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image imagewidth (px) 512 512 | label class label 3
classes |
|---|---|
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
0background | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
1coffee-mug | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp | |
2lamp |
Synthetic Image-Classification Dataset
Synthetic image-classification dataset generated with stable diffusion (zerogpu_sdxl_turbo) using text-to-image from class names + short descriptions.
Classes
| Label | Images |
|---|---|
background |
20 |
coffee-mug |
20 |
lamp |
20 |
Layout
train/<label>/<label>.<id>.jpg
test/<label>/<label>.<id>.jpg
metadata.csv
Loading
from datasets import load_dataset
ds = load_dataset("imagefolder", data_dir="coffee-lamp")
# or, once pushed to the Hub:
ds = load_dataset("eoinedge/coffee-lamp")
print(ds)
Edge Impulse
Filenames use the label.<id>.jpg convention, so they upload directly:
edge-impulse-uploader --category training train/**/*.jpg
Notes
Synthetic images are a bootstrap for image-classification models. Validate with real captures from the target device's camera before deployment.
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