Javiai/3dprintfails-yolo5vs
Object Detection
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Updated
•
88
•
5
Datasets:
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image_id
int64 52
52
| image
imagewidth (px) 375
1.6k
| width
int64 375
1.6k
| height
int64 277
2.05k
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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
- 59
Size of downloaded dataset files:
3.55 MB
Size of the auto-converted Parquet files:
3.55 MB
Number of rows:
73
Models trained or fine-tuned on Javiai/failures-3D-print