Datasets:
File size: 2,407 Bytes
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---
license: unknown
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: int64
splits:
- name: train
num_bytes: 3878997
num_examples: 73
download_size: 3549033
dataset_size: 3878997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- object-detection
size_categories:
- n<1K
---
# 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
''' |