{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-open-clip-torch","slug":"pypi-open-clip-torch","name":"open-clip-torch","type":"repo","url":"https://pypi.org/project/open-clip-torch/","page_url":"https://unfragile.ai/pypi-open-clip-torch","categories":["model-training"],"tags":["pytorch","clip","image-text","language-image","multimodal"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-open-clip-torch__cap_0","uri":"capability://data.processing.analysis.contrastive.language.image.embedding.generation","name":"contrastive language-image embedding generation","description":"Generates aligned embedding vectors for images and text using a contrastive learning framework that maximizes similarity between matched image-text pairs while minimizing similarity for unmatched pairs. Implements the CLIP architecture with dual encoders (vision transformer for images, text transformer for captions) trained via NT-Xent loss, enabling zero-shot classification and semantic search across modalities without task-specific fine-tuning.","intents":["I need to embed images and text into a shared semantic space for cross-modal retrieval","I want to perform zero-shot image classification without retraining on new categories","I need to find semantically similar images given a text query or vice versa","I want to measure semantic similarity between images and text descriptions"],"best_for":["computer vision researchers implementing multimodal models","teams building semantic search systems across images and text","developers creating zero-shot classification pipelines","organizations needing open-source alternatives to proprietary CLIP APIs"],"limitations":["Embedding quality depends heavily on training data distribution — models trained on LAION may have biases present in that dataset","Inference requires loading both vision and text encoders into memory (~1-2GB for ViT-B/32 models)","No built-in batch processing optimization for very large image collections (>1M images) without external distributed frameworks","Text encoder limited to ~77 token context window, truncating longer descriptions"],"requires":["PyTorch 1.9+","Python 3.7+","CUDA 11.0+ for GPU acceleration (CPU inference supported but slow)","Pretrained model weights (auto-downloaded or manually provided)"],"input_types":["image (PIL Image, numpy array, or tensor)","text (string or tokenized tensor)","batch of images and texts"],"output_types":["embedding vectors (float32 tensors, typically 512-1024 dimensions)","similarity scores (cosine similarity matrices)","logits for classification"],"categories":["data-processing-analysis","multimodal-embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_1","uri":"capability://image.visual.zero.shot.image.classification.via.text.prompts","name":"zero-shot image classification via text prompts","description":"Classifies images into arbitrary categories by encoding candidate class names as text and computing similarity scores against image embeddings, without requiring any labeled training data for new classes. Uses the pretrained CLIP embeddings to rank classes by relevance, supporting both single-label and multi-label classification through threshold-based or top-k selection strategies.","intents":["I want to classify images into custom categories without collecting labeled training data","I need to quickly evaluate if an image belongs to a set of predefined classes using natural language descriptions","I want to perform open-vocabulary object detection or scene understanding","I need to adapt classification to new domains without retraining"],"best_for":["rapid prototyping teams testing classification hypotheses","organizations with limited labeled data for niche domains","systems requiring dynamic category addition at runtime","researchers benchmarking transfer learning capabilities"],"limitations":["Performance degrades with ambiguous or very specific class names — requires careful prompt engineering","No explicit ranking or confidence calibration — similarity scores don't directly map to probability","Struggles with fine-grained distinctions (e.g., dog breeds) compared to supervised models trained on those categories","Requires encoding all candidate classes upfront — inefficient for very large label spaces (>10k classes)"],"requires":["PyTorch 1.9+","Pretrained CLIP model loaded in memory","Text tokenizer compatible with model (included in package)"],"input_types":["image (PIL Image, numpy array, tensor)","list of class name strings or longer text descriptions"],"output_types":["similarity scores per class (float tensor)","predicted class index or name","confidence scores (optionally normalized)"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_10","uri":"capability://automation.workflow.model.export.and.quantization.for.deployment","name":"model export and quantization for deployment","description":"Exports trained CLIP models to deployment-friendly formats (ONNX, TorchScript) with optional quantization (int8, fp16) to reduce model size and inference latency. Handles model conversion, weight quantization, and format validation to ensure exported models produce identical outputs to the original PyTorch models.","intents":["I want to deploy CLIP models on edge devices with limited memory","I need to reduce model size for faster downloads and inference","I want to export models to ONNX for cross-platform compatibility","I need to quantize models without significant accuracy loss"],"best_for":["teams deploying CLIP to mobile or edge devices","practitioners optimizing inference latency","organizations reducing model serving costs","developers building