upload parquet

README.md

@@ -29,7 +29,7 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: metadata.csv
+    path: data/train-*.parquet
 ---
 
 # Nonastreda: Multimodal Dataset for Tool Wear State Monitoring
@@ -51,24 +51,52 @@ This Hugging Face repository is a mirror and machine-learning-friendly access po
 
 The dataset combines visual inspection data, force-signal-derived image representations, and raw force signals for tool wear monitoring. Each sample is indexed by an identifier such as `T10R10B1`, and the same identifier is used across the available image modalities.
 
-Each sample-level row in `metadata.csv` includes:
+Each sample-level row includes:
 
+- `tool_id` and `run_id`, parsed from the sample identifier,
 - one microscope-image-based tool-wear classification label,
 - one additional force-phase classification label derived from force-signal amplitudes,
 - three regression targets,
-- paths to nine image modalities.
+- nine image modalities.
 
 The raw force-signal file is also included for future research, but it is not used as a sample-level visualization table because the sequences are organized per tool rather than cut into sub-sequences for each blade/image sample.
 
 ## Modalities
 
-Each sample is represented by nine image files:
+Each sample is represented by nine image modalities:
 
-| Modality group | Columns in `metadata.csv` | Description |
+| Modality group | Viewer columns | Description |
 |---|---|---|
-| Visual images | `chip_file_name`, `tool_file_name`, `work_file_name` | Images of chip, tool blade, and workpiece |
-| Mel-spectrograms | `spec_x_file_name`, `spec_y_file_name`, `spec_z_file_name` | Mel-spectrogram representations of force-signal axes X, Y, and Z |
-| Scalograms | `scal_x_file_name`, `scal_y_file_name`, `scal_z_file_name` | Scalogram representations of force-signal axes X, Y, and Z |
+| Visual images | `chip`, `tool`, `work` | Images of chip, tool blade, and workpiece |
+| Mel-spectrograms | `spec_x`, `spec_y`, `spec_z` | Mel-spectrogram representations of force-signal axes X, Y, and Z |
+| Scalograms | `scal_x`, `scal_y`, `scal_z` | Scalogram representations of force-signal axes X, Y, and Z |
+
+The human-readable `metadata.csv` file keeps the corresponding file path columns, such as `chip_file_name`, `tool_file_name`, and `scal_z_file_name`.
+
+## Dataset split and recommended evaluation protocol
+
+This repository provides a single Hugging Face split:
+
+- `train`: all 512 samples.
+
+No fixed train/test split is imposed. For model evaluation, we recommend **leave-one-tool-out cross-validation** using `tool_id`.
+
+Since the dataset contains 10 tools, this gives 10 train/test folds. In each fold, samples from one tool are held out for testing, and samples from the remaining nine tools are used for training. This protocol evaluates whether a model generalizes to unseen tools rather than only learning tool-specific visual or signal patterns.
+
+Example fold definition:
+
+```python
+from datasets import load_dataset
+
+NUM_TOOLS = 10
+
+ds = load_dataset("hubtru/nonastreda", split="train")
+
+for held_out_tool in range(1, NUM_TOOLS + 1):
+    train_ds = ds.filter(lambda x: x["tool_id"] != held_out_tool)
+    test_ds = ds.filter(lambda x: x["tool_id"] == held_out_tool)
+    print(f"Fold T{held_out_tool}: train={len(train_ds)}, test={len(test_ds)}")
+```
 
 ## Tasks
 
@@ -113,7 +141,7 @@ The `image_label` column is the primary classification label described in the as
 - `used`
 - `dulled`
 
-For convenience, `metadata.csv` also provides `image_label_id`:
+For convenience, the dataset also provides `image_label_id`:
 
 - `sharp = 1`
 - `used = 2`
@@ -167,6 +195,9 @@ nonastreda/
 ├── CITATION.cff
 ├── LICENSE
 ├── metadata.csv
+├── metadata.parquet
+├── data/
+│   └── train-00000-of-00001.parquet
 ├── chip/
 ├── tool/
 ├── work/
@@ -190,11 +221,13 @@ nonastreda/
 
 ## Metadata columns
 
-`metadata.csv` is the recommended entry point for machine-learning use and for the Hugging Face Dataset Viewer.
+`metadata.csv` is the recommended human-readable index. The default Hugging Face Dataset Viewer is configured to use `data/train-*.parquet`, where the nine image modalities are stored as image columns rather than file-name strings.
 
