{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"clip","slug":"clip","name":"CLIP","type":"repo","url":"https://github.com/openai/CLIP","page_url":"https://unfragile.ai/clip","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"clip__cap_0","uri":"capability://image.visual.zero.shot.image.classification.via.natural.language.descriptions","name":"zero-shot image classification via natural language descriptions","description":"Classifies images into arbitrary categories without training by encoding images and text descriptions into a shared embedding space, then computing cosine similarity between image and text embeddings. The dual-encoder architecture (separate image and text encoders) projects both modalities into the same vector space where semantically related concepts cluster together, enabling direct comparison without fine-tuning on target classes.","intents":["classify images into custom categories without labeled training data","build image classifiers that adapt to new categories at runtime","perform one-shot or few-shot image classification by describing target classes in natural language"],"best_for":["computer vision teams building flexible classification systems","developers prototyping image understanding features without labeled datasets","applications requiring dynamic category definitions that change per-user or per-session"],"limitations":["accuracy degrades on domain-specific or highly technical visual concepts not well-represented in training data","requires careful prompt engineering — class descriptions must be semantically clear and specific","no ability to learn from user feedback or examples without retraining the base model","performance varies significantly based on text prompt quality and specificity"],"requires":["Python 3.7+","PyTorch 1.7.1+","One of 9 pre-trained model variants (RN50, RN101, RN50x4, RN50x16, RN50x64, ViT-B/32, ViT-B/16, ViT-L/14, ViT-L/14@336px)","GPU recommended for inference speed (CPU inference ~500ms-2s per image depending on model size)"],"input_types":["PIL Image objects","numpy arrays (H×W×3 format)","file paths to image files (JPEG, PNG, etc.)"],"output_types":["similarity scores (float tensors, range 0-1 after softmax)","predicted class labels (strings)","confidence scores per class"],"categories":["image-visual","zero-shot-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_1","uri":"capability://image.visual.image.text.similarity.scoring.with.shared.embedding.space","name":"image-text similarity scoring with shared embedding space","description":"Computes semantic similarity between images and text by encoding both into a 512-dimensional (or larger, depending on model variant) shared embedding space using separate image and text encoders, then calculating cosine similarity between the resulting vectors. The contrastive training objective aligns related image-text pairs close together in this space while pushing unrelated pairs apart, enabling ranking and matching tasks.","intents":["find the most relevant images for a given text query","rank images by semantic relevance to a text description","measure semantic alignment between images and captions","build image-text retrieval systems without labeled training data"],"best_for":["search and retrieval teams building image search engines","content moderation systems that need to match images to policy descriptions","multimodal recommendation systems requiring image-text alignment scoring"],"limitations":["similarity scores are relative, not absolute — only meaningful when comparing multiple image-text pairs","text descriptions must be reasonably specific; vague queries produce unreliable rankings","no ability to weight different aspects of images (e.g., 'prioritize color over shape')","embedding space is fixed at model load time; cannot adapt to domain-specific similarity notions"],"requires":["Python 3.7+","PyTorch 1.7.1+","Pre-trained CLIP model loaded via clip.load()","Image preprocessing via the returned preprocessing transform (resizing, normalization)"],"input_types":["PIL Image objects or numpy arrays (for images)","strings or tokenized text tensors (for text)"],"output_types":["similarity scores (float tensors, typically in range -1 to 1 for cosine similarity)","ranked lists of images or texts sorted by similarity"],"categories":["image-visual","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_10","uri":"capability://text.generation.language.byte.pair.encoding.tokenization.with.fixed.vocabulary.and.context.length","name":"byte-pair encoding tokenization with fixed vocabulary and context length","description":"Tokenizes text strings using a custom byte-pair encoding (BPE) tokenizer with a 49,152-token vocabulary trained on the pre-training corpus. The tokenizer is accessed via clip.tokenize(text) and converts text to token IDs, automatically padding or truncating to a fixed context length of 77 tokens. The tokenizer handles special tokens (start-of-sequence, end-of-sequence, padding) and produces integer token tensors suitable for the text encoder.","intents":["convert text strings to token IDs for input to the text encoder","handle variable-length text with automatic padding and truncation","understand how text is tokenized and represented internally","process batches of text with consistent token tensor shapes"],"best_for":["developers building text encoding pipelines","researchers studying how CLIP tokenizes and represents text","teams processing text for image-text matching or classification"],"limitations":["context length is fixed at 77 tokens; longer text is silently truncated","vocabulary is fixed at 49,152 tokens; out-of-vocabulary words are handled by BPE subword splitting","tokenizer cannot be fine-tuned or extended; no support for custom vocabularies","no built-in support for multi-language text; primarily trained on English"],"requires":["Python 3.7+","CLIP model loaded (tokenizer is included)","Text input as Python strings or lists of strings"],"input_types":["single text string","list of text strings (for batch processing)"],"output_types":["token ID tensors (B×77, integer dtype)","attention masks (B×77, indicating padding)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_2","uri":"capability://image.visual.image.feature.extraction.into.fixed.dimensional.embeddings","name":"image feature extraction into fixed-dimensional embeddings","description":"Extracts images into fixed-size embedding vectors (512 to 768 dimensions depending on model variant) by passing images through the image encoder (either a modified ResNet or Vision Transformer backbone) and projecting the output into the shared embedding space. These embeddings can be stored, indexed, and used for downstream tasks like clustering, retrieval, or as input to other models.","intents":["extract visual features from images for use in downstream machine learning pipelines","build searchable image databases by pre-computing and indexing image embeddings","cluster images by visual similarity without labels","use image embeddings as input to other models (e.g., classifiers, recommendation systems)"],"best_for":["data engineers building image indexing and retrieval pipelines","ML teams using embeddings as features for downstream supervised learning","applications requiring fast image similarity search via vector databases"],"limitations":["embeddings are model-specific; switching model variants requires re-computing all embeddings","no interpretability — embeddings are high-dimensional vectors without semantic labels","embedding quality depends on whether images are in-distribution with training data (400M internet images)","batch processing is significantly faster than single-image inference, but requires managing memory for large batches"],"requires":["Python 3.7+","PyTorch 1.7.1+","CLIP model loaded and in eval mode","Images preprocessed to model input size (224×224 for most variants, 336×336 for ViT-L/14@336px)"],"input_types":["PIL Image objects","numpy arrays (H×W×3, uint8 or float32)","batches of preprocessed image tensors (B×3×H×W)"],"output_types":["embedding tensors (B×D where D is 512, 768, or 1024 depending on model)","numpy arrays or lists of embeddings for storage/indexing"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_3","uri":"capability://text.generation.language.text.feature.extraction.and.tokenization.with.context.aware.encoding","name":"text feature extraction and tokenization with context-aware encoding","description":"Converts text strings into fixed-size embedding vectors (512 to 768 dimensions) by first tokenizing text using a byte-pair encoding (BPE) tokenizer with a 49,152-token vocabulary, then passing tokenized sequences through a Transformer encoder with causal attention masking, and finally projecting the output into the shared embedding space. The tokenizer handles arbitrary text up to 77 tokens (context length) and pads or truncates as needed.","intents":["convert text descriptions into embeddings for image-text matching","tokenize and encode arbitrary text queries for semantic search","extract semantic features from text for downstream tasks","handle variable-length text inputs with automatic padding/truncation"],"best_for":["developers building text-to-image search or retrieval systems","teams using CLIP embeddings for text-based image classification","applications requiring semantic text encoding aligned with visual concepts"],"limitations":["maximum context length is 77 tokens; longer text is truncated without warning","tokenizer is fixed and cannot be fine-tuned; vocabulary is limited to 49,152 tokens","causal attention masking (used in text encoder) may not be optimal for all text understanding tasks","no support for multi-language text; primarily trained on English"],"requires":["Python 3.7+","PyTorch 1.7.1+","CLIP model loaded (includes tokenizer)","Text input as Python strings"],"input_types":["Python strings (arbitrary length, will