Datasets:

Javiai
/

failures-3D-print

image_id int64 52 52	width int64 375 1.6k	height int64 277 2.05k	objects dict
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Failures in 3D printing Dataset

This is a small dataset of images from failures in 3D print. That idea of this dataset is use for train and object detection model for failures detection on 3D printing.

In the images it detected 4 categories:

Error: This refer a any error in the part except the type of error known like spaghetti
Extrusor: The base of the extrusor
Part: The part is the piece that is printing
Spagheti: This is a type of error produced because the extrusor is printing on the air

Structure

The structure of the dataset is

image_id: Id of the image
image: Image instance in PIL format
width: Width of the image in pixels
height: Height of the image in pixels
objects: bounding boxes in the images
- bbox: coordinates of the bounding box. The coordinates are [x_center, y_center, bbox width, bbox height]
- categories: category of the bounding box. The categories are 0: error, 1: extrusor, 2: part and 3: spaghetti

Download the dataset

from datasets import load_dataset

dataset = load_dataset('Javiai/failures-3D-print')

Show the Bounding Boxes

import numpy as np
import os
from PIL import Image, ImageDraw

image = dataset["train"][0]["image"]
annotations = dataset["train"][0]["objects"]
draw = ImageDraw.Draw(image)

categories = ['error','extrusor','part','spagheti']

id2label = {index: x for index, x in enumerate(categories, start=0)}
label2id = {v: k for k, v in id2label.items()}

for i in range(len(annotations["categories"])):
    box = annotations["bbox"][i]
    class_idx = annotations["categories"][i]
    x, y, w, h = tuple(box)
    draw.rectangle((x - w/2, y - h/2, x + w/2, y + h/2), outline="red", width=1)
    draw.text((x - w/2, y - h/2), id2label[class_idx], fill="white")

image

Downloads last month: 51

Size of downloaded dataset files:

3.55 MB

Size of the auto-converted Parquet files:

3.55 MB

Number of rows:

Models trained or fine-tuned on Javiai/failures-3D-print

Javiai/3dprintfails-yolo5vs

Object Detection • Updated Oct 6, 2023 • 11 • 5