Ryanflash/herislab-ca-training-data
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… See the full description on the dataset page: https://huggingface.co/datasets/Ryanflash/herislab-ca-training-data.
CATrainingData -- 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
Source Datasets
Notes
- PV O&M files are prefixed
dr_(double-row) andsr_(single-row) to avoid filename collisions - Solar module images were filtered from modulemetadata.json (anomalyclass == "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
