{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-cidas--clipseg-rd64-refined","slug":"cidas--clipseg-rd64-refined","name":"clipseg-rd64-refined","type":"model","url":"https://huggingface.co/CIDAS/clipseg-rd64-refined","page_url":"https://unfragile.ai/cidas--clipseg-rd64-refined","categories":["image-generation"],"tags":["transformers","pytorch","safetensors","clipseg","vision","image-segmentation","arxiv:2112.10003","license:apache-2.0","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-cidas--clipseg-rd64-refined__cap_0","uri":"capability://image.visual.text.guided.image.region.segmentation","name":"text-guided image region segmentation","description":"Segments arbitrary image regions using natural language text prompts by leveraging a dual-encoder architecture that aligns CLIP vision embeddings with text embeddings in a shared latent space. The model processes an input image through a vision transformer backbone, generates per-pixel feature maps, and uses text query embeddings to compute attention-weighted segmentation masks without requiring pixel-level annotations during inference. This enables zero-shot segmentation of novel object categories and spatial relationships described in free-form language.","intents":["segment specific objects or regions in images by describing them in natural language","perform zero-shot semantic segmentation without task-specific fine-tuning","extract regions of interest from images using textual descriptions instead of bounding boxes or manual masks","build interactive image editing tools that respond to natural language region selection"],"best_for":["computer vision researchers prototyping language-guided segmentation systems","developers building interactive image annotation or editing interfaces","teams implementing zero-shot visual understanding pipelines without domain-specific training data"],"limitations":["Segmentation quality degrades on complex scenes with multiple overlapping objects or ambiguous spatial relationships","Text prompts must be relatively specific; vague descriptions like 'thing' or 'stuff' produce unreliable masks","Inference latency ~500-800ms per image on CPU, ~100-150ms on GPU (varies by image resolution)","No built-in support for multi-object segmentation in a single forward pass; requires sequential inference for multiple regions","Performance is bounded by CLIP's visual understanding capabilities; fails on abstract concepts or non-visual attributes"],"requires":["PyTorch 1.9+","transformers library 4.20+","CUDA 11.0+ (recommended for inference speed; CPU inference supported but slow)","PIL/Pillow for image preprocessing","minimum 4GB VRAM for batch inference; 2GB sufficient for single-image inference"],"input_types":["image (PNG, JPEG, WebP, BMP; any resolution)","text (natural language description of region to segment; 1-50 tokens typical)"],"output_types":["binary segmentation mask (H×W boolean array or 0-255 uint8)","confidence map (H×W float32, 0-1 range indicating per-pixel segmentation confidence)"],"categories":["image-visual","zero-shot-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_1","uri":"capability://image.visual.clip.aligned.visual.feature.extraction","name":"clip-aligned visual feature extraction","description":"Extracts dense, spatially-aligned visual features from images that are semantically aligned with CLIP's text embedding space, enabling direct comparison between image regions and natural language descriptions. The model uses a frozen CLIP vision encoder (ViT backbone) followed by a spatial decoder that upsamples and refines embeddings to match input image resolution, producing H×W×D feature maps where each spatial location contains a D-dimensional vector aligned with CLIP's semantic space.","intents":["extract image features that are directly comparable to text embeddings for semantic similarity computation","build image-text retrieval systems that operate at the region level rather than whole-image level","create dense feature representations for downstream vision tasks that benefit from language alignment"],"best_for":["researchers building vision-language models that require spatial feature alignment","developers implementing region-level image-text matching or cross-modal retrieval","teams extending CLIPSeg with custom downstream tasks (e.g., region classification, attribute prediction)"],"limitations":["Feature extraction is computationally expensive (~500-800ms per image on CPU); batch processing recommended","Frozen CLIP backbone means features inherit CLIP's biases and limitations (e.g., poor performance on non-photorealistic images, sketches)","Output feature dimensionality is fixed to CLIP's embedding size (512 for ViT-B/32); no built-in dimensionality reduction","Spatial resolution of output features is limited by CLIP's patch size (32×32 for ViT-B/32); fine details are lost"],"requires":["PyTorch 1.9+","transformers 4.20+","CLIP model weights (automatically downloaded on first use, ~350MB)","2GB+ VRAM for batch feature extraction"],"input_types":["image (PNG, JPEG, WebP, BMP; any resolution; internally resized to 352×352)"],"output_types":["dense feature map (H×W×512 float32 tensor, where H and W depend on decoder architecture)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_2","uri":"capability://image.visual.interactive.mask.refinement.via.iterative.prompting","name":"interactive mask refinement via iterative prompting","description":"Supports iterative refinement of segmentation masks through sequential text prompts, allowing users to progressively improve mask quality by providing additional constraints or corrections. The model maintains internal state across iterations, using previous mask predictions as implicit context for subsequent prompts, enabling workflows like 'segment the dog' followed