Similar to AlexNet but uses multiple smaller kernel-sized filters that provides more accuracy when classifying images.Ī Deep CNN model (up to 8 layers) where the input is an image and the output is a vector of 1000 numbers.ĭeep CNN model(up to 22 layers). Uses shortcut connections to achieve higher accuracy when classifying images.Ī light-weight CNN model providing AlexNet level accuracy with 50x fewer parameters.ĭeep CNN model(up to 19 layers). Light-weight deep neural network best suited for mobile and embedded vision applications.Ī CNN model (up to 152 layers). This collection of models take images as input, then classifies the major objects in the images into 1000 object categories such as keyboard, mouse, pencil, and many animals.
SANDRA MODEL SET COLLECTION HOW TO
INT8 models are generated by Intel® Neural Compressor, read the Introduction to know how to use it to quantize ONNX model. npz), downloading multiple ONNX models through Git LFS command line, and starter Python code for validating your ONNX model using test data. Models Read the Usage section below for more details on the file formats in the ONNX Model Zoo (.onnx. To download an ONNX model, navigate to the appropriate Github page and click the Download button on the top right. We have standardized on Git LFS (Large File Storage) to store ONNX model files. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The ONNX Model Zoo is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members like you. ONNX is supported by a community of partners who have implemented it in many frameworks and tools.
Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models.
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