object recognition with machine learning on Respberry pi

Last update: Dec 11, 2021

Overview

Respberrypi_object-recognition

object recognition with machine learning on Respberry pi

line.py

建立一支與樹梅派連線的 linebot
使用此 linebot 遠端控制樹梅派拍照

config.ini

linebot 的 channel token & secret raspberrypi 的連線位址

app.py

raspberrypi的主控制程式

infrared.py

控制樹梅派的外接照相模組

config_pi.ini

linbot 的連線位址

recongnize.py

匯入訓練好的 tensorflow lite 物體辨識模型
並根據樹梅派的照片資料做出物體辨識
顯示照片中辨識信心度最高的三樣物品

Owner

GitHub Repository

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object recognition with machine learning on Respberry pi

Related tags

Overview

Respberrypi_object-recognition

line.py

config.ini

app.py

infrared.py

config_pi.ini

recongnize.py

Owner

Pytorch tutorials for Neural Style transfert

Project page for the paper Semi-Supervised Raw-to-Raw Mapping 2021.

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Sharing of contents on mitochondrial encounter networks

Pytorch implementation of "Forward Thinking: Building and Training Neural Networks One Layer at a Time"

SwinTrack: A Simple and Strong Baseline for Transformer Tracking

Official project website for the CVPR 2021 paper "Exploring intermediate representation for monocular vehicle pose estimation"

HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records

A Python Package for Convex Regression and Frontier Estimation

Layer 7 DDoS Panel with Cloudflare Bypass ( UAM, CAPTCHA, BFM, etc.. )

Official Repository for Machine Learning class - Physics Without Frontiers 2021

Wav2Vec for speech recognition, classification, and audio classification

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

[SDM 2022] Towards Similarity-Aware Time-Series Classification

Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

Practical Single-Image Super-Resolution Using Look-Up Table

code associated with ACL 2021 DExperts paper

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