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{
"url": "https://github.com/onnx/tensorflow-onnx/blob/master/tutorials/efficientnet-lite.ipynb",
"name": "tensorflow-onnx/tutorials/efficientnet-lite.ipynb at main - GitHub",
"snippet": "This tutorial shows how to convert them to ONNX. To start, we setup a few environment variables and download the pre-trained model.",
"host_name": "github.com",
"rank": 0,
"date": "",
"favicon": ""
},
{
"url": "https://huggingface.co/onnxmodelzoo/efficientnet-lite4-11",
"name": "onnxmodelzoo/efficientnet-lite4-11 - Hugging Face",
"snippet": "EfficientNet-Lite4 is an image classification model that achieves state-of-the-art accuracy. It is designed to run on mobile CPU, GPU, and ...",
"host_name": "huggingface.co",
"rank": 1,
"date": "",
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},
{
"url": "https://developers.google.com/edge/mediapipe/solutions/vision/image_embedder",
"name": "Image embedding task guide | Google AI Edge",
"snippet": "The MediaPipe Image Embedder task lets you create a numeric representation of an image, which is useful in accomplishing various ML-based ...",
"host_name": "developers.google.com",
"rank": 2,
"date": "",
"favicon": ""
},
{
"url": "https://debuggercafe.com/person-segmentation-with-efficientnet-lite",
"name": "Person Segmentation with EfficientNet Lite Based - DebuggerCafe",
"snippet": "Person segmentation using a EfficientNet Lite based encoder and FCN32s decoder with the PyTorch Deep Learning framework.",
"host_name": "debuggercafe.com",
"rank": 3,
"date": "",
"favicon": ""
},
{
"url": "https://www.youtube.com/watch?v=nVTropZJC88",
"name": "Local Image Embeddings in .NET | CLIP + ONNX Multimodal AI",
"snippet": "... image embeddings locally using ONNX Runtime - Build a simple image search scenario - Connect it to a lightweight RAG-style chat - Discuss ...",
"host_name": "www.youtube.com",
"rank": 4,
"date": "",
"favicon": ""
},
{
"url": "https://blog.tensorflow.org/2020/03/higher-accuracy-on-vision-models-with-efficientnet-lite.html",
"name": "Higher accuracy on vision models with EfficientNet-Lite",
"snippet": "We are excited to announce EfficientNet-Lite (GitHub, TFHub), which runs on TensorFlow Lite and designed for performance on mobile CPU, GPU, and EdgeTPU.",
"host_name": "blog.tensorflow.org",
"rank": 5,
"date": "",
"favicon": ""
},
{
"url": "https://www.edgecortix.com/en/blog/connecting-edge-ai-software-with-pytorch-tensorflow-lite-onnx-models",
"name": "Connecting Edge AI Software with PyTorch, TensorFlow Lite, and",
"snippet": "Edge AI software workflows can start with PyTorch, TensorFlow, or ONNX models, and MERA automatically converts them to code for EdgeCortix DNA IP.",
"host_name": "www.edgecortix.com",
"rank": 6,
"date": "",
"favicon": ""
},
{
"url": "https://medium.com/data-science/bye-bye-mobilenet-hello-efficientnet-9b8ec2cc1a9c",
"name": "Bye-bye MobileNet. Hello EfficientNet! | by Vardan Agarwal - Medium",
"snippet": "MobileNet was the go-to model for an app and edge deployment. Now it is dethroned by the family of EfficientNet Lite models.",
"host_name": "medium.com",
"rank": 7,
"date": "",
"favicon": ""
},
{
"url": "https://docs.oracle.com/en/database/oracle/machine-learning/oml4py/2-23ai/mlpug/onnx-pipeline-models-image-embedding.html",
"name": "ONNX Pipeline Models: Image Embedding - Oracle Help Center",
"snippet": "ONNX pipeline models provides image embedding models that accepts image as an input and produces embeddings as output.",
"host_name": "docs.oracle.com",
"rank": 8,
"date": "",
"favicon": ""
},
{
"url": "https://www.researchgate.net/publication/397017514_An_Enhanced_MobileNet-Based_Lightweight_Image_Classification_Model_for_Environments_with_Limited_Resources",
"name": "An Enhanced MobileNet-Based Lightweight Image Classification",
"snippet": "Using the ImageNet-10 dataset, the model is compared against industry-leading lightweight architectures such as MobileNetV2 and EfficientNet.",
"host_name": "www.researchgate.net",
"rank": 9,
"date": "",
"favicon": ""
}
] |