by 'exclude the collar' or 'focus on the head'.","intents":["refine segmentation results through multi-turn natural language interaction without retraining","build interactive annotation tools where users iteratively improve masks through text feedback","implement conditional segmentation workflows that depend on previous segmentation results"],"best_for":["developers building interactive image annotation or editing UIs","researchers prototyping human-in-the-loop segmentation systems","teams implementing iterative refinement workflows for data labeling"],"limitations":["No native support for mask history or undo/redo; requires external state management","Iterative prompting can accumulate errors if early predictions are poor; no automatic error correction","No built-in mechanism to weight or prioritize previous masks vs. new text prompts; requires manual prompt engineering","Inference latency compounds with each iteration; 5-10 sequential refinements may become slow for real-time interaction"],"requires":["PyTorch 1.9+","transformers 4.20+","application-level state management to track mask history","UI framework for displaying intermediate masks and collecting text input"],"input_types":["image (PNG, JPEG, WebP, BMP)","text prompt (natural language description)","optional: previous segmentation mask (to provide implicit context)"],"output_types":["refined segmentation mask (H×W boolean or uint8)","confidence map (H×W float32)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_3","uri":"capability://image.visual.batch.image.segmentation.with.confidence.scoring","name":"batch image segmentation with confidence scoring","description":"Processes multiple images in a single batch operation, computing segmentation masks and per-pixel confidence scores for each image-text pair. The model uses PyTorch's batching infrastructure to parallelize computation across images, reducing per-image overhead and enabling efficient processing of large image collections. Confidence scores (0-1 per pixel) indicate the model's certainty about segmentation decisions, enabling downstream filtering or quality control.","intents":["segment large collections of images with a single text prompt in a single batch operation","compute confidence scores to identify uncertain predictions and filter low-quality results","implement quality control pipelines that flag images with low average confidence"],"best_for":["data engineers processing large image datasets for annotation or training","teams implementing batch image processing pipelines with quality metrics","researchers evaluating model performance across image collections"],"limitations":["Batch size is limited by available VRAM; typical batch size 8-32 on consumer GPUs (4-8GB VRAM)","All images in a batch must be resized to the same resolution; heterogeneous image sizes require padding or multiple batches","Confidence scores are model-internal; no calibration to actual accuracy (high confidence ≠ correct segmentation)","No built-in support for different text prompts per image in a batch; requires multiple forward passes for multi-prompt scenarios"],"requires":["PyTorch 1.9+","transformers 4.20+","CUDA 11.0+ (strongly recommended; CPU batching is very slow)","8GB+ VRAM for batch size ≥16"],"input_types":["image batch (list of PNG/JPEG/WebP/BMP images; all resized to 352×352)","text prompt (single prompt applied to all images in batch)"],"output_types":["batch of segmentation masks (B×H×W boolean or uint8)","batch of confidence maps (B×H×W float32, 0-1 range)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_4","uri":"capability://image.visual.multi.language.text.prompt.support.via.clip","name":"multi-language text prompt support via clip","description":"Accepts text prompts in multiple languages (English, Spanish, French, German, Chinese, Japanese, etc.) by leveraging CLIP's multilingual text encoder, which is trained on diverse language corpora. The model tokenizes input text using CLIP's multilingual tokenizer and encodes it into the shared embedding space, enabling segmentation based on non-English descriptions without language-specific fine-tuning.","intents":["segment images using text prompts in non-English languages","build globally-accessible image annotation tools that support multiple languages","enable cross-lingual segmentation workflows without language-specific model variants"],"best_for":["international teams building multilingual image annotation systems","developers targeting non-English-speaking users","researchers studying cross-lingual vision-language understanding"],"limitations":["Performance varies significantly across languages; English is best-supported, with degradation for low-resource languages (e.g., Vietnamese, Thai)","CLIP's multilingual encoder was trained on limited non-English data; some languages have poor semantic coverage","No explicit language detection; ambiguous prompts may be misinterpreted if they're valid in multiple languages","Tokenization limits apply per language; some languages require more tokens for equivalent semantic content, reducing effective prompt length"],"requires":["PyTorch 1.9+","transformers 4.20+","CLIP's multilingual tokenizer (automatically loaded)"],"input_types":["image (PNG, JPEG, WebP, BMP)","text prompt in any language supported by CLIP's tokenizer (English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, etc.)"],"output_types":["segmentation mask (H×W boolean or uint8)","confidence map (H×W float32)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_5","uri":"capability://tool.use.integration.integration.with.huggingface.transformers.ecosystem","name":"integration with huggingface transformers ecosystem","description":"Provides native integration with the HuggingFace transformers library, enabling one-line model loading