{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-zhengpeng7--birefnet","slug":"zhengpeng7--birefnet","name":"BiRefNet","type":"model","url":"https://huggingface.co/ZhengPeng7/BiRefNet","page_url":"https://unfragile.ai/zhengpeng7--birefnet","categories":["image-generation"],"tags":["birefnet","safetensors","image-segmentation","background-removal","mask-generation","Dichotomous Image Segmentation","Camouflaged Object Detection","Salient Object Detection","pytorch_model_hub_mixin","model_hub_mixin","transformers","custom_code","arxiv:2401.03407","license:mit","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-zhengpeng7--birefnet__cap_0","uri":"capability://image.visual.dichotomous.image.segmentation.with.boundary.aware.refinement","name":"dichotomous image segmentation with boundary-aware refinement","description":"Performs pixel-level binary segmentation using a bidirectional refinement architecture that iteratively refines object boundaries through multi-scale feature fusion. The model uses a two-stream encoder-decoder design with explicit boundary detection pathways, enabling precise separation of foreground objects from backgrounds even in ambiguous regions. BiRefNet achieves this through learnable refinement modules that progressively sharpen mask edges by combining coarse semantic predictions with fine-grained boundary cues across multiple resolution levels.","intents":["I need to extract clean object masks from images with precise boundary delineation for downstream vision tasks","I want to remove backgrounds from photos while preserving fine details like hair, fur, or transparent regions","I need to segment objects in challenging scenarios where foreground-background contrast is low or ambiguous"],"best_for":["computer vision engineers building image processing pipelines","product teams implementing background removal or object isolation features","researchers working on salient object detection or camouflaged object detection benchmarks"],"limitations":["Inference latency increases with image resolution; typical 1024x1024 images require 200-500ms on consumer GPUs","Performance degrades on extremely small objects (<5% of image area) due to receptive field constraints","Requires GPU memory proportional to input resolution; 4GB VRAM minimum for batch processing at 1024x1024","Binary segmentation only — does not support multi-class instance segmentation or panoptic segmentation"],"requires":["PyTorch 1.9+","torchvision for image preprocessing utilities","CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","transformers library 4.25+ for model loading via HuggingFace Hub","PIL/Pillow for image I/O operations"],"input_types":["RGB images (3-channel, uint8 or float32)","Images of arbitrary resolution (tested up to 4K)","Batch inputs via tensor stacking"],"output_types":["Binary segmentation masks (single-channel, float32 in [0,1] range)","Probability maps for soft segmentation","Optionally: upsampled masks matching input resolution"],"categories":["image-visual","semantic-segmentation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_1","uri":"capability://image.visual.camouflaged.object.detection.via.adversarial.feature.learning","name":"camouflaged object detection via adversarial feature learning","description":"Detects objects that visually blend with their backgrounds through learned feature representations that capture subtle texture and color discontinuities. The model employs adversarial training principles where the segmentation head learns to distinguish objects even when foreground-background appearance similarity is high, using contrastive loss functions that push camouflaged object features away from background features in embedding space. This capability leverages the bidirectional refinement architecture to iteratively enhance detection of low-contrast boundaries.","intents":["I need to find and segment animals or objects that are camouflaged or have low contrast against their background","I want to detect objects in challenging natural scenes where traditional contrast-based methods fail","I need to benchmark my model against camouflaged object detection datasets (COD10K, CAMO, etc.)"],"best_for":["wildlife and ecological imaging applications","medical imaging teams detecting low-contrast lesions or anatomical structures","researchers evaluating adversarial robustness of vision models"],"limitations":["Requires training on camouflaged object datasets; zero-shot performance on unseen camouflage patterns is limited","Computational cost is higher than standard segmentation due to adversarial loss computation during inference","Performance depends heavily on training data diversity; models may overfit to specific camouflage types in training set","No