BUTTONS Object Detection Dataset by Buttons

Images

25 images

Explore Dataset

detect.py --source C:\Users\Sara\Desktop\yolov5\train\images --weights C:\Users\Sara\Desktop\yolov5\runs\train\yolo_det12\weights\best.pt --conf 0.25 --name yolo_det

import torch from IPython.display import Image import os import random import shutil from sklearn.model_selection import train_test_split import xml.etree.ElementTree as ET from xml.dom import minidom from tqdm import tqdm from PIL import Image, ImageDraw import numpy as np import matplotlib.pyplot as plt random.seed(108) detections_dir = "C:\Users\Sara\Desktop\yolov5\runs\detect\yolo_det2" detection_images = [os.path.join(detections_dir, x) for x in os.listdir(detections_dir)] random_detection_image = Image.open(random.choice(detection_images)) plt.imshow(np.array(random_detection_image)) plt.show()

curl -L "https://app.roboflow.com/ds/YRn2JumHuC?key=kFqXQBEMup" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip

train.py --img 640 --cfg yolov5s.yaml --hyp hyp.scratch.yaml --batch 32 --epochs 100 --data data.yaml --weights yolov5s.pt --workers 24 --name yolo_det

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            buttons-43x73_dataset,
                            title = { BUTTONS Dataset },
                            type = { Open Source Dataset },
                            author = { Buttons },
                            howpublished = { \url{ https://universe.roboflow.com/buttons/buttons-43x73 } },
                            url = { https://universe.roboflow.com/buttons/buttons-43x73 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2021 },
                            month = { sep },
                            note = { visited on 2024-06-21 },
                            }