{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512","slug":"nvidia--segformer-b0-finetuned-ade-512-512","name":"segformer-b0-finetuned-ade-512-512","type":"finetune","url":"https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512","page_url":"https://unfragile.ai/nvidia--segformer-b0-finetuned-ade-512-512","categories":["model-training"],"tags":["transformers","pytorch","tf","safetensors","segformer","vision","image-segmentation","dataset:scene_parse_150","arxiv:2105.15203","license:other","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-nvidia--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 lightweight SegFormer-B0 transformer encoder-decoder architecture trained on ADE20K scene parsing dataset. The model uses hierarchical shifted windows and overlapping patch merging to capture multi-scale contextual information across 150 scene categories, processing 512x512 RGB images through a pure transformer backbone (no convolutions) to generate dense per-pixel class predictions with spatial coherence.","intents":["segment indoor and outdoor scenes into 150 semantic categories for scene understanding applications","extract pixel-level masks for specific objects and regions in photographs or video frames","build computer vision pipelines that require understanding scene composition and spatial layout","deploy lightweight semantic segmentation models on resource-constrained devices or edge hardware"],"best_for":["computer vision engineers building scene understanding systems","robotics teams implementing visual perception for navigation and manipulation","mobile/edge AI developers needing sub-100MB segmentation models","researchers prototyping scene parsing applications without large GPU infrastructure"],"limitations":["Fixed input resolution of 512x512 — requires resizing/padding images to exact dimensions, causing distortion on non-square aspect ratios","Trained exclusively on indoor/outdoor scene data (ADE20K) — poor generalization to domain-specific imagery like medical, satellite, or industrial scenes","Inference latency ~100-150ms on CPU, ~20-30ms on single GPU — not suitable for real-time video at 30+ fps without batching or quantization","Memory footprint ~13MB model weights — requires 2-4GB RAM during inference due to activation tensors for 512x512 resolution","No built-in uncertainty quantification or confidence scores per pixel — cannot distinguish between confident and uncertain predictions"],"requires":["PyTorch 1.9+ or TensorFlow 2.6+ (model available in both frameworks via transformers library)","transformers library version 4.21.0+ (for SegFormer model class and tokenizer)","PIL/Pillow for image loading and preprocessing","CUDA 11.0+ for GPU acceleration (optional but recommended for inference speed)","Minimum 2GB RAM for inference, 8GB+ recommended for batch processing"],"input_types":["RGB images (3-channel, uint8 or float32)","image files (JPEG, PNG, BMP, TIFF)","numpy arrays with shape (H, W, 3) or batched (B, H, W, 3)"],"output_types":["dense segmentation masks (H, W) with integer class IDs (0-149)","logits tensor (H, W, 150) for per-pixel class probabilities","colored segmentation visualizations (H, W, 3) with class-specific color palettes"],"categories":["image-visual","computer-vision","scene-understanding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512__cap_1","uri":"capability://tool.use.integration.multi.framework.model.loading.with.safetensors.support","name":"multi-framework-model-loading-with-safetensors-support","description":"Loads pre-trained SegFormer-B0 weights from HuggingFace Hub in multiple serialization formats (PyTorch .pt, TensorFlow SavedModel, and SafeTensors .safetensors) with automatic framework detection and conversion. 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The inference pipeline accepts batches of arbitrary aspect ratios, applies center-crop or letterbox padding strategies, and outputs aligned segmentation masks with optional shape metadata for post-processing and reverse-transformation to original image coordinates.","intents":["process multiple images efficiently in a single forward pass to maximize GPU utilization","handle real-world image datasets with varying dimensions without manual preprocessing","maintain correspondence between output masks and original image coordinates for downstream tasks","optimize throughput in production inference servers handling heterogeneous image inputs"],"best_for":["production ML engineers optimizing inference throughput on GPU clusters","data pipeline teams processing large image datasets with variable dimensions","computer vision teams building batch processing services for web applications","researchers evaluating model performance across diverse image resolutions"],"limitations":["Batch size limited by GPU VRAM — typical maximum 32-64 images on 8GB GPU, 128-256 on 24GB GPU","Padding strategy (letterbox vs center-crop) affects segmentation accuracy at image borders — requires careful tuning per use case","No built-in dynamic batching — batch size must be predetermined, limiting adaptive load balancing","Reverse transformation from 512x512 mask to original coordinates requires storing shape metadata — adds complexity to output handling"],"requires":["PyTorch or TensorFlow with CUDA support for batch processing","sufficient GPU VRAM for batch size (minimum 2GB for batch_size=1, scales linearly)","image preprocessing library (torchvision.transforms or