Object Detection
Object detection is a computer vision technique used to identify and locate objects within an image or video. It involves both classification (identifying what the object is) and localization (identifying where the object is in the image)
The below input parameters are for different attack types. To start working with the APIs view the Object Detection.
Object Detection is an early access with limited functionality. It is not available in AIShield pypi package. For early access, kindly contact [email protected]
- Data: The processed data, ready to be passed to the model for prediction, should be saved in a folder.
- Label: A list of XML files that have the same names as the images in the dataset folder
- Model: The YOLO model should be saved in ONNX format and then zipped.
Note:
- All files uploaded should be in zipped format. The above files are sample data for the Pascal VOC use case.
- Prerequisite: Only 2-5 % of data is needed.
The below table parameters are common for all attact types such as Extraction, Evasion, and Poisoning.
To see the additional parameter specific for each attact type, such as Extraction and Evasion, refer to the below sections.
Parameter | Data type | Description | Remark |
|---|---|---|---|
model_Id | String | Model_id received during model registration. We need to provide this model ID in query parameter in URL. | You have to do model registration only once for a model to perform model analysis. This will help you track the no of api call made, and it's success metric. |
Request Body (Json format) | | | |
normalize_data | String | Model trained on Normalized data. | If model is trained on normalized data, then set this parameter as "yes" else "no". |
model_api_details | String | If use_model_api is Yes, then provide API details of hosted model as encrypted JSON string is mandatory | Provide this only if use_model_api is "yes". |
use_model_api | String | Use model API to train your model instead of uploading the model as a zip. | Use model API to train your model instead of uploading the model as a zip. |
defense_bestonly | String | Choose to train your model until it achieves the best results or above 95% accuracy. | When selected "yes", it will train N number of model and select best model. Ofcourse this will take longer time. If "no", then once defense model accuracy reached above 95% It will stop |
model_framework | string | Original model is built framework. | curretly supported framework are: onnx |
To access all sample artifacts, please visit Artifacts.
- For specific artifact details, refer
Note: For Object Detection, supported attack types are - Evasion