explicit handling of multiple camouflaged objects in same image — treats scene as binary foreground/background"],"requires":["PyTorch 1.9+ with autograd enabled","Training data from camouflaged object detection benchmarks (COD10K, CAMO, or similar)","GPU with 6GB+ VRAM for training; 2GB+ for inference","transformers library 4.25+"],"input_types":["RGB images with camouflaged objects","Images from natural or synthetic camouflage scenarios","Batch processing of variable-resolution images"],"output_types":["Binary segmentation masks highlighting camouflaged objects","Confidence scores per pixel indicating camouflage detection confidence","Feature embeddings for downstream analysis"],"categories":["image-visual","object-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_2","uri":"capability://image.visual.salient.object.detection.with.multi.scale.attention.fusion","name":"salient object detection with multi-scale attention fusion","description":"Identifies visually prominent or semantically important objects in images through a multi-scale attention mechanism that weights features based on their relevance to object saliency. The model processes input images at multiple resolution levels, computing attention maps at each scale that highlight regions likely to contain salient objects, then fuses these attention-weighted features through the bidirectional refinement pathway. This enables detection of salient objects regardless of their size or position in the image.","intents":["I need to identify the most visually prominent objects in an image for thumbnail generation or content-aware cropping","I want to detect salient regions for attention-based image compression or progressive loading","I need to evaluate saliency detection performance on standard benchmarks (SOD, ECSSD, DUT-RGBD)"],"best_for":["content management systems implementing smart image cropping or thumbnail generation","mobile app developers optimizing image delivery with saliency-aware compression","computer vision researchers benchmarking attention mechanisms"],"limitations":["Saliency is subjective and dataset-dependent; model performance varies significantly across different saliency definitions","Multi-scale processing increases memory footprint by 2-3x compared to single-scale inference","Attention mechanisms add ~100-150ms latency per image due to feature map computation at multiple scales","Does not distinguish between multiple salient objects — produces single unified saliency map"],"requires":["PyTorch 1.9+","transformers 4.25+","CUDA 11.0+ for GPU acceleration (multi-scale processing is GPU-intensive)","Minimum 4GB VRAM for batch processing"],"input_types":["RGB images of arbitrary resolution","Images from salient object detection benchmarks","Batch inputs with variable dimensions"],"output_types":["Saliency maps (single-channel float32, [0,1] range)","Attention weight maps at multiple scales","Binary salient object masks (thresholded saliency maps)"],"categories":["image-visual","attention-mechanisms"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_3","uri":"capability://image.visual.real.time.background.removal.with.gpu.acceleration","name":"real-time background removal with gpu acceleration","description":"Removes image backgrounds by generating precise foreground masks at interactive speeds through GPU-accelerated inference of the BiRefNet segmentation model. The capability leverages PyTorch's CUDA kernels and optimized tensor operations to achieve sub-second inference on consumer GPUs, enabling real-time video processing or interactive image editing applications. Masks are generated as float32 tensors that can be directly applied as alpha channels or used for compositing.","intents":["I need to remove backgrounds from images in real-time for video conferencing or streaming applications","I want to build an interactive image editing tool where users see background removal results instantly","I need to batch-process thousands of images efficiently for e-commerce product photography"],"best_for":["video conferencing platform developers implementing virtual backgrounds","e-commerce teams automating product image processing at scale","mobile app developers building image editing features (via ONNX export)"],"limitations":["Real-time performance requires GPU; CPU inference is 10-20x slower and unsuitable for interactive use","Memory usage scales with image resolution; 4K images require 4GB+ VRAM","Batch processing throughput is limited by GPU memory; typical batch size is 1-4 images at 1024x1024","No built-in handling of video temporal consistency; frame-by-frame processing may produce flickering artifacts"],"requires":["NVIDIA GPU with CUDA compute capability 3.5+ (or AMD GPU with ROCm support)","CUDA 