — requires manual resizing/normalization to 224x224","Confidence scores are softmax probabilities, not calibrated uncertainty estimates — may be overconfident on out-of-distribution faces","No per-face detection — assumes single dominant face per image or averages across multiple faces","Batch processing adds latency overhead (~100-200ms) compared to single-image inference"],"requires":["PyTorch with CUDA support (for GPU batching)","transformers library 4.0+","PIL/numpy for image preprocessing","GPU with 4GB+ VRAM for batch size >16"],"input_types":["list of PIL Image objects","list of file paths","numpy array (B, H, W, 3) where B is batch size"],"output_types":["torch.Tensor logits (B, num_classes)","numpy array of softmax probabilities (B, num_classes)","list of dicts with emotion labels and scores per image"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dima806--facial_emotions_image_detection__cap_4","uri":"capability://data.processing.analysis.emotion.class.label.mapping.and.interpretation","name":"emotion class label mapping and interpretation","description":"Maps raw model output logits to human-readable emotion class labels (e.g., happy, sad, angry, neutral, surprise, fear, disgust) with semantic meaning. The model outputs 7 discrete emotion classes based on standard facial expression taxonomies. Provides confidence scores for each class, enabling multi-label interpretation (e.g., 'slightly happy and slightly surprised') or single-label selection via argmax.","intents":["I need to understand what emotions the model detected in human-readable form","I want to present emotion detection results to end users with clear labels and confidence percentages","I need to map model outputs to my application's emotion taxonomy or custom labels"],"best_for":["UI/UX developers displaying emotion detection results to end users","product managers defining emotion-based features or workflows","researchers analyzing emotion detection outputs for validation","teams integrating emotion detection into chatbots or recommendation systems"],"limitations":["Fixed 7-class taxonomy — cannot detect emotions outside this set (e.g., confusion, contempt)","No confidence calibration — softmax scores may not reflect true prediction uncertainty","Single-label selection (argmax) loses information about mixed emotions","No temporal consistency — consecutive frames may flip between similar emotions","Label semantics are Western-centric (based on Ekman's 6 basic emotions + neutral)"],"requires":["Model output logits or softmax probabilities","emotion class index-to-label mapping (provided by model)"],"input_types":["torch.Tensor logits (num_classes,)","numpy array of softmax probabilities","raw model predictions"],"output_types":["string emotion label (e.g., 'happy')","dict mapping emotion labels to confidence scores","ranked list of emotions by confidence"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or transformers library 4.0+","PIL/Pillow for image preprocessing","GPU recommended for batch inference (CPU inference ~500ms per image)","HuggingFace account for API-based inference (optional, for serverless deployment)","transformers library 4.0+","torch 1.9+","~500MB disk space for model weights","2GB+ RAM for inference (4GB+ recommended for batching)","HuggingFace account (free or paid)"],"failure_modes":["Requires clear, frontal facial views — performance degrades significantly on profile angles, occluded faces, or low-resolution images","No multi-face tracking or temporal consistency across video frames — each image is classified independently","Fixed input size of 224x224 pixels may lose detail in high-resolution images or compress small faces","Emotion categories are discrete classes, not continuous valence/arousal scores","No confidence threshold filtering built-in — all predictions returned regardless of certainty","First inference requires ~500MB download of model weights (one-time, then cached locally)","CPU inference latency ~500-1000ms per image; GPU required for real-time batch processing","No built-in batching optimization — requires manual batch stacking for efficiency","Model size (345MB) may be prohibitive on mobile/embedded devices without quantization","Requires PyTorch installation which adds ~500MB+ to application size","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6739811889456079,"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":"2026-04-22T08:08:25.899Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":604041,"model_likes":120}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dima806--facial_emotions_image_detection","compare_url":"https://unfragile.ai/compare?artifact=dima806--facial_emotions_image_detection"}},"signature":"IdehEoHkULtzXL/9PblU7fmxBPMf8FkjlknOXLl98D0O0bzDM/9pGGcGeoIWlAhooZJjzCE+v2QqDQ2jDOquAg==","signedAt":"2026-06-21T10:46:07.409Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dima806--facial_emotions_image_detection","artifact":"https://unfragile.ai/dima806--facial_emotions_image_detection","verify":"https://unfragile.ai/api/v1/verify?slug=dima806--facial_emotions_image_detection","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"}}