cross-platform applications"],"limitations":["Quantization can reduce accuracy — int8 quantization may lose 1-5% accuracy depending on model","ONNX export requires careful operator support — some PyTorch operations may not have ONNX equivalents","Exported models are harder to debug — limited introspection compared to PyTorch","Format conversion adds complexity — requires testing to ensure numerical equivalence"],"requires":["PyTorch 1.9+","ONNX runtime (optional, for ONNX export)","TorchScript support (built into PyTorch)","Quantization libraries (torch.quantization)"],"input_types":["trained PyTorch CLIP model","optional: quantization configuration (bit width, calibration data)","optional: export format specification"],"output_types":["exported model file (ONNX, TorchScript, or quantized PyTorch)","model metadata (input/output shapes, quantization parameters)","validation report (numerical equivalence check)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_11","uri":"capability://data.processing.analysis.multimodal.dataset.loading.and.preprocessing.pipeline","name":"multimodal dataset loading and preprocessing pipeline","description":"Loads image-text datasets from multiple formats (CSV, JSON, directory structures) with automatic validation, deduplication, and filtering. Implements efficient data loading with prefetching, caching, and augmentation applied on-the-fly during training, supporting both local and cloud storage backends (S3, GCS).","intents":["I want to load my custom image-text dataset without manual preprocessing","I need to validate dataset quality and detect missing or corrupted files","I want to apply augmentation efficiently during training","I need to handle datasets stored in cloud storage (S3, GCS)"],"best_for":["practitioners preparing custom datasets for fine-tuning","researchers studying data quality effects on CLIP","teams managing large-scale image-text collections","organizations with datasets in cloud storage"],"limitations":["Dataset validation is slow — checking all files can take hours for large datasets","Cloud storage access adds latency — slower than local SSD storage","No automatic caption quality assessment — requires manual review for noisy data","Deduplication is approximate — exact deduplication requires expensive image hashing"],"requires":["PyTorch 1.9+","Dataset files (images + captions)","Optional: cloud storage credentials (AWS, GCP)","Sufficient disk space for caching"],"input_types":["dataset manifest (CSV, JSON, or directory structure)","image files (JPEG, PNG, WebP)","text captions (strings, one per image)","optional: metadata (labels, attributes)"],"output_types":["PyTorch DataLoader with batched samples","dataset statistics (size, format distribution)","validation report (missing files, corrupted images)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_2","uri":"capability://search.retrieval.image.text.similarity.scoring.and.ranking","name":"image-text similarity scoring and ranking","description":"Computes cosine similarity between image and text embeddings to rank images by relevance to a query or vice versa. Implements efficient batch similarity computation using matrix multiplication, supporting both single-query and multi-query scenarios with optional temperature scaling for calibrated confidence scores.","intents":["I want to find the most relevant images for a text query from a large collection","I need to rank candidate captions for an image by semantic relevance","I want to measure how well a text description matches an image","I need to build a semantic search index for image-text retrieval"],"best_for":["search and recommendation system builders","content moderation teams evaluating image-caption alignment","researchers studying multimodal semantic understanding","e-commerce platforms implementing visual search"],"limitations":["Similarity scores are relative, not absolute — require normalization or temperature scaling for meaningful thresholds","Batch computation requires all embeddings in memory — scales poorly beyond ~100k images without external vector databases","No ranking diversity — returns most similar items without novelty or diversity constraints","Sensitive to embedding quality — garbage embeddings produce meaningless similarity scores"],"requires":["Precomputed image and text embeddings (or ability to generate them)","PyTorch for efficient batch matrix operations","Optional: vector database (Faiss, Milvus, Pinecone) for large-scale retrieval"],"input_types":["image embeddings (float32 tensors, shape [N, embedding_dim])","text embeddings (float32 tensors, shape [M, embedding_dim])","optional: temperature parameter for scaling"],"output_types":["similarity matrix (float32 tensor, shape [N, M])","ranked indices and scores","top-k results with scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_3","uri":"capability://tool.use.integration.pretrained.model.loading.and.inference.with.multiple.architectures","name":"pretrained model loading and inference with multiple architectures","description":"Loads pretrained CLIP models from multiple sources (OpenAI, OpenCLIP, HuggingFace) with support for various vision backbones (ViT-B/32, ViT-L/14, ResNet50, ConvNeXt) and text encoders, handling model weight downloading, caching, and device placement (CPU/GPU). Provides a unified inference interface that abstracts architecture differences and handles tokenization, image preprocessing, and embedding computation.","intents":["I want to quickly load a pretrained CLIP model without manually downloading weights","I need to experiment with different model architectures to find