readme.rd

Human Action Recognition Model - 15 Actions
This model is designed to recognize 15 different human actions in images using a Convolutional Neural Network (CNN). Built with deep learning techniques, the model can analyze an image and classify it into one of the supported actions, making it suitable for applications in video surveillance, content analysis, and interactive systems.

Supported Actions
The model can recognize the following 15 human actions:

Calling - Recognizes the act of making a phone call, typically with a phone near the ear.
Clapping - Identifies clapping gestures, often with visible hands together.
Cycling - Detects individuals in a cycling posture, usually on a bicycle.
Dancing - Recognizes various dance movements, typically expressive or rhythmic.
Drinking - Detects the act of drinking, often with a visible bottle or glass.
Eating - Recognizes eating actions, usually with food or utensils in hand.
Fighting - Identifies physical confrontations or aggressive postures.
Hugging - Recognizes people embracing each other.
Laughing - Detects facial expressions and body language associated with laughter.
Listening to Music - Recognizes individuals with headphones or an attentive posture, likely listening to music.
Running - Identifies running motion, with typical forward-leaning posture.
Sitting - Recognizes individuals in a seated position.
Sleeping - Detects people lying down in a relaxed or sleeping posture.
Texting - Recognizes the action of texting, often with a phone held in both hands.
Using Laptop - Identifies individuals using a laptop or similar device.
How It Works
The model utilizes a Convolutional Neural Network (CNN) architecture, which captures spatial features in images. Trained on a dataset containing images labeled with each of the actions, the model learns to distinguish between different human poses, gestures, and activities.

Applications
This typed of model can be applied in a variety of fields, including:

Video Surveillance: Monitoring and classifying human actions for security and behavioral analysis.
Content Moderation: Automatically tagging or categorizing images and videos based on detected human actions.
Human-Computer Interaction: Enabling interaction-based applications where responses vary based on recognized actions.
Social Media Analysis: Detecting and classifying activities for content insights, moderation, or categorization.
Instructions
Upload an image featuring one of the supported actions.
The model will output the predicted action from the list of 15 categories.
Limitations
This model performs best on clear, well-lit images with the subject prominently visible. Performance may vary on images with multiple people, cluttered backgrounds, or ambiguous actions. Additionally, actions outside the supported list may yield incorrect predictions.