Remove Background Competitors Benchmark (Remove.bg, Clipdrop, SAM, Azure)

On this page you will find a comparison of our Remove Background API against some of our competitors.

For additional information about the important features to look for in image background removal technology, you can also consult this blog article.

Result Comparison

If you want to compare two results in details, we recommend using this website: https://www.diffchecker.com/image-compare/

Home Decoration Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Furniture Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Apparel Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Food Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Cosmetic Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Jewelry Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

People Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Industry Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Reproducing the results

If you want to reproduce the results of this benchmark, here are the Python scripts that were used to generate the images.

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests
import concurrent.futures

API_KEY = "YOUR_PHOTOROOM_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://sdk.photoroom.com/v1/segment"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            headers = {
                "Accept": "image/png, application/json",
                "pr-background-removal-model-version": "2024-09-26",
                "x-api-key": API_KEY
            }

            response = requests.post(url, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
        for root, _, files in os.walk(directory_path):
            for file in files:
                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                    file_path = os.path.join(root, file)

                    result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                    result_file_directory = os.path.join(result_directory, os.path.basename(root))

                    if not os.path.exists(result_file_directory):
                        os.makedirs(result_file_directory)

                    result_path = os.path.join(result_file_directory, result_file_name)

                    if not os.path.exists(result_path): # don't re-process images 
                        executor.submit(process_image, file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-photoroom/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_KEY = "YOUR_REMOVE_BG_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://api.remove.bg/v1.0/removebg"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            payload = {
                "size": "auto"
            }
            headers = {
                "Accept": "image/png, application/json",
                "x-api-key": API_KEY
            }

            response = requests.post(url, data=payload, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-remove-bg/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_KEY = "YOUR_CLIPDROP_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://clipdrop-api.co/remove-background/v1"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            payload = {
                "transparency_handling": "discard_alpha_layer"
            }
            headers = {
                "Accept": "image/png",
                "x-api-key": API_KEY
            }

            response = requests.post(url, data=payload, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-clipdrop/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code please:

download the original images that you want to re-process, and place them in a directory called original-images
run the command: pip install pillow rembg

from PIL import Image
from rembg import new_session, remove
import os

MODEL_NAME = "sam"
SESSION = new_session(MODEL_NAME)

def process_image(input_image_path, output_image_path):
    input = Image.open(input_image_path)
    output = remove(input)
    output.save(output_image_path)
    print(f"Image saved to {output_image_path}")
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '-sam.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-sam/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_ENDPOINT = "YOUR_AZURE_ENDPOINT"
API_KEY = "YOUR_AZURE_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = API_ENDPOINT + "computervision/imageanalysis:segment?api-version=2023-02-01-preview&mode=backgroundRemoval"

        with open(input_image_path, 'rb') as image_file:
            headers = {
                "Content-Type": "application/octet-stream",
                "Ocp-Apim-Subscription-Key": API_KEY
            }

            response = requests.post(url, data=image_file, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '-azure.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-azure/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

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Remove Background Competitors Benchmark (Remove.bg, Clipdrop, SAM, Azure)

On this page you will find a comparison of our Remove Background API against some of our competitors.

For additional information about the important features to look for in image background removal technology, you can also consult this blog article.

Result Comparison

If you want to compare two results in details, we recommend using this website: https://www.diffchecker.com/image-compare/

Home Decoration Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Furniture Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Apparel Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Food Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Cosmetic Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Jewelry Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

People Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Industry Images

Original Image

Photoroom API (response time: ~450 milliseconds)

Remove.bg API (response time: ~600 milliseconds)

Clipdrop API (response time: ~2 seconds, doesn't support cropping to the bounds of the subject)

Segment Anything Model (SAM) (response time: depends on the machine running the model)

Azure Background Removal API (response time: ~2 seconds)

Reproducing the results

If you want to reproduce the results of this benchmark, here are the Python scripts that were used to generate the images.

