Monkey
Image captioner
An end-to-end image captioning system that uses large multi-modal models and provides tools for training, inference, and demo usage.
【CVPR 2024 Highlight】Monkey (LMM): Image Resolution and Text Label Are Important Things for Large Multi-modal Models
2k stars
22 watching
132 forks
Language: Python
last commit: 3 months ago Related projects:
Repository | Description | Stars |
---|---|---|
| A framework for grounding language models to images and handling multimodal inputs and outputs | 478 |
| A multi-modal language model that integrates image, video, audio, and text data to improve language understanding and generation | 1,568 |
| An evaluation benchmark for OCR capabilities in large multmodal models. | 484 |
| A multi-modal large language model that integrates natural language and visual capabilities with fine-tuning for various tasks | 73 |
| A family of large multimodal models supporting multimodal conversational capabilities and text-to-image generation in multiple languages | 1,098 |
| Develops large multimodal models for high-resolution understanding and analysis of text, images, and other data types. | 143 |
| Develops a multimodal task and dataset to assess vision-language models' ability to handle interleaved image-text inputs. | 33 |
| A framework for training GPT4-style language models with multimodal inputs using large datasets and pre-trained models | 231 |
| Evaluates the capabilities of large multimodal models using a set of diverse tasks and metrics | 274 |
| A framework to build versatile Multimodal Large Language Models with synergistic comprehension and creation capabilities | 402 |
| A framework that enables large language models to process and understand multimodal inputs from various sources such as images and speech. | 308 |
| An implementation of a multimodal language model with capabilities for comprehension and generation | 585 |
| Evaluates and benchmarks multimodal language models' ability to process visual, acoustic, and textual inputs simultaneously. | 15 |
| An end-to-end trained model capable of generating natural language responses integrated with object segmentation masks for interactive visual conversations | 797 |
| A framework for large-scale cross-modal benchmarks and vision-language tasks in Chinese | 157 |