{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-pramallc--ben2","slug":"pramallc--ben2","name":"BEN2","type":"model","url":"https://huggingface.co/PramaLLC/BEN2","page_url":"https://unfragile.ai/pramallc--ben2","categories":["image-generation"],"tags":["ben2","onnx","safetensors","BEN2","background-remove","mask-generation","Dichotomous image segmentation","background remove","foreground","background","remove background","pytorch","model_hub_mixin","pytorch_model_hub_mixin","background removal","background-removal","image-segmentation","arxiv:2501.06230","license:mit","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-pramallc--ben2__cap_0","uri":"capability://image.visual.dichotomous.image.segmentation.with.binary.mask.generation","name":"dichotomous image segmentation with binary mask generation","description":"Performs pixel-level binary classification to separate foreground from background using a specialized neural architecture trained on dichotomous image segmentation datasets. The model processes input images through a deep convolutional encoder-decoder pipeline with skip connections, outputting per-pixel probability maps that are thresholded to produce crisp binary masks. This approach differs from general semantic segmentation by optimizing specifically for the two-class problem with high boundary precision.","intents":["I need to automatically extract the main subject from a photo and create a clean binary mask","I want to remove backgrounds from images programmatically without manual editing","I need to generate precise foreground masks for downstream image composition tasks","I want to batch-process hundreds of images to separate foreground and background regions"],"best_for":["computer vision engineers building automated image processing pipelines","product teams implementing background removal features in web/mobile apps","researchers working on image matting, compositing, or object extraction tasks","developers creating content creation tools that require precise subject isolation"],"limitations":["Optimized for natural images with clear foreground-background distinction; performance degrades on complex scenes with transparent or semi-transparent objects","Binary output only — does not provide soft alpha mattes or multi-class segmentation","Inference latency scales with image resolution; high-resolution inputs (4K+) may require downsampling or tiling strategies","No built-in batch processing optimization — requires external frameworks for efficient multi-image processing","Training data bias toward specific object categories may affect performance on underrepresented domains (e.g., medical imaging, industrial products)"],"requires":["Python 3.7+","PyTorch 1.9+ or ONNX Runtime 1.10+","PIL/Pillow for image I/O","NumPy for tensor manipulation","GPU with CUDA 11.0+ recommended for real-time inference (CPU inference ~500-2000ms per image depending on resolution)"],"input_types":["RGB images (PNG, JPG, WebP)","Grayscale images (auto-converted to RGB)","Images up to 4096x4096 pixels (larger images require preprocessing)"],"output_types":["Binary mask (single-channel uint8 or float32 in range [0, 1])","Probability map (float32 heatmap before thresholding)","ONNX-compatible tensor format for downstream inference"],"categories":["image-visual","computer-vision"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pramallc--ben2__cap_1","uri":"capability://tool.use.integration.multi.format.model.export.and.inference.compatibility","name":"multi-format model export and inference compatibility","description":"Provides pre-converted model weights in both PyTorch (.pt, .pth) and ONNX formats, enabling deployment across heterogeneous inference environments without requiring custom conversion pipelines. The model integrates with HuggingFace's model_hub_mixin pattern, allowing seamless loading via the transformers library while maintaining ONNX Runtime compatibility for edge devices, mobile platforms, and non-Python environments. This dual-format approach eliminates vendor lock-in and enables framework-agnostic deployment.","intents":["I need to deploy this model to both cloud (PyTorch) and edge devices (ONNX Runtime) without maintaining separate conversion code","I want to use this model in a non-Python environment (C++, JavaScript, mobile) without building custom ONNX conversion pipelines","I need to integrate this model into existing PyTorch training pipelines while also supporting ONNX inference servers","I want to reduce deployment complexity by using pre-optimized model formats instead of converting from source"],"best_for":["MLOps engineers managing multi-platform inference deployments","mobile app developers targeting iOS/Android with on-device inference","teams using ONNX Runtime for standardized model serving across heterogeneous hardware","researchers comparing PyTorch and ONNX inference performance characteristics"],"limitations":["ONNX export may lose some PyTorch-specific optimizations (e.g., custom CUDA kernels); performance parity not guaranteed across formats","ONNX model size typically 5-15% larger than PyTorch due to format overhead","Dynamic shape support in ONNX requires explicit configuration; fixed-shape models are more portable but