Ferret Description
An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.
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Pricing
Pricing Starts At:
Free
Pricing Information:
Open source
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
Apple
Year Founded:
1976
Headquarters:
United States
Website:
github.com/apple/ml-ferret
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
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