be truncated to 77 tokens)","lists of strings (for batch processing)"],"output_types":["tokenized tensors (B×77 integer token IDs)","embedding tensors (B×D where D is 512, 768, or 1024)","attention masks (B×77) indicating padding"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_4","uri":"capability://image.visual.multi.model.variant.selection.with.architecture.and.parameter.trade.offs","name":"multi-model variant selection with architecture and parameter trade-offs","description":"Provides 9 pre-trained model variants with different architectural choices (ResNet-50/101/50x4/50x16/50x64 or Vision Transformer B/32, B/16, L/14, L/14@336px) and parameter counts (50M to 400M), allowing users to select based on accuracy-speed-memory trade-offs. Models are loaded via clip.load(model_name) which downloads from OpenAI's Azure endpoint, caches locally, and returns the model plus preprocessing transform. Each variant has different input image sizes (224×224 to 448×448) and embedding dimensions.","intents":["choose a model variant optimized for inference speed vs accuracy for a specific application","deploy CLIP in resource-constrained environments by selecting smaller models","benchmark different architectures (ResNet vs Vision Transformer) on custom tasks","balance GPU memory usage with model capacity for batch processing"],"best_for":["ML engineers optimizing inference latency and memory for production systems","researchers comparing architectural choices (CNN vs Transformer) for vision-language tasks","teams deploying CLIP on edge devices or resource-constrained servers"],"limitations":["all models are frozen; no fine-tuning support in the official repository","larger models (ViT-L/14, RN50x64) require significant GPU memory (8GB+ for batch inference)","model selection is a one-time decision at load time; cannot switch models without reloading","no quantization or distillation variants provided; all models are full precision (float32)"],"requires":["Python 3.7+","PyTorch 1.7.1+","Internet connection for initial model download (models cached in ~/.cache/clip/)","GPU with sufficient VRAM for selected model (RN50: 2GB, ViT-L/14: 8GB+)"],"input_types":["model name string (e.g., 'ViT-B/32', 'RN50')"],"output_types":["loaded PyTorch model object","preprocessing transform function (torchvision.transforms.Compose)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_5","uri":"capability://data.processing.analysis.batch.processing.with.automatic.device.placement.and.mixed.precision.support","name":"batch processing with automatic device placement and mixed precision support","description":"Processes multiple images or text samples in batches through the model with automatic GPU/CPU device placement and optional JIT compilation for faster inference. The clip.load() function accepts a 'device' parameter (e.g., 'cuda', 'cpu') and a 'jit' boolean flag that compiles the model to TorchScript for optimized execution. Batch processing is significantly faster than single-sample inference due to GPU parallelization and reduced overhead.","intents":["process large numbers of images or text samples efficiently on GPU","optimize inference latency by batching requests","deploy CLIP with JIT compilation for faster inference in production","handle device placement automatically without manual GPU management"],"best_for":["data engineers building batch image processing pipelines","teams deploying CLIP inference servers handling multiple requests","researchers processing large image datasets for evaluation"],"limitations":["JIT compilation adds ~1-2 second overhead on first call but improves subsequent calls","batch size is limited by GPU VRAM; no automatic batching or gradient accumulation","mixed precision (float16) is not officially supported; all inference is float32","no built-in data loading utilities; users must handle batching and preprocessing manually"],"requires":["Python 3.7+","PyTorch 1.7.1+","CUDA 11.0+ for GPU inference (optional but recommended)","Sufficient GPU VRAM for batch size (scales linearly with batch size)"],"input_types":["batches of preprocessed image tensors (B×3×H×W)","batches of tokenized text tensors (B×77)"],"output_types":["batches of embedding tensors (B×D)","similarity matrices (B×B for image-text pairs)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_6","uri":"capability://image.visual.vision.transformer.and.modified.resnet.image.encoder.selection","name":"vision transformer and modified resnet image encoder selection","description":"Provides two distinct image encoder architectures: Vision Transformers (ViT-B/32, ViT-B/16, ViT-L/14, ViT-L/14@336px) that divide images into patches and process them with self-attention, and modified ResNets (RN50, RN101, RN50x4, RN50x16, RN50x64) that use convolutional layers with additional attention mechanisms. Both architectures are trained end-to-end with the text encoder using contrastive loss, and the choice affects accuracy, speed, and