less flexible","Quantization and pruning must be applied separately after export; no built-in post-training optimization pipeline"],"requires":["PyTorch 1.9+ for .pt/.pth loading","ONNX Runtime 1.10+ for ONNX inference","HuggingFace transformers library 4.20+ for hub_mixin integration","ONNX opset 13+ for full operator coverage"],"input_types":["PyTorch tensors (torch.Tensor)","NumPy arrays (auto-converted to tensors)","ONNX-compatible input tensors (float32, shape [batch, channels, height, width])"],"output_types":["PyTorch tensors for .pt/.pth models","NumPy arrays or raw tensor buffers for ONNX Runtime","Serialized ONNX protobuf format for model distribution"],"categories":["tool-use-integration","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pramallc--ben2__cap_2","uri":"capability://safety.moderation.safetensors.based.model.serialization.with.integrity.verification","name":"safetensors-based model serialization with integrity verification","description":"Uses the safetensors format for model weight storage, providing a safer and faster alternative to pickle-based PyTorch serialization. Safetensors includes built-in integrity checks (SHA256 hashing), prevents arbitrary code execution during deserialization, and enables lazy loading of individual weight tensors without loading the entire model into memory. This format is particularly valuable for untrusted model sources and resource-constrained environments.","intents":["I need to safely load pre-trained models from untrusted sources without risking code injection vulnerabilities","I want to load only specific layers or weights from a large model to reduce memory footprint during inference","I need to verify model integrity and detect corrupted or tampered weights before inference","I want faster model loading times compared to pickle-based PyTorch serialization"],"best_for":["security-conscious teams deploying models in production environments","edge device developers with limited RAM who need lazy-loading capabilities","researchers distributing models publicly and wanting to prevent deserialization exploits","teams using automated model validation pipelines that require integrity verification"],"limitations":["Safetensors support requires explicit library integration; not all PyTorch tools natively support the format","Lazy loading only works for sequential access patterns; random access to arbitrary tensors requires full deserialization","File size slightly larger than pickle format due to metadata overhead (~2-5%)","Conversion from existing pickle models requires one-time export step; no transparent backward compatibility"],"requires":["safetensors library 0.3.0+","PyTorch 1.9+ for tensor compatibility","HuggingFace transformers 4.20+ for automatic safetensors detection"],"input_types":["Safetensors binary files (.safetensors extension)","PyTorch state dicts (for conversion to safetensors)"],"output_types":["PyTorch state dict (dict[str, Tensor])","Individual tensor buffers (for lazy loading)","Integrity verification report (SHA256 hash validation)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pramallc--ben2__cap_3","uri":"capability://image.visual.batch.inference.with.dynamic.resolution.handling","name":"batch inference with dynamic resolution handling","description":"Supports variable-resolution image inputs through dynamic padding and resizing strategies, enabling efficient batch processing of images with different aspect ratios and dimensions without requiring uniform preprocessing. The model handles batching through a configurable batch size parameter and automatically manages memory allocation for heterogeneous input shapes, using padding-based alignment to maintain computational efficiency while preserving spatial information.","intents":["I need to process a folder of images with mixed resolutions and aspect ratios in a single batch without manual preprocessing","I want to maximize GPU utilization by batching images of different sizes while maintaining quality","I need to implement streaming inference for real-time video frame processing with variable frame sizes","I want to reduce preprocessing overhead by handling resolution variation at the model level"],"best_for":["production systems processing heterogeneous image datasets (e.g., user uploads with varied dimensions)","video processing pipelines handling variable frame resolutions","batch processing jobs that need to minimize preprocessing complexity","real-time inference systems where preprocessing latency is a bottleneck"],"limitations":["Padding-based alignment may introduce artifacts at image boundaries; requires post-processing to remove padding effects from output masks","Memory usage scales with the largest image in the batch; mixing very large and small images can waste GPU memory","Dynamic shape handling adds ~50-100ms overhead per batch due to shape inference and padding computation","No built-in aspect ratio preservation; images are resized to fit model input dimensions, potentially distorting content"],"requires":["PyTorch 1.9+ with CUDA support for GPU batching","Sufficient GPU memory for largest batch size (recommend 8GB+ for batch_size=4 at 1024x1024 resolution)","NumPy for shape computation and padding