AI正在发生 未来正在更新Florence-2-large-no-flash-attn
⚠️ This is a modified version of Florence 2 that modifies the custom `modeling_florence2.py` file to remove the need for installing `flash-attn` package (by hijacking the flash-attn methods and replacing with regular attention). It probably has impact in performance. This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.
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⚠️ This is a modified version of Florence 2 that modifies the custom `modeling_florence2.py` file to remove the need for installing `flash-attn` package (by hijacking the flash-attn methods and replacing with regular attention). It probably has impact in performance. This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.