{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512","slug":"xenova--segformer-b0-finetuned-ade-512-512","name":"segformer-b0-finetuned-ade-512-512","type":"finetune","url":"https://huggingface.co/Xenova/segformer-b0-finetuned-ade-512-512","page_url":"https://unfragile.ai/xenova--segformer-b0-finetuned-ade-512-512","categories":["model-training"],"tags":["transformers.js","onnx","segformer","image-segmentation","base_model:nvidia/segformer-b0-finetuned-ade-512-512","base_model:quantized:nvidia/segformer-b0-finetuned-ade-512-512","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512__cap_0","uri":"capability://image.visual.semantic.scene.segmentation.with.transformer.backbone","name":"semantic-scene-segmentation-with-transformer-backbone","description":"Performs pixel-level semantic segmentation using a SegFormer B0 transformer encoder-decoder architecture fine-tuned on ADE20K dataset. The model uses hierarchical self-attention blocks to capture multi-scale contextual information, then applies a lightweight MLP decoder to produce per-pixel class predictions across 150 ADE20K semantic categories. Inference runs via ONNX Runtime for CPU/GPU acceleration without requiring PyTorch.","intents":["segment indoor/outdoor scenes into semantic regions (furniture, walls, sky, etc.) for scene understanding applications","extract specific object classes from images for robotics or autonomous systems that need environmental awareness","generate pixel-accurate masks for scene editing, virtual staging, or augmented reality applications","analyze spatial composition of images by identifying and counting semantic regions"],"best_for":["computer vision engineers building scene understanding pipelines","robotics teams needing real-time environmental segmentation for navigation","web developers deploying ML inference client-side using transformers.js","researchers prototyping scene analysis without GPU infrastructure"],"limitations":["Fixed input resolution of 512×512 pixels — requires resizing/padding which may distort aspect ratios or lose fine details in high-resolution images","Trained exclusively on ADE20K indoor/outdoor scenes — performance degrades on domain-specific imagery (medical, satellite, microscopy)","Inference latency ~200-400ms on CPU, ~50-100ms on GPU — not suitable for real-time video at 30+ fps without batching optimization","No built-in confidence scoring per pixel — cannot distinguish between high-confidence and uncertain predictions","Quantized ONNX version uses 8-bit integer precision, introducing ~1-3% accuracy loss vs float32 original"],"requires":["transformers.js library (for browser/Node.js inference) or ONNX Runtime (for Python/C++)","minimum 512MB RAM for model weights (quantized) or 2GB for float32","input image in RGB format (3-channel, uint8 or float32 normalized to [0,1])","Node.js 14+ or Python 3.7+ depending on runtime"],"input_types":["image (RGB, 512×512 or resizable to 512×512)","image batch (multiple images for batch inference)"],"output_types":["segmentation mask (HxWx150 logits or class indices)","class probability map (per-pixel softmax output)","colored segmentation visualization (optional post-processing)"],"categories":["image-visual","deep-learning-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512__cap_1","uri":"capability://data.processing.analysis.ade20k.scene.class.prediction.with.150.categories","name":"ade20k-scene-class-prediction-with-150-categories","description":"Decodes segmentation logits into 150 semantic class labels from the ADE20K ontology (walls, floors, furniture, vegetation, sky, etc.). The decoder applies argmax over the 150-dimensional class dimension per pixel, optionally with confidence thresholding or softmax probability extraction. Supports both single-image and batch inference with vectorized operations.","intents":["identify what objects/surfaces are present in a scene for inventory or content moderation","filter segmentation masks by confidence threshold to reduce false positives in downstream tasks","generate human-readable class names and statistics (e.g., '45% wall, 30% floor, 15% furniture')","map predicted classes to application-specific categories (e.g., 'navigable' vs 'obstacle')"],"best_for":["scene understanding pipelines that need semantic labels beyond raw pixel masks","accessibility applications describing image content to visually impaired users","interior design or real estate tools analyzing room composition","robotics systems mapping environments into navigable/non-navigable regions"],"limitations":["Fixed to 150 ADE20K classes — cannot predict custom classes without retraining or fine-tuning","Class imbalance in ADE20K (rare classes like 'escalator' have <0.1% training data) causes poor recall on uncommon objects","No hierarchical class relationships — treats 'chair' and 'table' as equally distant despite both being furniture","Softmax probabilities are not calibrated — high confidence does not guarantee correctness, especially on out-of-distribution images"],"requires":["segmentation logits output from SegFormer B0 model (150-dimensional per pixel)","ADE20K class label mapping (provided in model card or transformers.js library)","optional: confidence threshold value (0.0-1.0) for filtering low-confidence predictions"],"input_types":["segmentation logits (HxWx150 float tensor)","segmentation probabilities (HxWx150 softmax output)"],"output_types":["class indices (HxW integer tensor, 0-149)","class names (HxW string array)","confidence scores (HxW float array, 0.0-1.0)","class statistics (dictionary of class→pixel_count)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512__cap_2","uri":"capability://tool.use.integration.browser.native.inference.via.onnx.runtime","name":"browser-native-inference-via-onnx-runtime","description":"Executes the quantized SegFormer model directly in browser or Node.js using ONNX Runtime WebAssembly backend, eliminating server-side inference dependencies. The model is pre-converted to ONNX format and quantized to 8-bit integers, reducing size from ~60MB (float32) to ~15MB. Transformers.js library provides a high-level API wrapping ONNX Runtime with automatic model downloading and caching.","intents":["deploy segmentation without backend infrastructure or API costs","process sensitive images client-side without sending