Object Detection API

REST API to detect objects in images. Get labels, confidence scores, and bounding box coordinates. Powered by neural networks.

Features

• Detect multiple objects in a single image
• Returns object names, confidence scores (0.0-1.0), and bounding box coordinates
• Supports JPEG and PNG formats (up to 10MB)
• 5,000 requests/month on free tier
• Example Response:

[
  {
    "object_name": "mango",
    "confidence_score": 0.61,
    "region": {
      "top_left_x": 7,
      "top_left_y": 177,
      "bottom_right_x": 718,
      "bottom_right_y": 1262
    }
  }
]

Authentication

1. Create account at omkar.cloud

2. Get API key from omkar.cloud/api-key

3. Include API-Key header in requests

Quick Start

curl -X POST "https://object-detection-api.omkar.cloud/detect" \
  -H "API-Key: YOUR_API_KEY" \
  -F "image=@photo.jpg"

[
  {
    "object_name": "mango",
    "confidence_score": 0.61,
    "region": {
      "top_left_x": 7,
      "top_left_y": 177,
      "bottom_right_x": 718,
      "bottom_right_y": 1262
    }
  }
]

Installation

Python

pip install requests

import requests

with open("photo.jpg", "rb") as image_file:
    response = requests.post(
        "https://object-detection-api.omkar.cloud/detect",
        headers={"API-Key": "YOUR_API_KEY"},
        files={"image": image_file}
    )

data = response.json()
for obj in data:
    print(f"Detected: {obj['object_name']} (confidence: {obj['confidence_score']:.2f})")

Node.js

npm install axios form-data

import axios from "axios";
import FormData from "form-data";
import fs from "fs";

const form = new FormData();
form.append("image", fs.createReadStream("photo.jpg"));

const response = await axios.post(
    "https://object-detection-api.omkar.cloud/detect",
    form,
    {
        headers: {
            "API-Key": "YOUR_API_KEY",
            ...form.getHeaders()
        }
    }
);

response.data.forEach(obj => {
    console.log(`Detected: ${obj.object_name} (confidence: ${obj.confidence_score})`);
});

API Reference

Endpoint

POST https://object-detection-api.omkar.cloud/detect

Headers

Header	Required	Description
API-Key	Yes	API key from omkar.cloud/api-key
Content-Type	Yes	multipart/form-data

Request Body

Field	Required	Description
image	Yes	Image file (JPEG or PNG, max 10MB)

Response Fields

Field	Type	Description
object_name	string	Detected object label (e.g., "car", "person", "dog")
confidence_score	float	Model confidence (0.0 to 1.0). Higher = more confident
region	object	Bounding box coordinates

Region object:

Field	Type	Description
top_left_x	int	X coordinate of top-left corner
top_left_y	int	Y coordinate of top-left corner
bottom_right_x	int	X coordinate of bottom-right corner
bottom_right_y	int	Y coordinate of bottom-right corner

Examples

Detect objects and filter by confidence

import requests

with open("photo.jpg", "rb") as image_file:
    response = requests.post(
        "https://object-detection-api.omkar.cloud/detect",
        headers={"API-Key": "YOUR_API_KEY"},
        files={"image": image_file}
    )

# Filter detections with confidence > 0.5
high_confidence = [obj for obj in response.json() if obj['confidence_score'] > 0.5]
for obj in high_confidence:
    print(f"{obj['object_name']}: {obj['confidence_score']:.2%}")

Get bounding box for cropping

import requests

with open("photo.jpg", "rb") as image_file:
    response = requests.post(
        "https://object-detection-api.omkar.cloud/detect",
        headers={"API-Key": "YOUR_API_KEY"},
        files={"image": image_file}
    )

for obj in response.json():
    region = obj['region']
    width = region['bottom_right_x'] - region['top_left_x']
    height = region['bottom_right_y'] - region['top_left_y']
    print(f"{obj['object_name']}: {width}x{height}px at ({region['top_left_x']}, {region['top_left_y']})")

Count specific objects

import requests
from collections import Counter

with open("photo.jpg", "rb") as image_file:
    response = requests.post(
        "https://object-detection-api.omkar.cloud/detect",
        headers={"API-Key": "YOUR_API_KEY"},
        files={"image": image_file}
    )

counts = Counter(obj['object_name'] for obj in response.json())
print(f"Objects found: {dict(counts)}")

Error Handling

import requests

with open("photo.jpg", "rb") as image_file:
    response = requests.post(
        "https://object-detection-api.omkar.cloud/detect",
        headers={"API-Key": "YOUR_API_KEY"},
        files={"image": image_file}
    )

if response.status_code == 200:
    data = response.json()
elif response.status_code == 401:
    # Invalid API key
    pass
elif response.status_code == 413:
    # Image too large (>10MB)
    pass
elif response.status_code == 429:
    # Rate limit exceeded
    pass

Rate Limits

Plan	Price	Requests/Month
Free	$0	5,000
Starter	$25	100,000
Grow	$75	1,000,000
Scale	$150	10,000,000

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