Home › Vision Models › Shap-E

Shap-E

Shap-E is an official GitHub repository by OpenAI for generating 3D implicit functions conditioned on text or images. It provides sample notebooks and usage instructions for converting text prompts or images into 3D models, making it a practical tool for generating 3D objects.

Key Information

  • Category: Vision Models
  • Source: Github
  • Tags: Python
  • Last updated: January 09, 2026

Structured Metrics

No structured metrics captured yet.

Links

Visit official website

Canonical source: https://github.com/openai/shap-e

Related Tools

BLIP-2

BLIP-2 is an advanced visual-language model that allows zero-shot image-to-text generation, enabling tasks such as image captioning and visual question answering using a combination of pretrained vision and language models.
deepfake-detector-model-v1

A deepfake detection image classification model fine-tuned from google/siglip2-base-patch16-512. It leverages the SiglipForImageClassification architecture to classify images as either 'fake' (deepfakes) or 'real', and is intended for applications such as media authentication, content moderation, forensic analysis, and security.
DeepSeek-OCR

DeepSeek-OCR is an open-weight, multilingual vision-language OCR model from DeepSeek that converts images/documents to text (e.g., Markdown) using a context-aware optical compression approach. It runs via Transformers and vLLM, supports FlashAttention, and is provided as BF16 safetensors (~3B params) with example inference code and broad community adoption.
DeepSeek-VL2

A series of advanced vision-language models designed for multimodal understanding, available in multiple sizes to suit varying complexity and performance requirements.
DeepSeek-VL2-small

DeepSeek-VL2-small is a variant of the DeepSeek-VL2 series, advanced mixture-of-experts vision-language models designed for multimodal tasks such as visual question answering, optical character recognition, document/table/chart understanding, and visual grounding.
Florence-2-large

An advanced vision foundation model by Microsoft designed for a wide range of vision and vision-language tasks such as captioning, object detection, OCR, and segmentation. It uses a prompt-based, sequence-to-sequence transformer architecture pretrained on the FLD-5B dataset and supports both zero-shot and finetuned settings.