Update README.md

README.md

@@ -11,19 +11,19 @@ pipeline_tag: object-detection
 
 ## Overview:
 
-AISAK-Visual, part of the AISAK system, is a pretrained model for image captioning based on the BLIP framework. Altered by the AISAK team from the https://huggingface.co/Salesforce/blip-image-captioning-large model, this model utilizes a ViT base backbone for unified vision-language understanding and generation.
+AISAK-Detect is an integral component of the AISAK-Visual system, specializing in object detection tasks. Leveraging an encoder-decoder transformer architecture with a convolutional backbone, AISAK-Detect excels in accurately and efficiently detecting objects within images. This model enhances the image understanding capabilities of AISAK-Visual, contributing to comprehensive visual analysis. Trained and fine-tuned by the AISAK team, AISAK-Detect is designed to seamlessly integrate into the broader AISAK system, ensuring cohesive performance in image analysis tasks.
 
 ## Model Information:
 
 - **Model Name**: AISAK-Visual
 - **Version**: 2.0
-- **Model Architecture**: Transformer with ViT base backbone
-- **Specialization**: AISAK-Visual is part of the broader AISAK system and is specialized in image captioning tasks.
+- **Model Architecture**: Transformer with convolutional backbone
+- **Specialization**: AISAK-Detect is a specialized model within the AISAK-Visual system, focusing on object detection tasks. It employs an encoder-decoder transformer architecture with a convolutional backbone, enabling it to effectively analyze images and generate precise object detection results. AISAK-Visual is part of the broader AISAK system and is specialized in image captioning tasks.
 
 ## Intended Use:
 
-AISAK-Visual, as part of AISAK, is designed to provide accurate and contextually relevant captions for images. Whether used for conditional or unconditional image captioning tasks, AISAK-Visual offers strong performance across various vision-language understanding and generation tasks.
-
+The model demonstrates high accuracy in object detection tasks, leveraging the synergy between its transformer-based encoder-decoder architecture and the convolutional backbone. When utilized in conjunction with AISAK-Visual, it enhances overall performance in image analysis tasks.  
+  
 ## Performance:
 
 AISAK-Visual, based on the BLIP framework, achieves state-of-the-art results on image captioning tasks, including image-text retrieval, image captioning, and VQA. Its generalization ability is demonstrated by its strong performance on video-language tasks in a zero-shot manner.
@@ -35,22 +35,21 @@ AISAK-Visual, based on the BLIP framework, achieves state-of-the-art results on
 
 ## Limitations:
 
-- While AISAK-Visual demonstrates proficiency in image captioning tasks, it may not be suitable for tasks requiring domain-specific knowledge.
-- Performance may vary when presented with highly specialized or out-of-domain images.
+- While proficient in general object detection, AISAK-Detect may encounter challenges in scenarios requiring specialized object recognition or highly cluttered images.
+- Users should be aware of these limitations and consider them when interpreting the model's outputs.
 
 ## Deployment:
 
-Inferencing for AISAK-Visual will be handled as part of the full deployment of the AISAK system in the future. The process is lengthy and intensive in many areas, emphasizing the goal of achieving the optimal system rather than the quickest. However, work is being done as fast as humanly possible. Updates will be provided as frequently as possible.
-
+AISAK-Detect's inferencing capabilities will be seamlessly integrated into the deployment of the AISAK-Visual system. This integration ensures smooth operation and maximizes the synergy between the two models, providing comprehensive image understanding and analysis.
 
 ## Caveats:
 
-- Users should verify important decisions based on AISAK-Visual's image captions, particularly in critical or high-stakes scenarios.
+- Users should verify critical decisions based on AISAK-Detect's object detection results, particularly in high-stakes scenarios. Considering the broader context provided by AISAK-Visual is essential for a comprehensive understanding of visual content and informed decision-making.
 
 ## Model Card Information:
 
-- **Model Card Created**: February 1, 2024
-- **Last Updated**: February 19, 2024
+- **Model Card Created**: April 25, 2024
+- **Last Updated**: April 25, 2024
 - **Contact Information**: For any inquiries or communication regarding AISAK, please contact me at mandelakorilogan@gmail.com.