Skip to main content

A lightning fast and insanely accurate agentic object detection system

Project description

AI Scout

AI Scout is a flexible object detection system that combines YOLO with LLMs for intelligent object identification and analysis.

Quick Start

import os
from aiscout import Scout
from aiscout.providers.anthropic import LLM

# Initialize with your preferred LLM
api_key = os.getenv("ANTHROPIC_API_KEY")
llm = LLM(api_key=api_key, model="claude-3-7-sonnet-20250219")
scout = Scout(llm=llm)

# Run detection
result = scout.detect(
    "path/to/image.jpg",
    target_list=["target1", "target2"],
    confidence_threshold=0.2
)

# Save annotated image
result["annotated_image"].save("output.jpg")

Features

  • Combines YOLO and LLM capabilities for enhanced object detection
  • Supports multiple LLM providers (Anthropic Claude, OpenAI GPT-4V)
  • Advanced prompt customization and management
  • Iterative refinement with configurable iterations
  • Debug mode for development
  • Flexible target specification
  • Provider-agnostic interface

Requirements

  • Python >=3.9
  • ultralytics (YOLO)
  • requests
  • rich
  • Anthropic API key (for Claude) or OpenAI API key (for GPT-4V)

Installation

pip install aiscout

Configuration

Set your API key as an environment variable:

# For Anthropic Claude
export ANTHROPIC_API_KEY="your_api_key"
# For OpenAI
export OPENAI_API_KEY="your_api_key"

Advanced Usage

# Enable debug mode
scout = Scout(llm=llm, debug_mode=True)

# Configure detection parameters
result = scout.detect(
    "image.jpg",
    target_list=["target1", "target2"],
    confidence_threshold=0.2,
    min_iterations=3,
    max_iterations=6
)

Prompt Customization

from aiscout.prompts import prompt_manager

# Replace entire prompt
prompt_manager.set_prompt(
    "identify_objects",
    """Analyze this image and identify objects with these requirements:
1. Focus on vehicles and traffic signs
2. Identify make and model when possible
3. Note any safety hazards"""
)

# Append additional instructions
prompt_manager.append_to_prompt(
    "analyze_targets",
    "Additional requirement: Prioritize specific vehicle types over generic classes"
)

# Reset prompts
prompt_manager.reset_prompt("identify_objects")  # Reset specific prompt
prompt_manager.reset_all()  # Reset all prompts

Available prompt types:

  • identify_objects: Initial object identification
  • analyze_targets: Target analysis and mapping
  • refine_detections: Detection refinement rules

Examples

The examples directory contains:

  • anthropic/: Claude integration example
  • openai/: GPT-4V integration example
  • custom_prompts/: Prompt customization examples
  • sample_images/: Test images

License

MIT License

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aiscout-0.1.0.tar.gz (21.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

aiscout-0.1.0-py3-none-any.whl (26.5 kB view details)

Uploaded Python 3

File details

Details for the file aiscout-0.1.0.tar.gz.

File metadata

  • Download URL: aiscout-0.1.0.tar.gz
  • Upload date:
  • Size: 21.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.11

File hashes

Hashes for aiscout-0.1.0.tar.gz
Algorithm Hash digest
SHA256 e6591fc7c3e3b95f616b52af1e26c7a1bed0087a6d05508a15b04af74c96191c
MD5 865556184142a16653b12de65fb2fa47
BLAKE2b-256 eff27877c4dc88ecda929e855fe72606c072dedbf988161ef99181b02a11350d

See more details on using hashes here.

File details

Details for the file aiscout-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: aiscout-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 26.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.11

File hashes

Hashes for aiscout-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a17474321fbdcd7b301fd5941740cabdf589767fa3692a6e54dfeb6430bb76df
MD5 7b12409c0c04e22dd5ac5a04f78a1203
BLAKE2b-256 37ad706e331a2fe9efd1c83371af74d74bd48b70f94c4dea7bc322ebb2683372

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page