11.0+ and cuDNN 8.0+","PyTorch compiled with CUDA support","transformers 4.25+","Minimum 2GB VRAM for single-image inference, 4GB+ for batch processing"],"input_types":["RGB images (PIL Image, numpy array, or torch tensor)","Video frames (as individual images or tensor batches)","Images of arbitrary resolution (tested up to 4K)"],"output_types":["Binary or soft alpha masks (float32, [0,1] range)","Composited images with transparent or custom backgrounds","Batch mask tensors for video processing"],"categories":["image-visual","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_4","uri":"capability://tool.use.integration.model.hub.integration.with.huggingface.transformers","name":"model hub integration with huggingface transformers","description":"Provides seamless integration with HuggingFace's model hub ecosystem through the pytorch_model_hub_mixin and model_hub_mixin classes, enabling one-line model loading, automatic weight downloading, and compatibility with the transformers library's inference APIs. The model is distributed as safetensors format (safer than pickle) and includes custom code for preprocessing and postprocessing, allowing users to load and run the model without manual architecture definition or weight file management.","intents":["I want to load BiRefNet in my Python script with a single line of code without manually downloading weights","I need to integrate BiRefNet into a transformers-based pipeline for batch processing or fine-tuning","I want to ensure model weights are loaded safely from HuggingFace Hub without executing untrusted pickle code"],"best_for":["Python developers building computer vision applications with minimal setup overhead","ML engineers integrating multiple models from HuggingFace Hub into unified pipelines","teams prioritizing security by using safetensors format instead of pickle-based model files"],"limitations":["Requires internet connection for initial model download (weights are cached locally after first download)","Custom code execution is required for preprocessing/postprocessing; users must trust the model repository","Model hub integration adds ~1-2 second overhead for first-time loading due to weight downloading and caching","No built-in support for quantization or model compression; full precision weights are downloaded"],"requires":["Python 3.7+","transformers 4.25+","PyTorch 1.9+","huggingface_hub library for model downloading and caching","Internet connection for initial model download"],"input_types":["Model identifier string (e.g., 'ZhengPeng7/BiRefNet')","Optional: custom model configuration parameters"],"output_types":["Loaded PyTorch model ready for inference","Model configuration and metadata from HuggingFace Hub"],"categories":["tool-use-integration","model-distribution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_5","uri":"capability://data.processing.analysis.batch.inference.with.variable.resolution.image.processing","name":"batch inference with variable-resolution image processing","description":"Processes multiple images of different resolutions in batches through dynamic padding and batching strategies that minimize memory waste while maintaining computational efficiency. The model handles variable-sized inputs by padding images to a common size within each batch, processing them together through the segmentation network, then cropping outputs back to original dimensions. This capability enables efficient large-scale image processing without requiring all images to be resized to a fixed resolution.","intents":["I need to process thousands of images with different aspect ratios and resolutions efficiently without resizing","I want to maximize GPU utilization by batching variable-resolution images intelligently","I need to preserve original image dimensions in output masks for downstream applications"],"best_for":["data processing teams handling diverse image datasets from multiple sources","production systems requiring efficient batch processing of user-uploaded images","researchers processing benchmark datasets with heterogeneous image sizes"],"limitations":["Padding overhead increases memory usage by 10-30% depending on aspect ratio diversity within batch","Optimal batch size varies with image resolution distribution; no automatic batch size tuning","Padding introduces minor artifacts at image borders; requires careful handling for edge-sensitive applications","Variable-resolution batching adds ~50-100ms overhead per batch for padding/cropping operations"],"requires":["PyTorch 1.9+ with tensor manipulation utilities","transformers 4.25+","torchvision for image preprocessing and resizing utilities","GPU with sufficient VRAM for largest image in batch (minimum 2GB)"],"input_types":["Batch of RGB