data to external servers","enable offline-first applications that work without internet connectivity","reduce latency for interactive applications by avoiding network round-trips"],"best_for":["web developers building privacy-first computer vision applications","teams with limited backend infrastructure or budget for inference APIs","edge devices (Raspberry Pi, mobile browsers) with limited connectivity","applications processing sensitive/confidential images (medical, security)"],"limitations":["ONNX Runtime WebAssembly is single-threaded — cannot parallelize inference across CPU cores, limiting throughput to ~1-2 images/sec on typical laptops","Browser memory constraints (typically 512MB-2GB available) limit batch size to 1-4 images before OOM errors","First inference is slow (~2-5 seconds) due to WASM module initialization and model loading from cache","GPU acceleration via WebGPU is experimental and not widely supported across browsers (Chrome 113+, Edge 113+)","Model download on first use adds 15-50MB to initial page load (depending on cache and network)"],"requires":["modern browser with WebAssembly support (Chrome 57+, Firefox 52+, Safari 14.1+, Edge 79+)","transformers.js library (npm install @xenova/transformers)","Node.js 14+ for server-side inference","~50MB free disk space for model cache (browser IndexedDB or Node.js filesystem)"],"input_types":["image file (JPEG, PNG, WebP)","image URL (with CORS headers)","canvas element (browser)","Tensor (pre-processed image data)"],"output_types":["segmentation mask (Tensor)","class indices (Uint32Array)","visualization (canvas or image blob)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512__cap_3","uri":"capability://image.visual.multi.scale.hierarchical.feature.extraction","name":"multi-scale-hierarchical-feature-extraction","description":"SegFormer B0 encoder uses hierarchical transformer blocks with overlapping patch embeddings to extract features at 4 scales (1/4, 1/8, 1/16, 1/32 of input resolution). Each scale captures different receptive fields — lower scales detect fine details (edges, small objects), higher scales capture global context (scene layout, large regions). The decoder fuses these multi-scale features via upsampling and concatenation before final classification.","intents":["improve segmentation accuracy on objects of varying sizes (small furniture vs large walls)","enable coarse-to-fine refinement strategies for iterative segmentation","extract intermediate feature representations for transfer learning or visualization","balance local detail preservation with global context understanding"],"best_for":["scene understanding tasks requiring both fine-grained and global context","transfer learning scenarios where intermediate features are repurposed for custom tasks","interpretability research analyzing what features the model learns at each scale","applications needing hierarchical segmentation (coarse regions → fine boundaries)"],"limitations":["Multi-scale processing increases computational cost — each scale requires separate transformer blocks, adding ~30% latency vs single-scale models","Feature fusion at decoder requires careful alignment — misaligned scales can introduce artifacts or reduce accuracy","Intermediate features are not directly accessible via transformers.js API — requires custom ONNX model export to extract them","Memory footprint grows with number of scales — storing 4-scale features requires ~2-3x more GPU memory than single-scale"],"requires":["understanding of transformer architecture and multi-scale feature fusion","ONNX model with intermediate layer outputs exposed (not available in default quantized version)","sufficient GPU/CPU memory for storing multi-scale feature maps"],"input_types":["image (512×512 RGB)"],"output_types":["multi-scale feature maps (4 tensors at different resolutions)","final segmentation logits (512×512×150)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-xenova--segformer-b0-finetuned-ade-512-512__cap_4","uri":"capability://image.visual.quantized.model.inference.with.8.bit.precision","name":"quantized-model-inference-with-8-bit-precision","description":"The model is pre-quantized to 8-bit integer precision using post-training quantization, reducing model size from ~60MB (float32) to ~15MB while maintaining inference speed on CPU/GPU. Quantization maps float32 weights and activations to int8 range using learned scale factors per layer. ONNX Runtime automatically dequantizes to float32 during computation, introducing minimal accuracy loss (~1-3%) while dramatically reducing memory bandwidth and model download size.","intents":["deploy models on memory-constrained devices (mobile, edge, IoT) where float32 is infeasible","reduce model download time and storage footprint for web applications","accelerate inference on CPUs that have native int8 operations (x86 AVX2, ARM NEON)","enable batch inference on devices with limited VRAM"],"best_for":["mobile and edge device deployment (phones, tablets, Raspberry Pi)","web applications where model size directly impacts page load time","resource-constrained environments (IoT, embedded systems)","cost-sensitive cloud deployments where bandwidth is metered"],"limitations":["Quantization introduces ~1-3% accuracy loss on ADE20K validation set — rare classes may see higher degradation","Quantized models are not differentiable — cannot fine-tune without converting back to float32","Quantization parameters are baked into the model — cannot dynamically adjust precision for different accuracy/speed trade-offs","Not all ONNX operations support int8 execution — some layers may fall back to float32, reducing speedup","Accuracy loss is non-uniform across classes — common classes (wall, floor) maintain high accuracy, rare classes (escalator, fountain) may degrade significantly"],"requires":["ONNX Runtime with quantization support (version 1.10+)","understanding that int8 precision is a fixed trade-off — cannot be adjusted post-deployment","validation on target domain to measure actual accuracy loss (may differ from ADE20K benchmark)"],"input_types":["image (512×512 RGB, float32 or uint8)"],"output_types":["segmentation logits (float32, dequantized by runtime)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["transformers.js 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