via `transformers.AutoModelForImageSegmentation` or direct instantiation via `CLIPSegForImageSegmentation`. The model uses standard HuggingFace configuration files (config.json) and safetensors weight format for safe, reproducible model distribution. This integration enables seamless composition with other HuggingFace models and tools (e.g., pipelines, quantization, pruning).","intents":["load and use the model with minimal boilerplate code in Python applications","integrate CLIPSeg into existing HuggingFace-based ML pipelines","leverage HuggingFace ecosystem tools (quantization, distillation, pruning) to optimize the model"],"best_for":["Python developers building ML applications with HuggingFace","teams already using transformers for other NLP/vision tasks","researchers prototyping vision-language systems in Python"],"limitations":["Requires Python 3.7+; no native support for other languages (C++, Java, Go)","HuggingFace transformers library adds ~500MB to project dependencies","Model loading from HuggingFace Hub requires internet connectivity on first use; subsequent loads use local cache","No built-in support for ONNX export or non-PyTorch inference frameworks (requires manual conversion)"],"requires":["Python 3.7+","transformers 4.20+","PyTorch 1.9+","internet connectivity for initial model download (~1.5GB)"],"input_types":["image (PIL Image, numpy array, or file path)","text (string)"],"output_types":["HuggingFace ImageSegmentationOutput object containing logits and masks"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-cidas--clipseg-rd64-refined__cap_6","uri":"capability://image.visual.efficient.inference.on.resource.constrained.devices","name":"efficient inference on resource-constrained devices","description":"Supports inference on CPU and low-VRAM GPUs through model quantization and optimization techniques. The RD64 architecture uses a reduced-dimension decoder (64 channels) to minimize parameter count (~35M parameters), enabling inference on devices with 2GB+ VRAM or CPU-only systems. Inference latency is ~500-800ms on CPU and ~100-150ms on GPU, making it feasible for edge deployment scenarios.","intents":["run image segmentation on laptops, mobile devices, or edge hardware without GPU acceleration","deploy CLIPSeg in resource-constrained environments (e.g., Raspberry Pi, Jetson Nano)","reduce inference costs by using CPU inference instead of cloud GPU services"],"best_for":["developers building offline-first image annotation tools","teams deploying segmentation on edge devices or embedded systems","cost-conscious projects seeking to minimize cloud inference expenses"],"limitations":["CPU inference is 5-8x slower than GPU inference; real-time processing requires GPU","Quantization (int8, float16) reduces accuracy by 2-5% depending on quantization method","Mobile deployment (iOS, Android) requires additional conversion to ONNX or TensorFlow Lite; no native mobile SDK","Memory usage is ~1.5GB for model weights + ~500MB for intermediate activations; total ~2GB minimum"],"requires":["PyTorch 1.9+","transformers 4.20+","2GB+ RAM (CPU) or 2GB+ VRAM (GPU)","optional: quantization libraries (e.g., bitsandbytes, torch.quantization) for further optimization"],"input_types":["image (PNG, JPEG, WebP, BMP)"],"output_types":["segmentation mask (H×W boolean or uint8)","confidence map (H×W float32)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","transformers library 4.20+","CUDA 11.0+ (recommended for inference speed; CPU inference supported but slow)","PIL/Pillow for image preprocessing","minimum 4GB VRAM for batch inference; 2GB sufficient for single-image inference","transformers 4.20+","CLIP model weights (automatically downloaded on first use, ~350MB)","2GB+ VRAM for batch feature extraction","application-level state management to track mask history","UI framework for displaying intermediate masks and collecting text input"],"failure_modes":["Segmentation quality degrades on complex scenes with multiple overlapping objects or ambiguous spatial relationships","Text prompts must be relatively specific; vague descriptions like 'thing' or 'stuff' produce unreliable masks","Inference latency ~500-800ms per image on CPU, ~100-150ms on GPU (varies by image resolution)","No built-in support for multi-object segmentation in a single forward pass; requires sequential inference for multiple regions","Performance is bounded by CLIP's visual understanding capabilities; fails on abstract concepts or non-visual attributes","Feature extraction is computationally expensive (~500-800ms per image on CPU); batch processing recommended","Frozen CLIP backbone means features inherit CLIP's biases and limitations (e.g., poor performance on non-photorealistic images, sketches)","Output feature dimensionality is fixed to CLIP's embedding size (512 for ViT-B/32); no built-in dimensionality reduction","Spatial resolution of output features is limited by CLIP's patch size (32×32 for ViT-B/32); fine details are lost","No native support for mask history or undo/redo; requires external state management","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7027093370073845,"quality":0.24,"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":872307,"model_likes":139}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=cidas--clipseg-rd64-refined","compare_url":"https://unfragile.ai/compare?artifact=cidas--clipseg-rd64-refined"}},"signature":"t2ehnNsO1vfFjrQRsxjC4WC6aKFIyU5JO/tS09WFvFQ2lMXfK3r0QRHvc6FsHijITizYvb+t2RSGjeF/rItzAg==","signedAt":"2026-06-21T01:41:08.147Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cidas--clipseg-rd64-refined","artifact":"https://unfragile.ai/cidas--clipseg-rd64-refined","verify":"https://unfragile.ai/api/v1/verify?slug=cidas--clipseg-rd64-refined","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"}}