apple/ml-mobileclip

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1627 stars · 130 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official implementation of Apple's MobileCLIP and MobileCLIP2, fast image-text contrastive (CLIP-style) models optimized for mobile latency. It includes inference, training, and evaluation code, pretrained checkpoints on HuggingFace, and an iOS demo app for real-time zero-shot image classification.

Use cases

• run zero-shot image classification on mobile devices
• compute image and text embeddings with a fast CLIP model
• train a lightweight CLIP model with multi-modal reinforced training
• evaluate image-text models on ImageNet zero-shot benchmarks
• deploy CLIP models on iPhone with low latency
• compare MobileCLIP accuracy against OpenAI CLIP and SigLIP

When to choose

• you need CLIP-style embeddings or zero-shot classification with low latency on mobile hardware
• you want small, efficient multimodal models with pretrained checkpoints
• you want to reproduce or extend MobileCLIP/MobileCLIP2 training

When to avoid

• you need the largest, highest-accuracy CLIP models regardless of latency
• you need non-PyTorch or non-Apple-ecosystem deployment support out of the box
• you need a general-purpose vision-language framework rather than specific model implementations

Facets

library · maturity active

machine-learning deep-learning image-processing nlp machine-learning computer-vision mobile-development python clip zero-shot-classification image-text-models mobile-inference multimodal apple natural-language-processing ios gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="apple/ml-mobileclip")