New Microsoft Vision Model has AMAZING TRICKS!!!
AI Summary
Summary: Microsoft’s Florence 2 Vision Model
- Introduction of Florence 2:
- Microsoft released a new vision language model named Florence 2.
- Despite its small size, it outperforms larger models in zero-shot tasks.
- Capabilities:
- Florence 2 is a generalist model capable of handling specialist tasks.
- It is designed to be a unified model for a variety of downstream vision tasks.
- Demonstration:
- Demonstrated using Hugging Face Spaces, showing the model’s ability to caption images at different levels of detail.
- The model can also perform object detection, OCR, and other tasks.
- Microsoft’s Vision:
- The model is part of a two-dimensional framework:
- Spatial hierarchy: Image level, region level, and pixel level tasks.
- Semantic granularity: No semantics, coarse semantics, and fine-grained semantics.
- Data Engine and Training:
- Florence 2 was trained using a dataset called FLDd 5B with 5.4 billion annotations from 126 million images.
- Annotations were initially provided by specialist models, followed by data filtering and enhancement.
- Architecture:
- Consists of an image encoder, multitask prompts, visual and text embeddings, and Transformer encoders/decoders.
- Can take text prompts as task instructions and generate text-based results.
- Performance:
- Florence 2 performs well against state-of-the-art models, including Google DeepMind’s Flamingo, in various benchmarks.
- Running the Model:
- Instructions provided for running Florence 2 on Google Colab with GPU support.
- Requires installation of specific libraries and loading of the model and processor.
- Users can input text prompts for different tasks and receive generated responses.
- Conclusion:
- Florence 2 is a versatile and efficient model suitable for a range of vision tasks and hobby projects.