operations"],"input_types":["Batch of RGB images with variable resolutions (e.g., 512x512, 1024x768, 800x600 in same batch)","Images with different aspect ratios (landscape, portrait, square)"],"output_types":["Batch of binary masks with original input dimensions preserved","Probability maps (float32) for each image in batch","Metadata indicating padding regions for post-processing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-pramallc--ben2__cap_4","uri":"capability://tool.use.integration.huggingface.hub.integration.with.model.versioning.and.auto.download","name":"huggingface hub integration with model versioning and auto-download","description":"Integrates with HuggingFace's model hub infrastructure using the model_hub_mixin pattern, enabling one-line model loading with automatic version management, caching, and download orchestration. The model supports semantic versioning through git-based revision tracking, allowing users to pin specific model versions or automatically fetch the latest weights. This integration provides built-in model card documentation, license metadata, and usage statistics without requiring custom hosting or distribution infrastructure.","intents":["I want to load this model with a single line of code without manually downloading weights or managing file paths","I need to pin my application to a specific model version and automatically update when new versions are released","I want to track model usage statistics and understand how my model is being deployed in the wild","I need to distribute model updates to users without requiring application code changes"],"best_for":["developers building applications that depend on pre-trained models","researchers sharing models with reproducible version pinning","teams managing model lifecycle with versioning and rollback requirements","open-source projects requiring low-friction model distribution"],"limitations":["Requires internet connectivity for initial model download; no offline-first workflow without pre-caching","HuggingFace Hub rate limiting may throttle downloads for high-volume deployments (recommend caching at application level)","Model card and metadata are stored separately from weights; inconsistencies can occur if not synchronized","Version pinning relies on git-based revision tracking; force-pushing or deleting revisions can break reproducibility"],"requires":["HuggingFace transformers library 4.20+","huggingface_hub package 0.10.0+","Internet connectivity for model download","HuggingFace account (optional, for private model access)"],"input_types":["Model identifier string (e.g., 'PramaLLC/BEN2')","Revision/version specifier (e.g., 'main', 'v1.0', commit hash)"],"output_types":["Loaded PyTorch model instance","Model configuration and metadata","Local cache path for offline access"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+ or ONNX Runtime 1.10+","PIL/Pillow for image I/O","NumPy for tensor manipulation","GPU with CUDA 11.0+ recommended for real-time inference (CPU inference ~500-2000ms per image depending on resolution)","PyTorch 1.9+ for .pt/.pth loading","ONNX Runtime 1.10+ for ONNX inference","HuggingFace transformers library 4.20+ for hub_mixin integration","ONNX opset 13+ for full operator coverage","safetensors library 0.3.0+"],"failure_modes":["Optimized for natural images with clear foreground-background distinction; performance degrades on complex scenes with transparent or semi-transparent objects","Binary output only — does not provide soft alpha mattes or multi-class segmentation","Inference latency scales with image resolution; high-resolution inputs (4K+) may require downsampling or tiling strategies","No built-in batch processing optimization — requires external frameworks for efficient multi-image processing","Training data bias toward specific object categories may affect performance on underrepresented domains (e.g., medical imaging, industrial products)","ONNX export may lose some PyTorch-specific optimizations (e.g., custom CUDA kernels); performance parity not guaranteed across formats","ONNX model size typically 5-15% larger than PyTorch due to format overhead","Dynamic shape support in ONNX requires explicit configuration; fixed-shape models are more portable but less flexible","Quantization and pruning must be applied separately after export; no built-in post-training optimization pipeline","Safetensors support requires explicit library integration; not all PyTorch tools natively support the format","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6136852017399371,"quality":0.2,"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":"2026-05-03T14:23:00.161Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":207542,"model_likes":228}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pramallc--ben2","compare_url":"https://unfragile.ai/compare?artifact=pramallc--ben2"}},"signature":"6OY/GX/GpVqCHNpj5oTJ9b3/qdMACesE78maCTPMLRpLB/bOMQQRb068KgZi6NsLnh0bEtSg15wZgZG92PFeAA==","signedAt":"2026-06-21T05:08:30.543Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pramallc--ben2","artifact":"https://unfragile.ai/pramallc--ben2","verify":"https://unfragile.ai/api/v1/verify?slug=pramallc--ben2","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"}}