semvec/pypi-packages huggingface dataset:
monk-obj-test2 0.0.12
Monk Object Detection's 2_pytorch_finetune
Requires Python: >=3.6

## Project Details
Pipeline based on GluonCV Fintuning project - https://gluon-cv.mxnet.io/build/examples_detection/index.html

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## Installation

Supports 
- Python 3.6
- Python 3.7

`cd installation`

Check the cuda version using the command

`nvcc -V`

Select the right requirements file and run 

`cat <selected requirements file> | xargs -n 1 -L 1 pip install`

For example for cuda 9.0

`cat requirements_cuda9.0.txt | xargs -n 1 -L 1 pip install`


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## Functional Documentation
  [Link](https://abhi-kumar.github.io/1_gluoncv_finetune_docs/)

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## Pipeline

- Load Dataset

`gtf.Dataset(root_dir, img_dir, anno_file, batch_size=batch_size);`

- Load Model

`gtf.Model(model_name, use_pretrained=pretrained, use_gpu=gpu);`

- Set Hyper-parameter

`gtf.Set_Learning_Rate(0.001);`

- Train

`gtf.Train(epochs, params_file);`



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## TODO

- [x] Add SSD support
- [x] Add YoloV3 support
- [ ] Add support for Coco-Type Annotated Datasets
- [x] Add support for VOC-Type Annotated Dataset
- [ ] Add Faster-RCNN support
- [x] Test on Kaggle and Colab 
- [ ] Add validation feature & data pipeline
- [ ] Add Optimizer selection feature
- [ ] Enable Learning-Rate Scheduler Support
- [ ] Enable Layer Freezing
- [ ] Set Verbosity Levels
- [ ] Add Project management and version control support (Similar to Monk Classification)
- [ ] Add Graph Visualization Support
- [ ] Enable batch proessing at inference
- [ ] Add feature for top-k output visualization
- [x] Add Multi-GPU training
- [ ] Auto correct missing or corrupt images - Currently skips them
- [ ] Add Experimental Data Analysis Feature


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## External Contributors list 

- https://github.com/THEFASHIONGEEK: Multi GPU feature
GenGraph 0.2
Tools for the creation and use of genome graphs

# GenGraph
A repository for the GenGraph toolkit for the creation and manipulation of graph genomes

This project aims to make genome graphs simple to create, and provide tools for their manipulation and intergration into common workflows.

Please refer to the [wiki](https://github.com/jambler24/GenGraph/wiki) for installation and usage instructions. 

## Quickstart 

    python ./gengraphTool.py make_genome_graph --seq_file <sequence_file.txt> --out_file_name <filename> --recreate_check

A Docker file is also available that will contain everything you need for your first run. 



## Sequence file

The sequence file is a tab delimited file with 4 columns:

seq_name    |	aln_name	|   seq_path	|   annotation_path
------------ | ------------- | ------------- | -------------
H37Rv |	seq0 |	/Users/panix/Desktop/genomes/H37Rv/sequence.fasta |	NA
Beijing |	seq1 |	/Users/panix/Desktop/genomes/Beijing-NITR203/sequence.fasta |	NA
H37Ra |	seq2 |	/Users/panix/Desktop/genomes/H37Ra/sequence.fasta |	NA


The seq_name column is used as the node and edge 'ids' attribute value. It needs to be unique.