the best accuracy-speed tradeoff","I want to run inference on GPU or CPU depending on available hardware","I need to use models from different sources (OpenAI, OpenCLIP) with a consistent API"],"best_for":["practitioners prototyping multimodal systems quickly","researchers comparing model architectures and training datasets","teams deploying CLIP in resource-constrained environments","developers integrating CLIP into production pipelines"],"limitations":["Model weights are large (300MB-1GB+) — first load requires significant download bandwidth","No automatic model quantization — full precision models consume substantial GPU memory","Limited built-in caching strategy — repeated model loads may hit disk I/O bottlenecks","Device placement is manual — no automatic fallback from GPU to CPU on OOM"],"requires":["PyTorch 1.9+","Python 3.7+","Internet connection for initial model download (or local weight files)","Sufficient disk space (~2GB for multiple model variants)","CUDA 11.0+ for GPU inference (optional)"],"input_types":["model name string (e.g., 'ViT-B/32', 'ViT-L/14@336')","optional: pretrained weights path","optional: device specification ('cuda', 'cpu')"],"output_types":["loaded PyTorch model (vision encoder + text encoder)","tokenizer for text preprocessing","image preprocessing pipeline"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_4","uri":"capability://code.generation.editing.fine.tuning.on.custom.image.text.datasets","name":"fine-tuning on custom image-text datasets","description":"Enables training CLIP models on custom datasets using contrastive loss (NT-Xent) with support for distributed training across multiple GPUs/TPUs via PyTorch DistributedDataParallel. Handles data loading, augmentation, mixed precision training, and gradient accumulation to optimize for different hardware configurations and dataset sizes.","intents":["I want to adapt CLIP to my domain-specific images and captions","I need to improve zero-shot performance on niche categories by fine-tuning","I want to train a CLIP model on proprietary data without using external APIs","I need to optimize model size and speed for deployment while maintaining accuracy"],"best_for":["organizations with proprietary image-text datasets","researchers studying domain adaptation in multimodal models","teams building specialized search systems for niche domains","practitioners optimizing CLIP for edge deployment"],"limitations":["Requires substantial compute — training on large datasets (>1M images) needs multiple GPUs and weeks of training","Data quality is critical — poor captions or misaligned pairs degrade embeddings significantly","No automatic hyperparameter tuning — requires manual experimentation with learning rate, batch size, warmup","Distributed training adds complexity — requires careful synchronization and gradient aggregation across devices"],"requires":["PyTorch 1.9+","CUDA 11.0+ for GPU training","Custom dataset with image-text pairs (CSV, JSON, or directory structure)","Multiple GPUs recommended (single GPU training is very slow)","Sufficient disk space for dataset and checkpoints"],"input_types":["image files (JPEG, PNG, WebP)","text captions (strings, one per image)","optional: metadata (labels, attributes)"],"output_types":["fine-tuned model weights (PyTorch checkpoint)","training logs (loss curves, validation metrics)","updated embeddings for custom dataset"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_5","uri":"capability://data.processing.analysis.batch.image.preprocessing.and.augmentation","name":"batch image preprocessing and augmentation","description":"Applies standardized image preprocessing (resizing, normalization, center cropping) and optional augmentation (random crops, flips, color jitter) to prepare images for CLIP encoders. Implements efficient batched operations using torchvision transforms and supports multiple image formats (PIL, numpy, tensor) with automatic format conversion and device placement.","intents":["I want to prepare images consistently for CLIP inference without manual preprocessing","I need to apply data augmentation during training to improve model robustness","I want to handle images of different sizes and formats in a batch","I need to optimize preprocessing for speed without sacrificing quality"],"best_for":["practitioners building inference pipelines","researchers studying data augmentation effects on CLIP","teams processing large image collections","developers integrating CLIP into production systems"],"limitations":["Preprocessing is model-specific — different architectures may require different image sizes (224x224 vs 336x336)","Augmentation is applied deterministically during inference — no stochastic augmentation for uncertainty estimation","Batch processing requires all images to fit in memory — very large images may cause OOM","No adaptive preprocessing — doesn't adjust for image quality or content characteristics"],"requires":["PyTorch 1.9+","torchvision for image transforms","PIL or numpy for image loading","CUDA 11.0+ for GPU-accelerated preprocessing (optional)"],"input_types":["image files (JPEG, PNG, WebP, BMP)","PIL Image objects","numpy arrays (uint8 or float32)","PyTorch tensors"],"output_types":["preprocessed image tensors (float32, normalized to [-1, 1] or [0, 1])","batched tensors ready for model inference"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_6","uri":"capability://text.generation.language.text.tokenization.and.encoding.with.context.window.management","name":"text tokenization and encoding with context window management","description":"Tokenizes text descriptions into token IDs