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README.md

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-license: other
-license_name: aplux-model-farm-license
-license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
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+---
+license: other
+license_name: aplux-model-farm-license
+license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
+---
+
+![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F%25E5%259B%25BE-17.png&w=640&q=75)
+
+## EfficientNet-B0: Image Classification
+
+EfficientNet-B0 is the baseline model in the EfficientNet series introduced by Google in 2019. EfficientNet employs a "compound scaling" method, which jointly optimizes the network's depth, width, and resolution to achieve greater computational efficiency and accuracy. EfficientNet-B0 utilizes the Mobile Inverted Bottleneck Convolution (MBConv) module along with depthwise separable convolutions, significantly reducing parameters and computational cost. Despite being the smallest model in the series, EfficientNet-B0 performs excellently across various computer vision tasks, delivering high accuracy while maintaining low resource requirements, making it ideal for mobile devices and edge computing in resource-constrained environments.
+
+### Source model
+
+- Input shape: 224x224
+- Number of parameters: 5.04M
+- Model size: 20.16M
+- Output shape: 1x1000
+
+Source model repository: [EfficientNet-B0](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py)
+
+## Performance Reference
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## Inference & Model Conversion
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## License
+
+- Source Model: [BSD-3-CLAUSE](https://github.com/pytorch/vision/blob/main/LICENSE)
+
+- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf)