Object detection evaluation metrics using Python.

Last update: Sep 06, 2022

Overview

Object detection evaluation metrics

Prerequisites
How to use
Author

Current features

Prerequisites

Python
Numpy pip install numpy

How to use

Prepare ground truth and prediction files

Each "image" should have separate text files.
Use same names for both ground truth and predictions.
Use separate folder for both ground truth and predictions.
Each file should be in this format: xmin ymin xmax ymax label_id
Example (label id starts with 0):

1 2 3 4 0
1 2 3 4 1
1 2 3 4 1

Example code

>> pred_dir = Path(Path.cwd(), "examples", "pred") >>> odem = ObjectDetectionEval(true_dir, pred_dir, labels=["cat", "dog"]) >>> odem.confusion_matrix() >>> odem.classification_report() predictions true cat dog None Total cat 1 0 0 1 dog 0 1 3 4 None 2 0 0 2 Total 3 1 3 precision recall f1-score cat 1.00 0.33 0.50 dog 0.25 1.00 0.40">

>>> from pathlib import Path
>>> from odem import ObjectDetectionEval

>>> true_dir = Path(Path.cwd(), "examples", "true")
>>> pred_dir = Path(Path.cwd(), "examples", "pred")

>>> odem = ObjectDetectionEval(true_dir, pred_dir, labels=["cat", "dog"])
>>> odem.confusion_matrix()
>>> odem.classification_report()
        predictions
true     cat       dog       None      Total
cat       1         0         0         1
dog       0         1         3         4
None      2         0         0         2

Total     3         1         3
      precision     recall    f1-score
cat       1.00      0.33      0.50
dog       0.25      1.00      0.40

Author

Louis Philippe Facun

You might also like...

Python Single Object Tracking Evaluation

pysot-toolkit The purpose of this repo is to provide evaluation API of Current Single Object Tracking Dataset, including VOT2016 VOT2018 VOT2018-LT OT

348 Dec 22, 2022

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera. This project prepares training and testing data for various deep learning projects such as 6D object pose estimation projects singleshotpose, as well as object detection and instance segmentation projects.

305 Dec 16, 2022

[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

106 Jan 3, 2023

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Skin Lesion detection using YOLO This project deal

1 Nov 22, 2021

A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API

Timbre Dissimilarity Metrics A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API Installation pip install -e . Usag

21 Jan 5, 2022

Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.

Predicitng_viability Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for

1 Nov 8, 2021

Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

This is the official PyTorch implementation of our paper: "Joint Object Detection and Multi-Object Tracking with Graph Neural Networks". Our project website and video demos are here.

443 Dec 6, 2022

CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

CLOCs is a novel Camera-LiDAR Object Candidates fusion network. It provides a low-complexity multi-modal fusion framework that improves the performance of single-modality detectors. CLOCs operates on the combined output candidates of any 3D and any 2D detector, and is trained to produce more accurate 3D and 2D detection results.

254 Dec 16, 2022

Object Detection and Multi-Object Tracking

1.6k Jan 4, 2023

Object detection evaluation metrics using Python.

Related tags

Overview

Object detection evaluation metrics

Table of contents

Current features

Prerequisites

How to use

Author

You might also like...

Python Single Object Tracking Evaluation

Tools to create pixel-wise object masks, bounding box labels (2D and 3D) and 3D object model (PLY triangle mesh) for object sequences filmed with an RGB-D camera.

[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

This project deals with the detection of skin lesions within the ISICs dataset using YOLOv3 Object Detection with Darknet.

A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API

Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.

Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection

Object Detection and Multi-Object Tracking

Releases(v1.0.0)

Owner

Louis Facun

Using VapourSynth with super resolution models and speeding them up with TensorRT.

Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Flybirds - BDD-driven natural language automated testing framework, present by Trip Flight

K-Nearest Neighbor in Pytorch

Pydantic models for pywttr and aiopywttr.

Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

The DL Streamer Pipeline Zoo is a catalog of optimized media and media analytics pipelines.

Train DeepLab for Semantic Image Segmentation

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

Pseudo lidar - (CVPR 2019) Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

Parallel Latent Tree-Induction for Faster Sequence Encoding

This repo holds codes of the ICCV21 paper: Visual Alignment Constraint for Continuous Sign Language Recognition.

Run object detection model on the Raspberry Pi

Real-Time High-Resolution Background Matting

This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

R interface to fast.ai

EfficientNetV2 implementation using PyTorch

Recommendation algorithms for large graphs

Code release for Universal Domain Adaptation(CVPR 2019)