infery-examples
EdgeSAM
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| infery-examples | EdgeSAM | |
|---|---|---|
| 1 | 1 | |
| 50 | 679 | |
| - | - | |
| 0.0 | 7.5 | |
| 5 months ago | about 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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infery-examples
EdgeSAM
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EdgeSAM: Prompt-in-the-Loop Distillation for On-Device Deployment of Sam
Not affiliated with the authors but find the topic interesting. They have a GitHub page with code and a short demo also:
https://github.com/chongzhou96/EdgeSAM
What are some alternatives?
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
hand-gesture-recognition-mediapipe - This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. Handpose is estimated using MediaPipe.
fastT5 - ⚡ boost inference speed of T5 models by 5x & reduce the model size by 3x.
x-stable-diffusion - Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6
nebuly - The user analytics platform for LLMs
timm-flutter-pytorch-lite-blogpost - PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter.
keras_cv_stable_diffusion_to_tflite - Scripts for converting Keras CV Stable Diffusion to tflite
w2v2-how-to - How to use our public wav2vec2 dimensional emotion model
TNN - TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.