semvec/pypi-packages huggingface dataset:
procore-cv-eval 0.1.0
For evaluation of computer vision deep learning based models
Requires Python: <=3.6

# Computer Vision Evaluation Tool
This package is prepared to perform the task of performance evaluation of different tasks of:

* Single-Label Multi-Class Classification
* Multi-Label Multi-Class Classification
* Multi-Label Object detection
* Semantic Segmentation

## Installation
### Requirements
* PYTHON==3.6
First install the pre-requisite packages:
```
cd procore_cv_eval
pip install -r requirements.txt
```
### PIP Installation
The tool can be installed as a pip package by any of the following methods:

1. Simply run `pip install procore-cv-eval` inside virtual environment (RECOMMENDED)
2. Clone the repo and install the pip package in-place
```
git clone https://github.com/indusai/procore_cv_eval.git
cd procore_cv_eval
pip install .
```
3. Use the `.whl` ditribution in procore_cv_eval/dist directory
```
cd procore_cv_eval
pip install procore_cv_eval-0.0.2-py3-none-any.whl
```
### Application
Go to `procore_cv_eval/demo` directory and check demos for each type of evaluation
Before running the demos:
``` cd procore_cv_eval
mkdir saved_models
```
Download the contents of this folder: https://drive.google.com/drive/folders/10jdKnvlw0OwQ7lRvuDCY9IlUC_WVv2iM?usp=sharing
into the saved_models folder

## Future Releases
### To-do List
* Read segmentation ground truth annotation from coco-format JSON file
* Evaluation based on a range of confidence thresholds
* Add YOLOv3(Darknet) option to object detection evaluation
* Change Segmentation script to read model from .pb graph and images directory instead of .tfrecord
* Visualize confusion matrix for classification
* Add Keras detection class for object detection
* Add instance segmentation to evaluation options