This model classifies each object in an image into one of 1,000 general object classes, which are organized in a hierarchy, available in the ImageNet dataset. The model can also defend against possible adversarial attacks. This model accepts JPEG images of any size. The model outputs the top five predictions that have the highest confidence, along with the explainability object for the respective predictions, in a JSON file that also includes object hierarchy information.
This model can be used to classify a large set of images into different hierarchical categories based on object classes presented in the images.
Adversarial Defense: This model has a built-in adversarial defense feature. Click here to read more about Modzy’s proprietary adversarial defense strategy.
Explainable: This model has a built-in explainability feature. Click here to read more about model explainability.
67% Top 1 Accuracy
86% Top 5 Accuracy
68% Top 1 Robustness Accuracy
86% Top 5 Robustness Accuracy
This model was trained on the ImageNet dataset. The model was tested on the ImageNet test dataset of 50,000 images. The model obtains the state-of-the-art top 1 accuracy of 0.67 and top 5 accuracy of 0.86 on the test dataset, and top 1 robustness accuracy of 0.68 and top 5 robustness accuracy of 0.86 on the synthetically generated adversarial dataset. Some of this model’s strengths include its capability in detecting and classifying object classes for a large range of images of different sizes and pixel distributions with the ability to find the top 5 appropriate object classes presented in the image. For fast inference time, this model works best on a GPU.
Top 1 Accuracy: The ratio of the number of correct predictions of the top 1 predicted class to the total number of input samples. Further information here.
Top 5 Accuracy: The fraction of the top 5 predictions made by the classifier. Further information here.
Top 1 Robustness Accuracy: The accuracy of the classifier on synthetically generated adversarial dataset. Further information here.
Top 5 Robustness Accuracy: The fraction of the top 5 accuracies made by the classifier on synthetically generated adversarial test dataset. Further information here.
This model utilizes a residual neural network called ResNet50, which was first developed and published by Microsoft Research. ResNet50 uses residual learning, which leads to networks that are very deep and able to learn a large set of features from the training dataset. The features learned by this deep learning model are low-level, mid-level and high-level features presented at the end of each sub-block inside the network. These features are residual features that can be simply understood as subtraction of features learned by the previous layer. The features learned by this model are not only representative of the object classes in images but are also robust against adversarial attacks. This model was developed using the TensorFlow deep learning framework.
This model was trained on the ImageNet dataset, a large imagery dataset of 1.2 million images and 1,000 different object classes. The model was trained using our Lyapunov based robust solution. The training took 51 days on 1 DGX-1 GPU. The model was trained using stochastic gradient descent with a learning rate of 0.01 and momentum of 0.9 for 100 epochs on 1.2 million training images.
The performance of the model was tested on a validation dataset of 50,000 images and obtained the state-of-the-art top 1 accuracy of 0.67 and top 5 accuracy of 0.86. The model also obtained the state-of-the-art top 1 robustness accuracy of 0.68 and top 5 robustness accuracy of 0.86 on the synthetically generated adversarial dataset of 50,000 adversarial images generated by a range of different adversarial attacks.
The input(s) to this model must adhere to the following specifications:
This model will output the following:
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience and Modzy product offering.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.