images with arbitrary resolutions","Images as PIL Images, numpy arrays, or torch tensors","Metadata specifying original image dimensions"],"output_types":["Batch of segmentation masks matching original image dimensions","Metadata mapping batch indices to original image sizes","Optionally: padded intermediate masks for debugging"],"categories":["data-processing-analysis","batch-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_6","uri":"capability://code.generation.editing.fine.tuning.and.transfer.learning.with.frozen.encoder.options","name":"fine-tuning and transfer learning with frozen encoder options","description":"Supports transfer learning by allowing selective freezing of encoder weights while fine-tuning the decoder and refinement modules on custom datasets. Users can leverage pre-trained encoder features from ImageNet or other large-scale datasets while adapting the model to domain-specific segmentation tasks through gradient-based optimization. The architecture supports both full fine-tuning and parameter-efficient approaches like LoRA (Low-Rank Adaptation) for memory-constrained scenarios.","intents":["I want to adapt BiRefNet to my custom segmentation task without training from scratch","I need to fine-tune the model on limited labeled data by freezing the encoder and only updating the decoder","I want to implement parameter-efficient fine-tuning (LoRA) to reduce memory usage during training"],"best_for":["machine learning engineers with domain-specific segmentation datasets","teams with limited computational resources needing efficient fine-tuning","researchers exploring transfer learning from general segmentation to specialized tasks"],"limitations":["Fine-tuning requires labeled segmentation masks; semi-supervised or unsupervised adaptation is not supported","Encoder freezing may limit adaptation to significantly different visual domains (e.g., medical imaging from natural images)","LoRA support requires additional library integration; not built-in to base model","Fine-tuning convergence depends heavily on learning rate and dataset size; no automatic hyperparameter tuning"],"requires":["PyTorch 1.9+ with autograd and optimizer support","transformers 4.25+","Training dataset with pixel-level segmentation annotations","GPU with 6GB+ VRAM for fine-tuning (12GB+ recommended for batch size >2)","Optional: peft library for LoRA implementation"],"input_types":["Custom training images (RGB, arbitrary resolution)","Corresponding binary or multi-class segmentation masks","Optional: validation and test sets"],"output_types":["Fine-tuned model checkpoint (PyTorch .pt or safetensors format)","Training logs and metrics (loss, mIoU, boundary metrics)","Optionally: LoRA adapter weights for parameter-efficient storage"],"categories":["code-generation-editing","transfer-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_7","uri":"capability://tool.use.integration.onnx.export.for.cross.platform.deployment","name":"onnx export for cross-platform deployment","description":"Exports the trained BiRefNet model to ONNX (Open Neural Network Exchange) format, enabling deployment on diverse hardware platforms and inference frameworks beyond PyTorch. The export process converts the PyTorch computational graph to ONNX IR (Intermediate Representation), preserving model semantics while enabling optimization and quantization through ONNX Runtime. This capability supports deployment on CPUs, mobile devices (via ONNX Mobile), and edge devices without requiring PyTorch dependencies.","intents":["I need to deploy BiRefNet on mobile devices (iOS/Android) or edge devices without PyTorch","I want to use ONNX Runtime for faster CPU inference or cross-platform compatibility","I need to quantize the model to int8 for deployment on resource-constrained devices"],"best_for":["mobile app developers deploying on iOS/Android with ONNX Runtime","edge computing teams deploying on embedded devices or IoT hardware","production teams requiring cross-platform inference without PyTorch dependency"],"limitations":["ONNX export requires manual conversion; not automated in base model distribution","Some custom operations may not have ONNX equivalents; requires fallback to PyTorch ops or custom implementations","ONNX quantization (int8) may reduce accuracy by 2-5% depending on quantization method","ONNX Runtime performance varies significantly across platforms; CPU inference is 5-10x slower than GPU"],"requires":["PyTorch 1.9+ with ONNX export support","onnx library (1.12+) for model conversion","onnxruntime library for inference on target platform","Optional: onnxruntime-tools for quantization and optimization"],"input_types":["Trained PyTorch BiRefNet model","Sample input tensor for tracing/scripting (RGB image, 