Picture this: an intelligent robot needs to find items in your messy room. Conventional object detectors can only find things they've already learned, such as cups or chairs. It will be a disaster! We need a way for robots to find things they've never seen before.
Yolo World ModelAdvanced real-time Ultralyticsyolov8 - an advanced real-time method based on open vocabulary detection tasks is introduced. This innovation can detect any object in an image based on descriptive text. As you can see in the image below, you indicate your nose, eyes, and tongue, and the world model will give you the corresponding positions.
Whereas, EfficientSAM is a lightweight fast SAM model with good performance and 20x faster inference speed compared to SAM! The parameters are reduced by 20 times! The combination of the two has even greater potential!
Yolo World ModelYOLO - Traditional open-ended vocabulary detection models often rely on cumbersome deformer models that require a lot of computational resources. The dependence of these models on predefined object categories also limits their usefulness in dynamic scenarios. Yolo-World reinvigorates the YOLO8 framework with open vocabulary detection, using visual language modeling and pre-training on a large dataset to recognize a large number of objects with unmatched efficiency at zero scenes**.
Leveraging the computational speed of CNNs, Yolo-World provides a fast and open vocabulary detection solution that meets the needs of various industries for instant results.
Yolo-World was introduced"Prompt first, then detect"strategies to further improve efficiency with offline vocabulary. This approach simplifies the detection process by using pre-computed custom prompts, including headings or categories, and encoding and storing them as offline lexical embeddings.
efficientsamThe core idea of the EfficientSAM model is to use the masking mechanism to mask certain areas in the image, so that the model pays more attention to the unmasked parts during the training process. The purpose of this is to improve the generalization ability of the model so that it can better adapt to different tasks and data distributions. In addition, this masking mechanism can also significantly reduce the computational burden of the model, thereby improving the training efficiency.
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