Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

lonlonago/African-Animals-Detection-Dataset-VOC-YOLO-Format-1418-Images-in-5-Classes

Type de record:

dataset
Créateur:
lon
Hôte:
dataset page (readme + preview images) # African Animals Detection Dataset: VOC+YOLO Format, 1418 Images in 5 Classes The dataset format is Pascal VOC format combined with YOLO format (txt file without split paths, only containing jpg images, along with corresponding VOC format xml files and YOLO format txt files). Number of images (jpg file count): 1418 Number of annotations (xml file count): 1418 Number of annotations (txt file count): 1418 Number of annotation categories: 5 Annotation category names (note that the order of categories in YOLO format may not correspond to this, but refer to the labels folder classes.txt for consistency): ["Elephant", "Giraffe", "Leopard", "Rhino", "lion"] Each category's number of bounding boxes: - Elephant (elephant) bounding box count = 418, occupying image count = 278 - Giraffe (giraffe) bounding box count = 330, occupying image count = 254 - Leopard (leopard) bounding box count = 360, occupying image count = 296 - Rhino (rhinoceros) bounding box count = 430, occupying image count = 296 - Lion (lion) bounding box count = 410, occupying image count = 294 Total bounding box count: 1948 Image resolution: 640x640 Annotation tool used: labelImg Annotation rules: Drawing bounding boxes for each category Important note: The dataset does not have separate training, validation, or test sets; they need to be manually divided. Special declaration: This dataset does not guarantee the accuracy of models trained on it or weight files. Image preview: ## Images Here is a pay link on Stripe ( buy.stripe.com ). Please contact me lonlonago@foxmail.com after funding $89, and I will send you a complete data files , thank you!

Visit

github.com

Tasks

image classificationcomputer vision

Similaires

INDIGENOUS WEAPON DETECTION IN IMAGES FOR ENHANCED SECURITY IN NIGERIA USING YOLO APPROACHComparative Evaluation of YOLO Models on an African Road Obstacles Dataset for Real-Time Obstacle DetectionWild Animals 5-B 20150921bSesame Plant Segmentation Dataset: A YOLO Formatted Annotated DatasetBertrand-noubissi/Traffic-Conflict-Detection-Yaounde-with-Yolo-Cow Images 5 20150826a

INDIGENOUS WEAPON DETECTION IN IMAGES FOR ENHANCED SECURITY IN NIGERIA USING YOLO APPROACH

This work explores the use of the ‘You Only Look Once’ (YOLO) deep learning model, for real-time wea

Comparative Evaluation of YOLO Models on an African Road Obstacles Dataset for Real-Time Obstacle Detection

Public datasets are used to train road obstacle detection models, but they lack diverse and rare obj

Wild Animals 5-B 20150921b

Ayi Raheli, Tluwáy, and Safari chat about wild animals, particularly snakes || Ayi Raheli Tluwáy, na

Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset

This paper presents the Sesame Plant Segmentation Dataset, an open source annotated image dataset de

Bertrand-noubissi/Traffic-Conflict-Detection-Yaounde-with-Yolo-

Video-based traffic conflict safety evaluation of road intersections in Yaoundé, Cameroon, using YOL

Cow Images 5 20150826a

Safari attempts to choose a cow image corresponding to the one described by Tluwáy || Safari anajari