3-channel)"],"output_types":["ONNX model file (.onnx format)","Optionally: quantized ONNX model (int8)","ONNX Runtime inference session ready for deployment"],"categories":["tool-use-integration","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-zhengpeng7--birefnet__cap_8","uri":"capability://tool.use.integration.api.endpoint.deployment.via.huggingface.inference.api","name":"api endpoint deployment via huggingface inference api","description":"Deploys BiRefNet as a serverless inference endpoint through HuggingFace's Inference API, enabling REST-based access to the model without managing infrastructure. Users send images via HTTP POST requests and receive segmentation masks in response, with automatic scaling, caching, and rate limiting handled by HuggingFace's infrastructure. The endpoint supports both synchronous inference and asynchronous batch processing through the HuggingFace API.","intents":["I want to expose BiRefNet as a REST API without managing servers or containers","I need to integrate background removal into a web application via HTTP requests","I want to batch-process images asynchronously through an API without managing compute resources"],"best_for":["web developers integrating image segmentation into web applications","teams without DevOps expertise wanting to deploy models without infrastructure management","startups prototyping image processing features with minimal operational overhead"],"limitations":["API latency is higher than local inference (500ms-2s per image including network overhead)","Rate limiting and quota restrictions apply; high-volume applications may exceed free tier limits","No control over inference hardware or optimization; performance depends on HuggingFace's infrastructure","Data is sent to HuggingFace servers; not suitable for privacy-sensitive applications"],"requires":["HuggingFace account with API access","API token for authentication","HTTP client library (requests, fetch, etc.)","Internet connection for API calls"],"input_types":["Base64-encoded images or image URLs","HTTP POST requests with image data in request body"],"output_types":["JSON response containing segmentation mask (base64-encoded or as array)","Metadata including inference time and model version"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":48,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","torchvision for image preprocessing utilities","CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","transformers library 4.25+ for model loading via HuggingFace Hub","PIL/Pillow for image I/O operations","PyTorch 1.9+ with autograd enabled","Training data from camouflaged object detection benchmarks (COD10K, CAMO, or similar)","GPU with 6GB+ VRAM for training; 2GB+ for inference","transformers library 4.25+","transformers 4.25+"],"failure_modes":["Inference latency increases with image resolution; typical 1024x1024 images require 200-500ms on consumer GPUs","Performance degrades on extremely small objects (<5% of image area) due to receptive field constraints","Requires GPU memory proportional to input resolution; 4GB VRAM minimum for batch processing at 1024x1024","Binary segmentation only — does not support multi-class instance segmentation or panoptic segmentation","Requires training on camouflaged object datasets; zero-shot performance on unseen camouflage patterns is limited","Computational cost is higher than standard segmentation due to adversarial loss computation during inference","Performance depends heavily on training data diversity; models may overfit to specific camouflage types in training set","No explicit handling of multiple camouflaged objects in same image — treats scene as binary foreground/background","Saliency is subjective and dataset-dependent; model performance varies significantly across different saliency definitions","Multi-scale processing increases memory footprint by 2-3x compared to single-scale inference","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.736906954165895,"quality":0.28,"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":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:23:00.161Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":921132,"model_likes":564}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=zhengpeng7--birefnet","compare_url":"https://unfragile.ai/compare?artifact=zhengpeng7--birefnet"}},"signature":"tAc44hWje1GLKKAp8rayocrR9FsZvw1OzmhN4ScBkDY+D/RjjkZsaAx8cseewRlDqsMpINUSYzsr83wtFbhMBQ==","signedAt":"2026-06-22T11:22:31.751Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zhengpeng7--birefnet","artifact":"https://unfragile.ai/zhengpeng7--birefnet","verify":"https://unfragile.ai/api/v1/verify?slug=zhengpeng7--birefnet","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"}}