 | Column | Description |
 |---|---|
 | `sample_id` | Unique sample identifier |
+| `tool_id` | Tool identifier parsed from `sample_id`, e.g. `T10R14B3` → `10` |
+| `run_id` | Run identifier parsed from `sample_id`, e.g. `T10R14B3` → `14` |
 | `image_label` | Primary microscope-image-based class label: `sharp`, `used`, or `dulled` |
 | `image_label_id` | Numeric encoding of `image_label`: 1, 2, or 3 |
 | `force_phase_label` | Additional force-amplitude-based wear phase label: 1, 2, or 3 |
@@ -203,15 +236,15 @@ nonastreda/
 | `gaps` | Regression target |
 | `flank_wear` | Regression target |
 | `overhang` | Regression target |
-| `chip_file_name` | Path to chip image |
-| `tool_file_name` | Path to tool blade image |
-| `work_file_name` | Path to workpiece image |
-| `spec_x_file_name` | Path to X-axis Mel-spectrogram image |
-| `spec_y_file_name` | Path to Y-axis Mel-spectrogram image |
-| `spec_z_file_name` | Path to Z-axis Mel-spectrogram image |
-| `scal_x_file_name` | Path to X-axis scalogram image |
-| `scal_y_file_name` | Path to Y-axis scalogram image |
-| `scal_z_file_name` | Path to Z-axis scalogram image |
+| `chip_file_name` | Path to chip image in `metadata.csv` |
+| `tool_file_name` | Path to tool blade image in `metadata.csv` |
+| `work_file_name` | Path to workpiece image in `metadata.csv` |
+| `spec_x_file_name` | Path to X-axis Mel-spectrogram image in `metadata.csv` |
+| `spec_y_file_name` | Path to Y-axis Mel-spectrogram image in `metadata.csv` |
+| `spec_z_file_name` | Path to Z-axis Mel-spectrogram image in `metadata.csv` |
+| `scal_x_file_name` | Path to X-axis scalogram image in `metadata.csv` |
+| `scal_y_file_name` | Path to Y-axis scalogram image in `metadata.csv` |
+| `scal_z_file_name` | Path to Z-axis scalogram image in `metadata.csv` |
 
 ## Raw files
 
@@ -232,7 +265,7 @@ ds = load_dataset("hubtru/nonastreda")
 print(ds["train"][0])
 ```
 
-To load the training split directly:
+To load the single split directly:
 
 ```python
 from datasets import load_dataset
@@ -241,27 +274,26 @@ ds = load_dataset("hubtru/nonastreda", split="train")
 print(ds[0])
 ```
 
-To load the metadata table directly after cloning or downloading the repository:
+Example image access:
 
 ```python
-import pandas as pd
+from datasets import load_dataset
 
-metadata = pd.read_csv("metadata.csv")
-print(metadata.head())
+ds = load_dataset("hubtru/nonastreda", split="train")
+row = ds[0]
+
+chip_image = row["chip"]
+tool_image = row["tool"]
+work_image = row["work"]
 ```
 
-Example image loading after cloning or downloading the repository:
+To load the human-readable metadata table after cloning or downloading the repository:
 
 ```python
-from PIL import Image
 import pandas as pd
 
 metadata = pd.read_csv("metadata.csv")
-row = metadata.iloc[0]
-
-chip = Image.open(row["chip_file_name"])
-tool = Image.open(row["tool_file_name"])
-work = Image.open(row["work_file_name"])
+print(metadata.head())
 ```
 
 ## Intended use
@@ -280,6 +312,8 @@ This dataset is intended for research in:
 ## Limitations and notes
 
 - The dataset contains 512 sample-level records.
+- The Hugging Face `train` split contains all samples; no fixed test split is imposed.
+- For evaluation, leave-one-tool-out cross-validation using `tool_id` is recommended.
 - The default Hugging Face viewer is centered on sample-level image modalities and labels.
 - The raw force signals are included for completeness but require separate handling.
 - The two classification labels are derived from different sources and should not be assumed to be equivalent.
@@ -323,4 +357,3 @@ If you use this dataset, please cite the associated *Data in Brief* article and
 ## Contact
 
 For questions about the dataset, please refer to the contact information provided in the associated *Data in Brief* article and Mendeley Data record.
-

data/train-00000-of-00001.parquet

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