memory trade-offs.","intents":["select between CNN and Transformer architectures based on accuracy and speed requirements","understand architectural differences in how images are processed (patches vs convolutions)","benchmark vision transformers vs ResNets on image-text alignment tasks","choose architectures optimized for different input image resolutions"],"best_for":["researchers studying vision transformer vs CNN performance on multimodal tasks","teams optimizing for specific hardware (e.g., ViT for TPUs, ResNet for CPUs)","applications requiring interpretability (ViT attention maps vs ResNet feature maps)"],"limitations":["Vision Transformers require more GPU memory than ResNets of comparable accuracy","ResNets are faster on CPU inference; ViTs benefit more from GPU parallelization","no architectural documentation or ablation studies provided; design choices are opaque","both architectures are frozen; no ability to modify or extend them"],"requires":["Python 3.7+","PyTorch 1.7.1+","Model name string specifying architecture (e.g., 'ViT-B/32' or 'RN50')"],"input_types":["images preprocessed to model input size (224×224 for most variants, 336×336 for ViT-L/14@336px)"],"output_types":["image embeddings (B×D)","intermediate feature maps (for analysis or visualization)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_7","uri":"capability://image.visual.contrastive.loss.training.objective.for.image.text.alignment","name":"contrastive loss training objective for image-text alignment","description":"Implements a contrastive pre-training objective where image-text pairs from the training corpus are pulled together in embedding space while negative pairs (unrelated images and text) are pushed apart. The loss function computes similarity between all image-text pairs in a batch, creating a symmetric contrastive objective that aligns both modalities. This training approach enables the learned embeddings to capture semantic relationships without explicit labels for downstream tasks.","intents":["understand the training objective that enables zero-shot transfer","fine-tune CLIP on custom image-text datasets using the same contrastive loss","adapt CLIP to domain-specific image-text relationships by retraining with custom data"],"best_for":["researchers studying contrastive learning for multimodal models","teams fine-tuning CLIP on domain-specific image-text pairs","developers implementing custom training loops using CLIP as a backbone"],"limitations":["official repository does not provide training code or fine-tuning utilities","contrastive loss requires large batch sizes (256+) for effective negative sampling","no guidance on hyperparameters, data augmentation, or convergence criteria for fine-tuning","training from scratch requires 400M image-text pairs; fine-tuning requires thousands of pairs"],"requires":["Python 3.7+","PyTorch 1.7.1+","Understanding of contrastive learning and multimodal training","Large-scale image-text dataset (for fine-tuning or training from scratch)"],"input_types":["batches of images and corresponding text descriptions"],"output_types":["contrastive loss value (scalar)","aligned embeddings (image and text in shared space)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_8","uri":"capability://image.visual.image.preprocessing.and.normalization.with.model.specific.transforms","name":"image preprocessing and normalization with model-specific transforms","description":"Applies model-specific image preprocessing including resizing to the correct input dimensions (224×224 for most variants, 336×336 for ViT-L/14@336px, 448×448 for RN50x64), center cropping, conversion to tensors, and normalization using ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]). The clip.load() function returns a preprocessing transform (torchvision.transforms.Compose) that encapsulates these operations, ensuring consistency with training-time preprocessing.","intents":["apply correct preprocessing to images before encoding","ensure consistency between training and inference preprocessing","handle variable-sized input images by resizing and cropping","normalize images using ImageNet statistics for optimal model performance"],"best_for":["developers building inference pipelines that need correct image preprocessing","teams ensuring reproducibility by matching training-time preprocessing","applications handling diverse image formats and sizes"],"limitations":["preprocessing is fixed and cannot be customized (e.g., no data augmentation)","center cropping may lose information from images with off-center subjects","ImageNet normalization assumes images are in RGB format; BGR or other formats require manual conversion","preprocessing is applied in-memory; no streaming or lazy evaluation"],"requires":["Python 3.7+","torchvision 0.8.0+","PIL or numpy for image loading","Images in standard formats (JPEG, PNG, etc.)"],"input_types":["PIL Image objects","numpy arrays (H×W×3, uint8 or float32)","file