๐Ÿ”ฅ Cannlytics-powered artificial intelligence ๐Ÿค–

Overview

Cannlytics AI

License: MIT

๐Ÿ”ฅ Cannlytics-powered artificial intelligence ๐Ÿค–

๐Ÿ—๏ธ Installation

You can simply clone the repository to get your hands on the AI source code.

git clone https://github.com/cannlytics/cannlytics-ai.git

๐Ÿƒโ€โ™€๏ธ Quickstart

You can run each data collection routine through the command line. For example:

python ai/get_cannabis_data/get_data_ma.py

๐Ÿงฑ Development

Please see the data collection guides for information on how public data is collected.

๐Ÿฆพ Automation

Now for the fun part, automation. [Instructions coming soon]

๐Ÿ’ธ Support

Made with ๐Ÿงก and your good will.

๐Ÿ›๏ธ License

Copyright (c) 2021 Cannlytics

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Owner
Cannlytics
Simple, easy, end-to-end cannabis analytics. Open source. Made by scientists for scientists.
Cannlytics
Transfer Learning for Pose Estimation of Illustrated Characters

bizarre-pose-estimator Transfer Learning for Pose Estimation of Illustrated Characters Shuhong Chen *, Matthias Zwicker * WACV2022 [arxiv] [video] [po

Shuhong Chen 142 Dec 28, 2022
Research on Event Accumulator Settings for Event-Based SLAM

Research on Event Accumulator Settings for Event-Based SLAM This is the source code for paper "Research on Event Accumulator Settings for Event-Based

Robin Shaun 26 Dec 21, 2022
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Automatic differentiation with weighted finite-state transducers.

GTN: Automatic Differentiation with WFSTs Quickstart | Installation | Documentation What is GTN? GTN is a framework for automatic differentiation with

100 Dec 29, 2022
INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing

INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing Existing studies on semantic parsing focus primarily on mapping a natural-la

7 Aug 22, 2022
Deep Learning for Time Series Classification

Deep Learning for Time Series Classification This is the companion repository for our paper titled "Deep learning for time series classification: a re

Hassan ISMAIL FAWAZ 1.2k Jan 02, 2023
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning This repository is the official implementation of CARE.

ChongjianGE 89 Dec 02, 2022
Real-time multi-object tracker using YOLO v5 and deep sort

This repository contains a two-stage-tracker. The detections generated by YOLOv5, a family of object detection architectures and models pretrained on the COCO dataset, are passed to a Deep Sort algor

Mike 3.6k Jan 05, 2023
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.

openpifpaf Continuously tested on Linux, MacOS and Windows: New 2021 paper: OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Te

VITA lab at EPFL 50 Dec 29, 2022
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
We are More than Our JOints: Predicting How 3D Bodies Move

We are More than Our JOints: Predicting How 3D Bodies Move Citation This repo contains the official implementation of our paper MOJO: @inproceedings{Z

72 Oct 20, 2022
Self-Supervised CNN-GCN Autoencoder

GCNDepth Self-Supervised CNN-GCN Autoencoder GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network To be published

53 Dec 14, 2022
Authors implementation of LieTransformer: Equivariant Self-Attention for Lie Groups

LieTransformer This repository contains the implementation of the LieTransformer used for experiments in the paper LieTransformer: Equivariant self-at

35 Oct 18, 2022
A light-weight image labelling tool for Python designed for creating segmentation data sets.

An image labelling tool for creating segmentation data sets, for Django and Flask.

117 Nov 21, 2022
A Topic Modeling toolbox

Topik A Topic Modeling toolbox. Introduction The aim of topik is to provide a full suite and high-level interface for anyone interested in applying to

Anaconda, Inc. (formerly Continuum Analytics, Inc.) 93 Dec 01, 2022
Chinese named entity recognization with BiLSTM using Keras

Chinese named entity recognization (Bilstm with Keras) Project Structure ./ โ”œโ”€โ”€ README.md โ”œโ”€โ”€ data โ”‚ย ย  โ”œโ”€โ”€ README.md โ”‚ย ย  โ”œโ”€โ”€ data ๆ•ฐๆฎ้›† โ”‚ย ย  โ”‚ย ย  โ”œโ”€

1 Dec 17, 2021
CVPR2021 Workshop - HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization.

HDRUNet [Paper Link] HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization By Xiangyu Chen, Yihao Liu, Zhengwen Zhang, Yu Qiao an

XyChen 105 Dec 20, 2022
Duke Machine Learning Winter School: Computer Vision 2022

mlwscv2002 Welcome to the Duke Machine Learning Winter School: Computer Vision 2022! The MLWS-CV includes 3 hands-on training sessions on implementing

Duke + Data Science (+DS) 9 May 25, 2022
Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019)

Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019) Introduction Official implementation of Dynamic Multi-scale Filters for Semant

23 Oct 21, 2022
[NeurIPS 2021] โ€œImproving Contrastive Learning on Imbalanced Data via Open-World Samplingโ€,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022