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Model Card for YOLOv8s Stock Market Real Time Pattern Detection from Live Screen Capture The YOLOv8s Stock Market Pattern Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect various chart patterns in real-time from screen-captured stock market trading data. The model aids traders and investors by automating the analysis of chart patterns, providing timely insights for informed decision-making. The model has been fine-tuned on a diverse dataset and achieves high accuracy in detecting and classifying stock market patterns in live trading scenarios. Model Description The YOLOv8s Stock Market Pattern Detection model enables real-time detection of crucial chart patterns within stock market screen captures. As stock markets evolve rapidly, this model's capabilities empower users with timely insights, allowing them to make informed decisions with speed and accuracy. The model is designed to work with screen capture of stock market trading charts. It can detect patterns such as 'Head and shoulders bottom,' 'Head and shoulders top,' 'MHead,' 'StockLine,' 'Triangle,' and 'WBottom.' Traders can optimize their strategies, automate trading decisions, and respond to market trends in real-time. To integrate this model into live trading systems or for customization inquiries, please contact us at [email protected]. - Developed by: FODUU AI - Model type: Object Detection - Task: Stock Market Pattern Detection from Screen Capture Direct Use The model can be used for real-time pattern detection on screen-captured stock market charts. It can log detected patterns, annotate detected images, save results in an Excel file, and generate a video of detected patterns over time. Downstream Use The model's real-time capabilities can be leveraged to automate trading strategies, generate alerts for specific patterns, and enhance overall trading performance. Training Data The Stock Market model was trained on a custom dataset consisting of 9000 training images and 800 validation images. Out-of-Scope Use The model is not designed for unrelated object detection tasks or scenarios outside the scope of stock market pattern detection from screen-captured data. - Performance may be affected by variations in chart styles, screen resolution, and market conditions. - Rapid market fluctuations and noise in trading data may impact accuracy. - Market-specific patterns not well-represented in the training data may pose challenges for detection. Recommendations Users should be aware of the model's limitations and potential biases. Testing and validation with historical data and live market conditions are advised before deploying the model for real trading decisions. To begin using the YOLOv8s Stock Market Real Time Pattern Detection model, install the necessary libraries: Screen Capture and Pattern Detection Implementation Model Contact For inquiries and contributions, please contact us at [email protected].

The YOLOv8s Table Detection model is an object detection model based on the YOLO (You Only Look Once) framework. It is designed to detect tables, whether they are bordered or borderless, in images. The model has been fine-tuned on a vast dataset and achieved high accuracy in detecting tables and distinguishing between bordered and borderless ones. Model Description The YOLOv8s Table Detection model serves as a versatile solution for precisely identifying tables within images, whether they exhibit a bordered or borderless design. Notably, this model's capabilities extend beyond mere detection – it plays a crucial role in addressing the complexities of unstructured documents. By employing advanced techniques such as bounding box delineation, the model enables users to isolate tables of interest within the visual content. What sets this model apart is its synergy with Optical Character Recognition (OCR) technology. This seamless integration empowers the model to not only locate tables but also to extract pertinent data contained within. The bounding box information guides the cropping of tables, which is then coupled with OCR to meticulously extract textual data, streamlining the process of information retrieval from unstructured documents. We invite you to explore the potential of this model and its data extraction capabilities. For those interested in harnessing its power or seeking further collaboration, we encourage you to reach out to us at [email protected]. Whether you require assistance, customization, or have innovative ideas, our collaborative approach is geared towards addressing your unique challenges. Additionally, you can actively engage with our vibrant community section for valuable insights and collective problem-solving. Your input drives our continuous improvement, as we collectively pave the way towards enhanced data extraction and document analysis. - Developed by: FODUU AI - Model type: Object Detection - Task: Table Detection (Bordered and Borderless) Furthermore, the YOLOv8s Table Detection model is not limited to table detection alone. It is a versatile tool that contributes to the processing of unstructured documents. By utilizing advanced bounding box techniques, the model empowers users to isolate tables within the document's visual content. What sets this model apart is its seamless integration with Optical Character Recognition (OCR) technology. The combination of bounding box information and OCR allows for precise data extraction from the tables. This comprehensive approach streamlines the process of information retrieval from complex documents. User collaboration is actively encouraged to enrich the model's capabilities. By contributing table images of different designs and types, users play a pivotal role in enhancing the model's ability to detect a diverse range of tables accurately. Community participation can be facilitated through our platform or by reaching out to us at [email protected]. We value collaborative efforts that drive continuous improvement and innovation in table detection and extraction. The YOLOv8s Table Detection model can be directly used for detecting tables in images, whether they are bordered or borderless. It is equipped with the ability to distinguish between these two categories. The model can also be fine-tuned for specific table detection tasks or integrated into larger applications for furniture recognition, interior design, image-based data extraction, and other related fields. The model is not designed for unrelated object detection tasks or scenarios outside the scope of table detection. The YOLOv8s Table Detection model may have some limitations and biases: - Performance may vary based on the quality, diversity, and representativeness of the training data. - The model may face challenges in detecting tables with intricate designs or complex arrangements. - Accuracy may be affected by variations in lighting conditions, image quality, and resolution. - Detection of very small or distant tables might be less accurate. - The model's ability to classify bordered and borderless tables may be influenced by variations in design. Users should be informed about the model's limitations and potential biases. Further testing and validation are advised for specific use cases to evaluate its performance accurately. To begin using the YOLOv8s Table Detection model, follow these steps: The model is trained on a diverse dataset containing images of tables from various sources. The dataset includes examples of both bordered and borderless tables, capturing different designs and styles. The training process involves extensive computation and is conducted over multiple epochs. The model's weights are adjusted to minimize detection loss and optimize performance. - [email protected] (box): - All: 0.962 - Bordered: 0.961 - Borderless: 0.963 The YOLOv8s architecture employs a modified CSPDarknet53 as its backbone, along with self-attention mechanisms and feature pyramid networks. These components contribute to the model's ability to detect and classify tables accurately, considering variations in size, design, and style. The model was trained and fine-tuned using a Jupyter Notebook environment. For inquiries and contributions, please contact us at [email protected].

