The Clothing Categorization API is an image classification API that uses an advanced algorithm to identify and categorize different types of clothes and accessories. With this API, you can easily analyze and categorize fashion images, which can be helpful in a variety of applications.
Using the Clothing Categorization API is easy - simply pass an image to the API, and it will identify and categorize the different types of clothing and accessories that appear in the image. This can be used to build sophisticated fashion analysis systems that can help users find the clothes and accessories that they're looking for.
One potential use case for the Clothing Categorization API is in the fashion industry. Retailers can use the API to automatically categorize and tag images of their products, making it easier for customers to find the items they're looking for. The API can also be used to analyze and categorize images from fashion blogs, magazines, and social media, which can provide valuable insights into trends and popular styles.
Another potential use case for the Clothing Categorization API is in the area of virtual styling. By analyzing a user's clothing preferences and style, the API can recommend outfits and accessories that match their preferences. This can be particularly useful for online shopping or virtual styling services.
The Clothing Categorization API can also be used in the area of computer vision research. By providing a powerful image classification algorithm, researchers can use the API to explore new techniques and approaches for image analysis.
Overall, the Clothing Categorization API is a powerful tool that can be used in a variety of applications in the fashion industry and beyond. By providing accurate and reliable image classification results, the API can help businesses and researchers make more informed decisions based on visual data.
Pass the image URL of the clothing that you want to categorize. Receive a prediction score with the recognized clothing.
Fashion E-commerce: The Clothing Categorization API can be used by fashion e-commerce businesses to automatically categorize and tag their products. This makes it easier for customers to search for specific items and helps retailers improve their search results.
Virtual Styling: The Clothing Categorization API can be integrated into virtual styling applications to recommend outfits and accessories that match a user's style and preferences.
Trend Analysis: The Clothing Categorization API can be used to analyze and categorize images from fashion blogs, social media, and magazines to identify emerging fashion trends and popular styles.
Inventory Management: Retailers can use the Clothing Categorization API to identify products that are in high demand and use that data to adjust their inventory accordingly.
Personalized Marketing: The Clothing Categorization API can be used to analyze customer preferences and provide personalized marketing recommendations based on their clothing and accessory choices.
Besides the number of API calls, there are no other limitations.
{"results":[{"status":{"code":"ok","message":"Success"},"name":"https://static.api4.ai/samples/fashion-1.jpg","md5":"873e9651b2e744d7310fd650a599838c","entities":[{"kind":"objects","name":"fashion","objects":[{"box":[0.102,0.243,0.404,0.757],"entities":[{"kind":"classes","name":"classes","classes":{"clothing":0.8876400589942932}}]},{"box":[0.243,0.643,0.24,0.357],"entities":[{"kind":"classes","name":"classes","classes":{"trousers":0.9859585165977478}}]},{"box":[0.102,0.243,0.404,0.45],"entities":[{"kind":"classes","name":"classes","classes":{"top":0.8126749992370605}}]},{"box":[0.273,0.638,0.113,0.034],"entities":[{"kind":"classes","name":"classes","classes":{"belt":0.8809930682182312}}]},{"box":[0.415,0.676,0.117,0.266],"entities":[{"kind":"classes","name":"classes","classes":{"bag":0.9904996156692505}}]},{"box":[0.243,0.643,0.24,0.357],"entities":[{"kind":"classes","name":"classes","classes":{"clothing":0.9575971961021423}}]},{"box":[0.101,0.055,0.405,0.945],"entities":[{"kind":"classes","name":"classes","classes":{"clothing":0.8549925684928894}}]},{"box":[0.1,0.249,0.403,0.397],"entities":[{"kind":"classes","name":"classes","classes":{"shirt":0.9329158067703247}}]},{"box":[0.262,0.257,0.114,0.398],"entities":[{"kind":"classes","name":"classes","classes":{"scarf":0.9823675155639648}}]},{"box":[0.102,0.243,0.404,0.45],"entities":[{"kind":"classes","name":"classes","classes":{"clothing":0.6128033995628357}}]}]}]}]}
curl --location --request POST 'https://zylalabs.com/api/1987/clothing+categorization+api/1751/get+analysis?url=https://static.api4.ai/samples/fashion-1.jpg' --header 'Authorization: Bearer YOUR_API_KEY'
| Header | Description |
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Authorization
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[Required] Should be Bearer access_key. See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The API returns a JSON response containing analysis results of the submitted image. This includes a status code, message, and a list of identified clothing and accessory classes along with their prediction scores.
Key fields in the response include "status" (indicating success), "name" (image URL), "md5" (image hash), "width" and "height" (image dimensions), and "entities" (detailing recognized clothing classes and their scores).
The response data is structured in a JSON format. It contains a top-level "results" array, where each result includes metadata about the image and an "entities" array that lists recognized clothing classes with their respective confidence scores.
The API provides information on various clothing and accessory types identified in the image, including categories like tops, bottoms, footwear, and accessories, along with their respective prediction confidence scores.
Users can customize their requests by providing different image URLs for analysis. The API accepts JPEG and PNG formats, allowing flexibility in the types of images submitted for categorization.
The API utilizes an advanced image classification algorithm trained on a diverse dataset of clothing and accessories. This ensures a broad understanding of fashion items across various styles and categories.
Typical use cases include automating product tagging for e-commerce, enhancing virtual styling applications, analyzing fashion trends from social media, and improving inventory management based on identified clothing demand.
Users can leverage the returned data to enhance search functionalities, personalize marketing efforts, and gain insights into fashion trends by analyzing the prediction scores and identified classes for better decision-making.
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