Compare pre-trained ImageNet models

Comparison of Deep Convolutional Neural Network Architectures

We are using canned architectures with pre-trained weights provided by TensorFlow Keras.
Here's a comparison between the SOTA ImageNet architectures.

How to use the API?

# Provide `url` of the image to classify and ImageNet `model` architectures list 
import requests

url = "https://gramener.com/amle-image-recognition/classify"
data = {"url": "https://i.imgur.com/WkomVeG.jpg",
 "model": ["MobileNetV2", "ResNet50", "VGG19", "InceptionV3", "Xception"]}
requests.get(url, data=data).json()

References

• J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei, "Imagenet: A large-scale hierarchical image database," in Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009
• K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," in International Conference on Learning Representations, 2015
• K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
• F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
• M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018