tf.image) for batch normalization","numpy for shape metadata tracking and post-processing"],"input_types":["batched numpy arrays (B, H, W, 3) with variable H, W across batch","list of PIL Images with different dimensions","tensor batches with automatic padding to max dimensions in batch"],"output_types":["batched segmentation masks (B, 512, 512) with class IDs","batched logits (B, 512, 512, 150) for confidence scores","shape metadata dict mapping batch indices to original image dimensions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.scene.datasets","name":"fine-tuning-on-custom-scene-datasets","description":"Provides a pre-trained encoder-decoder backbone that can be fine-tuned on custom scene segmentation datasets using standard supervised learning with cross-entropy loss. The model supports transfer learning with frozen encoder stages and trainable decoder, learning rate scheduling, and gradient accumulation for effective training on limited GPU memory, leveraging the 150-class ADE20K pre-training as initialization for faster convergence on downstream tasks.","intents":["adapt the model to domain-specific scene segmentation tasks (medical imaging, satellite imagery, industrial scenes) with limited labeled data","fine-tune on custom datasets with different class counts and spatial distributions than ADE20K","implement progressive unfreezing strategies to balance pre-trained knowledge with task-specific learning","reduce training time and data requirements by leveraging ADE20K pre-training"],"best_for":["computer vision teams adapting the model to proprietary or specialized scene datasets","researchers exploring transfer learning effectiveness for scene segmentation","practitioners with 100-10K labeled images seeking to build domain-specific models","teams with limited GPU resources (8-16GB VRAM) needing efficient fine-tuning"],"limitations":["Class mismatch between ADE20K (150 classes) and custom datasets requires retraining final classification head — cannot directly use pre-trained decoder for different class counts","Fine-tuning on small datasets (<1K images) risks overfitting despite pre-training — requires careful regularization (dropout, weight decay, early stopping)","No built-in domain adaptation or class imbalance handling — requires manual loss weighting or data augmentation for imbalanced datasets","Training requires 8GB+ GPU VRAM for batch_size=4 at 512x512 resolution — prohibitive for edge devices or CPU-only environments"],"requires":["PyTorch 1.9+ with CUDA support (TensorFlow fine-tuning less documented)","transformers library 4.21.0+ with training utilities","custom dataset with pixel-level annotations (semantic masks) in standard formats (PNG, TIFF)","8GB+ GPU VRAM for gradient accumulation-based training","training framework (PyTorch Lightning, HuggingFace Trainer, or custom training loop)"],"input_types":["RGB images (512x512 or arbitrary size with resizing)","semantic segmentation masks (H, W) with integer class IDs (0 to num_classes-1)","dataset splits (train/val/test) in standard formats (ImageFolder, COCO, custom loaders)"],"output_types":["fine-tuned model checkpoint with updated decoder weights","training metrics (loss curves, mIoU, per-class accuracy) for validation","inference-ready model for deployment on custom task"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512__cap_4","uri":"capability://image.visual.ade20k.scene.category.prediction.with.class.mapping","name":"ade20k-scene-category-prediction-with-class-mapping","description":"Outputs segmentation predictions mapped to 150 ADE20K scene categories including furniture, building parts, vegetation, sky, and human-made objects. The model provides per-pixel class IDs (0-149) that can be converted to human-readable labels, RGB color visualizations, and hierarchical category groupings (e.g., 'wall' → 'building', 'tree' → 'vegetation') using the official ADE20K class taxonomy and color palette for interpretable scene understanding.","intents":["identify and localize specific scene components (walls, doors, windows, furniture) in indoor photographs","generate human-readable scene descriptions from segmentation masks using class labels","create colored segmentation visualizations for annotation review and quality assurance","build hierarchical scene understanding by grouping related ADE20K classes into semantic categories"],"best_for":["computer vision teams building scene understanding applications for robotics or AR/VR","annotation and QA teams reviewing segmentation results with visual feedback","researchers analyzing scene composition and spatial relationships in image datasets","developers building user-facing applications requiring interpretable segmentation outputs"],"limitations":["150-class taxonomy is fixed and cannot be extended without retraining — custom scene categories require manual mapping or separate fine-tuning","Class imbalance in ADE20K (common classes like 'wall', 'sky' dominate; rare classes like 'escalator' appear <100 times) causes poor performance on underrepresented categories","Hierarchical grouping requires manual definition — no built-in semantic hierarchy beyond flat 150-class list","Color palette for visualization is arbitrary and may not match domain-specific conventions (e.g., medical imaging color schemes)"],"requires":["ADE20K class mapping file (available in transformers library or HuggingFace Hub)","color palette definition (standard RGB triplets for 150 