Model Summary The Watermark Removal model is an image processing model based on neural networks. It is designed to remove watermarks from images while preserving the original image quality. The model utilizes an encoder-decoder structure with skip connections to maintain fine details during the watermark removal process. Model Description - Developed by: FODUU AI - Model type: Computer Vision - Image Processing - Task: Remove watermark from image Limitations and Considerations - Performance may vary depending on watermark complexity and opacity - Best results achieved with semi-transparent watermarks - Model trained on 256x256 images; performance may vary with different resolutions - GPU recommended for faster inference Training Details - Dataset: The model was trained on a custom dataset consisting of 20,000 images with watermarks in various styles and intensities. - Training Time: The model was trained for 200 epochs on an NVIDIA GeForce RTX 3060 GPU. - Loss Function: The model uses a combination of MSE (Mean Squared Error) and perceptual loss to optimize watermark removal quality. Model Evaluation The model has been evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) on a test set of watermarked images, achieving an average PSNR of 30.5 dB and an SSIM of 0.92. Software The model was trained on Jupyter Notebook environment. Model Card Contact For inquiries and contributions, please contact us at [email protected]

StyleShift - Cloth Swap / Dress Swap / Style Change / Outfit Swap This project showcases a Cloth Swap feature, leveraging the powerful capabilities of ComfyUI, a modular and flexible interface for AI workflows. This guide provides step-by-step instructions to set up, use, and contribute to the project. The primary objective is to provide a simple, effective, and customizable tool for tasks such as virtual try-ons, creative prototyping, and realistic clothing mockups. Whether you're an e-commerce platform, a fashion designer, or just experimenting with image manipulation, this project offers endless possibilities. go to ComfyUI/customnodes dir in terminal(cmd) and clone this repo: Note: ComfyUI requires Python 3.9 or above. Ensure all required dependencies are installed. Now Go to Manager ->-> Custom Nodes Manager and install this two nodes ComfyUI Layer Style and ComfyUICatVTONWrapper, restart and reload the page. Place the samvith4b8939.pth model inside ComfyUI/models/sams directory and groundingdinoswintogc.pth model in ComfyUI/models/grounding-dino directory if not download it. (If directory name not there in ComfyUI/models/ create new) - For Reference you can download model by below link: https://huggingface.co/foduucom/StyleShift-ClothSwap/resolve/main/samvith4b8939.pth https://huggingface.co/foduucom/StyleShift-ClothSwap/resolve/main/groundingdinoswintogc.pth Clone this Repository Clone the repository containing the Cloth Swap JSON workflows and assets: - Start ComfyUI (by running python3 main.py) - Open ComfyUI in your browser (default: http://127.0.0.1:8188) - Click on Load button in menu bar and select the workflow.json file provided in this repository - Now click on Queue Prompt for generate output or you can use by python script provided in this repository: - Prepare your input images (ensure proper resolution for better results) - Select the uploaded workflow in ComfyUI - Provide necessary inputs as per the workflow: - Source Image: The base image where the clothing is to be swapped - Cloth Image: The image of the clothing to be applied - Start the process to generate swapped outputs - Save the generated images for further use Why is this Useful? This project has a broad range of applications, making it useful across multiple industries and for personal use: - Virtual Try-On Technology: Revolutionize online shopping experiences by enabling customers to "try on" clothes digitally. Reduce product returns by providing a realistic preview of clothing fit and style. - Fashion Design and Prototyping: Help designers test their creations on various models without the need for physical samples or photoshoots. Quickly iterate designs and visualize the final product. For inquiries and contributions, please contact us at [email protected].