🚀 ML Playground Dashboard An interactive Gradio app with mu
Identify objects in images based on text descriptions
it detects the multiple objects in between the image
Detect trash, bin, and hand in images
Detect objects in images using YOLO
Classify images based on text queries
Detect NSFW content in files
Check if an image contains adult content
Identify explicit images
Identify NSFW content in images
Filter images for adult content
Identify and segment objects in images using text
Testing Transformers JS
ML Playground Dashboard is an interactive Gradio app designed for exploring machine learning models. It provides a user-friendly interface to detect harmful or offensive content in images while allowing users to experiment with different models for text, images, and speech. This tool is perfect for developers and researchers looking to prototype and test ML solutions quickly.
• Model Exploration: Test and compare multiple ML models for image, text, and speech processing.
• Real-Time Detection: Identify harmful or offensive content in images using pre-trained models.
• Content Screening: Automatically flag and review potentially inappropriate material.
• Multi-Modal Support: Work with images, text, and speech in a single dashboard.
• Customization: Fine-tune model settings and thresholds for specific use cases.
• Interactive Demo: Experience a hands-on demonstration of ML capabilities.
pip install ml-playground-dashboard to install the Gradio app.ml-playground-dashboard in your terminal to start the app.What types of content can ML Playground Dashboard detect?
ML Playground Dashboard specializes in detecting harmful or offensive content in images, but it also supports text and speech analysis for similar purposes.
How do I customize the model settings?
You can adjust model thresholds and parameters directly through the dashboard interface. Experiment with different settings to optimize accuracy for your specific use case.
Is ML Playground Dashboard suitable for production use?
While it is primarily designed for prototyping and testing, you can deploy it in production environments by integrating it with your existing infrastructure and scaling solutions.