| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - ILSVRC/imagenet-1k |
| | pipeline_tag: image-classification |
| | --- |
| | # Introduction |
| |
|
| | This repository stores the model for Efficientnet-b4, compatible with Kalray's neural network API. </br> |
| | Please see www.github.com/kalray/kann-models-zoo for details and proper usage. </br> |
| | Please see https://huggingface.co/docs/transformers/main/en/model_doc/efficientnet for Efficientnet model description. </br> |
| | |
| | # Contents |
| | |
| | - ONNX: efficientNet-b4.onnx |
| | - Quantized ONNX (INT8): efficientNet-b4-q.onnx |
| | |
| | # Lecture note reference |
| | |
| | - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, https://arxiv.org/pdf/1905.11946 |
| | |
| | # Repository or links references |
| | |
| | - [PyTorch | TorchVision](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b4.html#torchvision.models.efficientnet_b4) |
| | |
| | BibTeX entry and citation info |
| | ``` |
| | @inproceedings{he2016deep, |
| | title = {Deep residual learning for image recognition}, |
| | author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, |
| | booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition}, |
| | pages = {770--778}, |
| | year = {2016} |
| | } |
| | ``` |
| | |
| | Authors: |
| | + qmuller@kalrayinc.com |
| | + nbouberbachene@kalrayinc.com |
