Datasets:

Ryanflash
/

herislab-ca-training-data

language:
  - en
license: mit
task_categories:
  - image-classification
tags:
  - thermal-imaging
  - anomaly-detection
  - autoencoder
  - electrical-fault-detection
  - infrared
size_categories:
  - 10K<n<100K

CA_Training_Data -- Convolutional Autoencoder (Track A)

Curated dataset for training and evaluating the Convolutional Autoencoder anomaly detection model.

Approach

The autoencoder is trained only on normal (no-fault) images. At inference, high reconstruction error indicates an anomaly/fault.

Structure

train/normal/          -- Normal images for autoencoder training
  electric_motor/      -- 168 PNG  (Electric Motor Thermal Fault Diagnosis, no_fault class)
  induction_motor/     -- 20 BMP   (Thermal Images of Induction Motor, Noload class)
  pv_om_inspection/    -- 7,836 TIFF (PV System O&M Inspection, double-row + single-row)
  pv_thermal_inspection/ -- 1,075 TIFF (PV System Thermal Inspection)
  solar_modules/       -- 2,302 JPG (Infrared Solar Modules, No-Anomaly class)

test/normal/           -- Held-out normal images for threshold calibration
  electric_motor/      -- 28 PNG
  induction_motor/     -- 5 BMP

test/fault/            -- Fault images for evaluating anomaly detection
  electric_motor/      -- 173 PNG  (Electric Motor Thermal Fault Diagnosis, fault class)
  induction_motor/     -- 344 BMP  (Thermal Images of Induction Motor, 10 fault conditions)

Total Counts

Split	Normal	Fault	Total
Train	11,401	0	11,401
Test	33	517	550

Source Datasets

Directory	Source Dataset	Domain
electric_motor	Electric Motor Thermal image Fault Diagnosis DATASET	Electrical (primary)
induction_motor	Thermal Images of Induction Motor Dataset	Electrical (primary)
pv_om_inspection	Photovoltaic System O&M inspection	Solar PV (adjacent)
pv_thermal_inspection	Photovoltaic system thermal inspection	Solar PV (adjacent)
solar_modules	Infrared Solar Modules (No-Anomaly only)	Solar PV (adjacent)

Notes

PV O&M files are prefixed dr_ (double-row) and sr_ (single-row) to avoid filename collisions
Solar module images were filtered from module_metadata.json (anomaly_class == "No-Anomaly")
Test/normal hold-out is ~14-20% of electrical equipment normal images
Image formats are mixed (PNG, BMP, TIFF, JPG) -- preprocessing/normalization is required before training