paths (as strings)"],"output_types":["preprocessed image tensors (3×H×W, float32, normalized)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__cap_9","uri":"capability://automation.workflow.model.availability.discovery.and.caching.with.automatic.downloads","name":"model availability discovery and caching with automatic downloads","description":"Provides clip.available_models() function that returns a list of all available pre-trained model names, and clip.load() automatically downloads models from OpenAI's Azure endpoint on first use, caches them locally in ~/.cache/clip/, and loads from cache on subsequent calls. This enables users to discover available models, automatically manage model downloads, and avoid re-downloading large model files.","intents":["discover which CLIP model variants are available","automatically download and cache models without manual setup","manage model storage and avoid redundant downloads","programmatically select models based on available options"],"best_for":["developers building applications that need to discover and load models dynamically","teams deploying CLIP in environments with limited bandwidth (caching reduces re-downloads)","researchers experimenting with different model variants"],"limitations":["models are cached in user's home directory; no control over cache location","no cache invalidation or version management; old model versions are not automatically cleaned up","downloads require internet connection; no offline mode or pre-downloaded model bundles","no progress indication or resumable downloads for large models"],"requires":["Python 3.7+","Internet connection for initial model download","Write access to ~/.cache/ directory","Sufficient disk space (models range from 350MB to 1.5GB)"],"input_types":["none (for available_models())","model name string (for load())"],"output_types":["list of available model names (for available_models())","loaded model and preprocessing transform (for load())"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"clip__headline","uri":"capability://image.visual.zero.shot.image.classification.model","name":"zero-shot image classification model","description":"OpenAI's CLIP is a powerful zero-shot image classification model that connects images and text, enabling users to perform image search and multimodal understanding tasks without needing labeled training data.","intents":["best zero-shot image classification model","zero-shot classification for image search","how to use CLIP for image-text similarity","CLIP model for multimodal tasks","top models for image recognition without labels"],"best_for":["users needing flexible image classification solutions"],"limitations":["requires understanding of multimodal inputs"],"requires":["image and text data"],"input_types":["images","text descriptions"],"output_types":["classification results","similarity scores"],"categories":["image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.7.1+","One of 9 pre-trained model variants (RN50, RN101, RN50x4, RN50x16, RN50x64, ViT-B/32, ViT-B/16, ViT-L/14, ViT-L/14@336px)","GPU recommended for inference speed (CPU inference ~500ms-2s per image depending on model size)","Pre-trained CLIP model loaded via clip.load()","Image preprocessing via the returned preprocessing transform (resizing, normalization)","CLIP model loaded (tokenizer is included)","Text input as Python strings or lists of strings","CLIP model loaded and in eval mode","Images preprocessed to model input size (224×224 for most variants, 336×336 for ViT-L/14@336px)"],"failure_modes":["accuracy degrades on domain-specific or highly technical visual concepts not well-represented in training data","requires careful prompt engineering — class descriptions must be semantically clear and specific","no ability to learn from user feedback or examples without retraining the base model","performance varies significantly based on text prompt quality and specificity","similarity scores are relative, not absolute — only meaningful when comparing multiple image-text pairs","text descriptions must be reasonably specific; vague queries produce unreliable rankings","no ability to weight different aspects of images (e.g., 'prioritize color over shape')","embedding space is fixed at model load time; cannot adapt to domain-specific similarity notions","context length is fixed at 77 tokens; longer text is silently truncated","vocabulary is fixed at 49,152 tokens; out-of-vocabulary words are handled by BPE subword splitting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=clip","compare_url":"https://unfragile.ai/compare?artifact=clip"}},"signature":"pp+LRt4Tahd4gtbAz4BU9gd398/Ao11SiaYdSiyH3KGSSw2M9dhff02+hSATYrmYEDpznAOAC2dwAzNsVIZOBA==","signedAt":"2026-06-21T19:40:59.399Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/clip","artifact":"https://unfragile.ai/clip","verify":"https://unfragile.ai/api/v1/verify?slug=clip","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"}}