Lost and Found Detector Computer Vision Model
How to use the Lost and Found Detector Detection API
Try This Model
Drop an image here or click to upload
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="lost-and-found-detector/5")Or, Use Free Bottle, Laptop and Mouse Detection API
Powered by general detection model
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "Bottle, Laptop, Mouse, Phone, Handbag"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
About Lost and Found Detector Model
Trace It – Smart Lost and Found System using Object Detection
Trace It is an intelligent lost-and-found management system designed to simplify and improve the process of reporting and recovering lost items. The system leverages modern computer vision techniques and machine learning to automatically detect and classify objects from images uploaded by users.
Using an object detection model trained on the Roboflow platform, the system can identify common personal belongings such as wallets, phones, bags, and keys. When a user uploads an image of a found or lost item, the system analyzes the image and automatically suggests the item category, reducing manual effort and improving accuracy.
The platform features a user-friendly interface where individuals can report lost or found items, view listings, and search for matches. To encourage active participation and improve civic responsibility, Trace It incorporates a gamified reward system, where users earn points for reporting items and contributing to successful recoveries. A leaderboard highlights top contributors, promoting engagement within the community.
Additionally, the system is designed to be scalable and can be deployed in environments such as schools, universities, public places, and smart cities. By combining artificial intelligence with community-driven interaction, Trace It enhances the efficiency of traditional lost-and-found systems and contributes to a more organized and responsible society.
Key Technologies Used:
- Python
- Object Detection (Computer Vision)
- Roboflow (Model Training & Deployment)
- Inference SDK (API Integration)
- Frontend (HTML, CSS, JavaScript)
To develop an AI-powered system that automates item recognition, improves recovery rates of lost belongings, and encourages community participation through a gamified experience.
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ lost-and-found-detector_dataset,
title = { Lost and Found Detector Dataset },
type = { Open Source Dataset },
author = { Tariqs Workspace },
howpublished = { \url{ https://universe.roboflow.com/tariqs-workspace-5laqn/lost-and-found-detector } },
url = { https://universe.roboflow.com/tariqs-workspace-5laqn/lost-and-found-detector },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2026 },
month = { apr },
note = { visited on 2026-07-29 },
}