CLIP-ViT-H-14-laion2B-s32B-b79K

Name: CLIP-ViT-H-14-laion2B-s32B-b79K
Author: laion

by laion

OpenCLIP ViT-H/14 trained on LAION-2B — the high-accuracy open CLIP checkpoint for visual search

419Kdl/month

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Identifier

Model ID

laion/CLIP-ViT-H-14-laion2B-s32B-b79K

Overview

This is the OpenCLIP ViT-H/14 model trained on the English LAION-2B image-text dataset (32B samples seen). It is one of the most downloaded open CLIP checkpoints and a strong, fully-open alternative to OpenAI CLIP, reaching roughly 78% zero-shot ImageNet top-1 — well above the original ViT-L/14.

On Mixpeek, CLIP-ViT-H/14 is a visual embedding extractor: it maps images and video frames into a 1024-dimensional space shared with text, so an agent can search a media library with natural-language queries. Its higher accuracy makes it a good default when retrieval quality matters more than latency.

Architecture

Vision Transformer Huge (ViT-H/14): 32 layers, 1280-dim width, 14x14 patches, paired with a text transformer. Both encoders project into a shared 1024-dim space trained with contrastive (InfoNCE) loss on LAION-2B.

Key Capabilities

•1024-dim image+text embeddings in a shared space
•Strong zero-shot classification (~78% ImageNet) and cross-modal retrieval
•Fully open weights and training data (LAION-2B)
•Drop-in OpenCLIP API, widely supported

Use Cases on Mixpeek

•Natural-language visual search over image and video-frame libraries
•Zero-shot tagging and content moderation without per-class training
•Higher-accuracy alternative to CLIP ViT-L when recall quality matters
•Keyframe embeddings feeding a reranker for precise media retrieval

Use CLIP-ViT-H-14-laion2B-s32B-b79K on Mixpeek

Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

Open Studio

How It Runs on Mixpeek

On Mixpeek, CLIP-ViT-H-14-laion2B-s32B-b79K runs as a managed extractor inside a processing pipeline. Point a bucket of zero shot image classification data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.