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests
import concurrent.futures

API_KEY = "YOUR_PHOTOROOM_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://sdk.photoroom.com/v1/segment"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            headers = {
                "Accept": "image/png, application/json",
                "pr-background-removal-model-version": "2024-09-26",
                "x-api-key": API_KEY
            }

            response = requests.post(url, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
        for root, _, files in os.walk(directory_path):
            for file in files:
                if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                    file_path = os.path.join(root, file)

                    result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                    result_file_directory = os.path.join(result_directory, os.path.basename(root))

                    if not os.path.exists(result_file_directory):
                        os.makedirs(result_file_directory)

                    result_path = os.path.join(result_file_directory, result_file_name)

                    if not os.path.exists(result_path): # don't re-process images 
                        executor.submit(process_image, file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-photoroom/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_KEY = "YOUR_REMOVE_BG_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://api.remove.bg/v1.0/removebg"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            payload = {
                "size": "auto"
            }
            headers = {
                "Accept": "image/png, application/json",
                "x-api-key": API_KEY
            }

            response = requests.post(url, data=payload, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-remove-bg/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_KEY = "YOUR_CLIPDROP_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = "https://clipdrop-api.co/remove-background/v1"

        with open(input_image_path, 'rb') as image_file:
            files = { "image_file": image_file }

            payload = {
                "transparency_handling": "discard_alpha_layer"
            }
            headers = {
                "Accept": "image/png",
                "x-api-key": API_KEY
            }

            response = requests.post(url, data=payload, files=files, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-clipdrop/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code please:

download the original images that you want to re-process, and place them in a directory called original-images
run the command: pip install pillow rembg

from PIL import Image
from rembg import new_session, remove
import os

MODEL_NAME = "sam"
SESSION = new_session(MODEL_NAME)

def process_image(input_image_path, output_image_path):
    input = Image.open(input_image_path)
    output = remove(input)
    output.save(output_image_path)
    print(f"Image saved to {output_image_path}")
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '-sam.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-sam/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)

Before running this code, please download the original images that you want to re-process, and place them in a directory called original-images.

import os
import requests

API_ENDPOINT = "YOUR_AZURE_ENDPOINT"
API_KEY = "YOUR_AZURE_API_KEY"

def process_image(input_image_path, output_image_path):
    try:
        url = API_ENDPOINT + "computervision/imageanalysis:segment?api-version=2023-02-01-preview&mode=backgroundRemoval"

        with open(input_image_path, 'rb') as image_file:
            headers = {
                "Content-Type": "application/octet-stream",
                "Ocp-Apim-Subscription-Key": API_KEY
            }

            response = requests.post(url, data=image_file, headers=headers)
            response.raise_for_status()

            with open(output_image_path, 'wb') as f:
                f.write(response.content)
                print(f"Image downloaded and saved to {output_image_path}")

    except requests.RequestException as e:
        print(f"Error: {str(e)} ({input_image_path})")
        return str(e)
    
def iterate_over_directory(directory_path, result_directory):
    for root, _, files in os.walk(directory_path):
        for file in files:
            if file.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.heic')):
                file_path = os.path.join(root, file)

                result_file_name = os.path.splitext(os.path.basename(file_path))[0] + '-azure.png'
                result_file_directory = os.path.join(result_directory, os.path.basename(root))

                if not os.path.exists(result_file_directory):
                    os.makedirs(result_file_directory)

                result_path = os.path.join(result_file_directory, result_file_name)

                if not os.path.exists(result_path): # don't re-process images 
                    process_image(file_path, result_path)    

if __name__ == "__main__":
    INPUT_DIRECTORY = "./original-images/"
    OUTPUT_DIRECTORY = "./result-azure/"

    if not os.path.exists(OUTPUT_DIRECTORY):
        os.makedirs(OUTPUT_DIRECTORY)

    iterate_over_directory(directory_path=INPUT_DIRECTORY, result_directory=OUTPUT_DIRECTORY)