compatible with CLIP's text encoder, handling vocabulary mapping, special tokens (BOS, EOS, padding), and context window truncation (77 tokens for standard CLIP). Supports batch tokenization with automatic padding to uniform length and optional token masking for variable-length sequences.","intents":["I want to convert text descriptions into token IDs for CLIP's text encoder","I need to handle variable-length text inputs with consistent batching","I want to understand how CLIP tokenizes and truncates long descriptions","I need to implement custom text preprocessing before tokenization"],"best_for":["developers building text-to-image search systems","researchers studying CLIP's text understanding","practitioners implementing prompt engineering workflows","teams handling multilingual or domain-specific text"],"limitations":["Fixed context window (77 tokens) truncates long descriptions — no support for longer sequences","Tokenizer is model-specific — different models may use different vocabularies","No built-in prompt templating — requires manual prompt engineering for best results","Limited multilingual support — primarily trained on English text"],"requires":["PyTorch 1.9+","Tokenizer weights (included with model)","Text input as strings"],"input_types":["text string or list of strings","optional: maximum sequence length","optional: padding strategy"],"output_types":["token IDs (int64 tensor, shape [batch_size, max_length])","attention masks (optional, for variable-length sequences)","token count per sequence"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_7","uri":"capability://memory.knowledge.embedding.caching.and.efficient.batch.inference","name":"embedding caching and efficient batch inference","description":"Caches computed embeddings to avoid redundant inference on repeated images or text, implementing in-memory caching with optional disk persistence. Supports efficient batch inference by processing multiple images/texts in parallel, with configurable batch sizes and memory management to balance speed and resource usage.","intents":["I want to avoid recomputing embeddings for images I've already processed","I need to efficiently embed large image collections without running out of memory","I want to speed up repeated queries on the same dataset","I need to persist embeddings across sessions for faster startup"],"best_for":["teams building production search systems with repeated queries","researchers processing large datasets with limited compute","practitioners optimizing inference latency","systems requiring fast response times on cached data"],"limitations":["In-memory caching requires sufficient RAM — large collections (>1M images) need external vector databases","Cache invalidation is manual — no automatic detection of model updates","Disk persistence adds I/O overhead — may be slower than recomputing for small datasets","No distributed caching — single-machine caching doesn't scale across multiple servers"],"requires":["PyTorch 1.9+","Sufficient RAM for in-memory cache (or disk space for persistent cache)","Optional: vector database (Faiss, Milvus) for large-scale caching"],"input_types":["image or text inputs (same as inference)","cache key (hash of input)","optional: cache directory for disk persistence"],"output_types":["cached embeddings (float32 tensors)","cache hit/miss indicators","cache statistics (size, hit rate)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_8","uri":"capability://planning.reasoning.model.evaluation.and.benchmark.reporting","name":"model evaluation and benchmark reporting","description":"Evaluates CLIP models on standard benchmarks (ImageNet, CIFAR-10, Flickr30K) using zero-shot classification and image-text retrieval metrics. Computes accuracy, recall@k, mean reciprocal rank, and other standard metrics with support for custom evaluation datasets and detailed per-class performance analysis.","intents":["I want to benchmark my fine-tuned CLIP model against standard datasets","I need to compare performance across different model architectures","I want to understand where my model fails (per-class analysis)","I need to report reproducible results for research papers"],"best_for":["researchers publishing CLIP variants","teams comparing model architectures","practitioners validating fine-tuned models","organizations tracking model performance over time"],"limitations":["Benchmark evaluation is slow — evaluating on large datasets (>100k images) takes hours","Standard benchmarks may not reflect real-world performance on niche domains","No automatic hyperparameter tuning — requires manual threshold selection for classification","Limited to predefined metrics — custom metrics require manual implementation"],"requires":["PyTorch 1.9+","Benchmark datasets (auto-downloaded or manually provided)","Sufficient compute for evaluation (GPU recommended)"],"input_types":["loaded CLIP model","benchmark dataset (images + labels or captions)","optional: custom evaluation configuration"],"output_types":["accuracy scores (overall and per-class)","recall@k metrics","detailed evaluation report (JSON or CSV)","confusion matrices and error analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-open-clip-torch__cap_9","uri":"capability://automation.workflow.distributed.training.with.gradient.synchronization","name":"distributed training with gradient synchronization","description":"Implements distributed training across multiple GPUs or TPUs using PyTorch DistributedDataParallel, handling gradient synchronization, loss aggregation, and checkpoint saving across devices. 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