LLaVA 1.5: The opensource AI Vision Model
LLaVA 1.5 a collaborative effort by research teams at UC Davis and Microsoft, is a game-changer in the realm of image understanding and conversation.
LLaVA 1.5 combines a pre-trained visual encoder (CLIP ViT-L/14) with a large-scale language model (Vicuna). The two models are connected by a simple mapping matrix, which aligns or transforms visual and linguistic features so they can be manipulated in a unified space. This unique configuration allows LLaVA 1.5 to understand and discuss images, identify objects, and offer solutions to problems. It can even identify manipulated or photoshopped images, showcasing its advanced capabilities.
The model has been trained on 8 A100 GPUs with 80GB memory, using a similar set of hyperparameters as Vicuna in finetuning. This high-end hardware and sophisticated algorithms ensure its high performance and accuracy. The model has been tested against various images and has shown impressive results. For instance, when tested against a vegetable image, it correctly identified and counted the vegetables, outperforming other models like Bard.
However, it's not all roses. LLaVA 1.5's ability to write front-end code according to design is considered weaker, with its output being relatively crude. Despite this, it has achieved state-of-the-art results on 11 benchmarks, utilizing merely 1.2M public data, and surpasses methods that use billion-scale data
LLaVA 1.5 is a free open-source model hosted on HuggingFace, making it accessible to anyone interested in AI and machine learning. This open-source nature encourages collaboration and innovation, allowing researchers and developers worldwide to contribute to its development and improvement.
One of the key features of LLaVA 1.5 is its multimodal chat ability. It can understand and discuss images, identify objects, and offer solutions to problems. It can even identify manipulated or photoshopped images. This makes it an excellent tool for various fields that require image understanding and discussion, object identification, and problem-solving. This could include areas like surveillance, autonomous vehicles, robotics, healthcare, and more.
Despite its impressive performance, LLaVA 1.5 does have its limitations. It struggles with translation tasks, indicating that it may not be as effective in tasks involving language translation. However, it excels in role-playing tasks, where it follows instructions effectively, making it more interactive and engaging.
In a comparison with GPT-4V and LLaVA, LLaVA 1.5 was the only model to correctly interpret a picture, demonstrating its superior image understanding capabilities. Despite its simpler architecture, it only requires 1.2 million public data, surpassing models like Qwen-VL and HuggingFace IDEFICS, which use 1.45 billion and 130 million data respectively.
In conclusion, LLaVA 1.5 is a powerful AI vision model that has shown impressive performance in image understanding and discussion. While it does have its limitations, its strengths far outweigh them, making it a promising tool in the field of AI and machine learning. As AI continues to evolve, we can expect to see even more improvements and advancements in models like LLaVA 1.5.
LLaVa 2 demo can be found here : LLaVA (hliu.cc)
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