classes)","numpy for class ID to label conversion","PIL/matplotlib for visualization rendering"],"input_types":["segmentation mask tensor (H, W) with integer class IDs (0-149)","logits tensor (H, W, 150) for confidence-based filtering"],"output_types":["class label strings (e.g., 'wall', 'floor', 'ceiling') per pixel","colored segmentation visualization (H, W, 3) with class-specific colors","hierarchical category groupings (e.g., 'building_part', 'furniture', 'vegetation')","per-class pixel counts and coverage statistics"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512__cap_5","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization-and-model-compression-for-edge-deployment","description":"Supports post-training quantization (INT8, FP16) and knowledge distillation to reduce model size from 13MB to 3-6MB and inference latency by 2-4x for deployment on mobile and edge devices. The model can be quantized using PyTorch quantization APIs or ONNX quantization tools, with optional layer-wise quantization awareness for maintaining accuracy on sensitive layers (attention mechanisms) while aggressively quantizing less critical components.","intents":["deploy semantic segmentation on mobile devices (iOS, Android) with <50MB app size overhead","reduce inference latency from 100ms to 25-50ms on edge devices for near-real-time processing","optimize power consumption for battery-constrained devices (drones, IoT cameras)","run segmentation on embedded systems (Raspberry Pi, Jetson Nano) with limited VRAM"],"best_for":["mobile app developers integrating on-device scene understanding","edge AI teams deploying models on resource-constrained hardware","IoT and robotics teams optimizing power and latency budgets","practitioners building privacy-preserving computer vision (no cloud inference)"],"limitations":["INT8 quantization typically causes 2-5% mIoU drop on ADE20K — requires fine-tuning on quantization-aware training (QAT) to recover accuracy","ONNX export and mobile framework conversion (CoreML, TensorFlow Lite) require manual pipeline setup — no one-click mobile deployment","Quantized models lose gradient information — cannot be further fine-tuned without full-precision retraining","Edge device inference speed depends heavily on hardware (Snapdragon vs Apple Neural Engine vs Qualcomm) — no universal latency guarantees"],"requires":["PyTorch 1.9+ with quantization support OR ONNX Runtime with quantization tools","mobile framework SDKs (CoreML Tools for iOS, TensorFlow Lite for Android)","target hardware specifications (RAM, compute capability) for optimization profiling","optional: quantization-aware training framework (PyTorch QAT or TensorFlow QAT) for accuracy recovery"],"input_types":["full-precision PyTorch model checkpoint","ONNX model representation","calibration dataset (100-1000 representative images) for post-training quantization"],"output_types":["INT8 quantized model (3-6MB)","FP16 half-precision model (6-7MB)","ONNX quantized model for cross-platform deployment","CoreML or TensorFlow Lite model for mobile frameworks","quantization metrics (accuracy drop, latency improvement, size reduction)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-nvidia--segformer-b0-finetuned-ade-512-512__cap_6","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.versioning","name":"huggingface-hub-integration-with-model-versioning","description":"Integrates with HuggingFace Hub for automatic model downloading, caching, and version management with support for git-based revision tracking and branch switching. The model can be loaded with specific commit hashes or tags (e.g., 'v1.0', 'main', 'experimental') to ensure reproducibility, and supports automatic cache management with configurable storage locations and cache invalidation strategies for CI/CD pipelines and production deployments.","intents":["load specific model versions in production to ensure reproducibility across deployments","manage model updates and rollbacks using git-based versioning without manual checkpoint management","cache models locally for offline inference or air-gapped environments","integrate model loading into CI/CD pipelines with version pinning and automated testing"],"best_for":["ML engineers managing production model deployments with version control requirements","teams implementing MLOps pipelines with model registry and versioning","researchers ensuring reproducibility across experiments with specific model snapshots","developers building offline-capable applications with pre-cached 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'v1.0')","cache directory path for custom storage location"],"output_types":["model checkpoint loaded from cache or Hub","metadata dict with model info (revision, download URL, cache path)","version information for logging and reproducibility tracking"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.6+ (model available in both frameworks via transformers library)","transformers library version 4.21.0+ (for SegFormer model class and tokenizer)","PIL/Pillow for image loading and preprocessing","CUDA 11.0+ for GPU acceleration (optional but recommended for inference speed)","Minimum 2GB RAM for inference, 8GB+ recommended for batch processing","transformers library 4.21.0+ with safetensors extra (pip install transformers[safetensors])","PyTorch 1.9+ OR TensorFlow 2.6+ (depending on target framework)","huggingface-hub 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