Metadata-Version: 2.4
Name: justdata
Version: 1.2.1
Summary: A TensorFlow-native data pipeline library with a modality-neutral core and first-class computer vision and acoustic recipes
Author: clementpoiret
Author-email: clementpoiret <clement@linux.com>
License-Expression: MIT
License-File: LICENSE
Requires-Dist: loguru>=0.7.3
Requires-Dist: tensorflow>=2.18.1
Requires-Dist: tensorflow-datasets>=4.9.9
Requires-Dist: datasets[audio]>=4.5.0 ; extra == 'acoustic'
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Requires-Dist: soxr>=0.5 ; extra == 'acoustic'
Requires-Dist: librosa>=0.11 ; extra == 'acoustic'
Requires-Dist: torch==2.10.0 ; extra == 'acoustic'
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Requires-Dist: datasets[audio]>=4.5.0 ; extra == 'dev'
Requires-Dist: datasets[vision]>=4.5.0 ; extra == 'dev'
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Requires-Dist: torchaudio==2.10.0 ; extra == 'golden'
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Requires-Dist: datasets[vision]>=4.5.0 ; extra == 'vision'
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Requires-Dist: pillow>=12.2.0 ; extra == 'vision'
Requires-Dist: wilds>=2.0.0 ; extra == 'wilds'
Requires-Python: >=3.11, <3.14
Provides-Extra: acoustic
Provides-Extra: dev
Provides-Extra: golden
Provides-Extra: vision
Provides-Extra: wilds
Description-Content-Type: text/markdown

# justdata

A TensorFlow-native data pipeline library with a modality-neutral core and first-class computer vision and acoustic
recipes. `justdata.core` owns loading, adapter, preset, metadata, padding, and seeded execution machinery;
`justdata.vision` owns image schemas, transforms, augmentations, corruptions, tasks, and vision presets.
`justdata.acoustic` owns audio schemas, decoding, resampling, segmentation, frontends, augmentations, corruptions, DCASE
helpers, stats, metadata/JAX helpers, and acoustic presets.

______________________________________________________________________

## Table of Contents

1. [Installation](#installation)
1. [Architecture](#architecture)
1. [Data Pipeline Stages](#data-pipeline-stages)
1. [Supervised Learning — Training Pipelines](#supervised-learning--training-pipelines)
1. [Automatic Augmentation Policies: RandAugment, TrivialAugment, and TrivialAugmentWide](#automatic-augmentation-policies-randaugment-trivialaugment-and-trivialaugmentwide)
1. [Supervised Learning — Validation Pipelines](#supervised-learning--validation-pipelines)
1. [The `timm` ImageNet Recipes (ResNet Strikes Back: A1/A2/A3)](#the-timm-imagenet-recipes-resnet-strikes-back-a1a2a3)
1. [Self-Supervised Learning Pipeline (DINOv2)](#self-supervised-learning-pipeline-dinov2)
1. [Registry System](#registry-system)
1. [Presets and Smart Merging](#presets-and-smart-merging)
1. [Acoustic Pipelines](#acoustic-pipelines)
1. [Dataset Adapters](#dataset-adapters)
1. [Mini-C Corruption Benchmark](#mini-c-corruption-benchmark)
1. [Audio Corruption Benchmark](#audio-corruption-benchmark)
1. [Usage](#usage)
1. [Development](#development)

______________________________________________________________________

## Installation

```bash
pip install justdata
```

**Requirements:** Python 3.11–3.13, TensorFlow ≥ 2.18.1, TensorFlow Datasets ≥ 4.9.9. Install optional modality extras
with `justdata[vision]` for Hugging Face vision datasets, `justdata[wilds]` for WILDS image classification datasets, or
`justdata[acoustic]` for audio dataset/source dependencies.

______________________________________________________________________

## Architecture

`justdata` is structured around a fixed, four-stage pipeline abstraction. The currently supported vision tasks,
classification, semantic segmentation, and panoptic segmentation, each expose exactly four composable functions. Object
detection and depth estimation are future scope and are not registered capabilities.

```
(preprocess_fn, augment_fn, late_augment_fn, postprocess_fn)
```

These are assembled by `justdata.core.load_ds` into the following execution graph:

```
fetch_ds -> adapter -> preprocess -> cache -> augment -> shuffle -> postprocess -> batch -> late_augment -> pad -> prefetch
```

The strict ordering reflects the execution domain requirements articulated throughout this document: format
normalization occurs before the optional preprocessing cache; spatial and photometric distortions precede tensor
conversion and normalization; and batch-level operations (Mixup, CutMix, random erasing) occur after batching on the
GPU. An optional model-input cache can be inserted after deterministic postprocessing and before batching; for training
it must be explicitly opted in and is placed before shuffle so epoch order is not frozen.

### TensorFlow execution controls

`load_ds` uses `tf.data.AUTOTUNE` for loader-owned maps by default. Advanced callers may set `map_parallel_calls`,
`private_threadpool_size`, and `max_intra_op_parallelism` to bound TensorFlow input-pipeline concurrency. Omitting these
arguments preserves the default TensorFlow behavior. The map limit also applies to metadata sidecar writing when
enabled.

### Core Public API

| Function                                                              | Description                                                                                                          |
| :-------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------- |
| `justdata.core.fetch_ds(dataset_names, splits_info, data_dir)`        | Raw dataset loading through registered source loaders. Returns a `tf.data.Dataset` in canonical schema.              |
| `justdata.core.load_ds(...)`                                          | Full pipeline for training or evaluation. Handles caching, augmentation, shuffling, batching, and prefetching.       |
| `justdata.core.admit_inventory(...)`                                  | Strict offline validation of a resolved inventory into a disk snapshot with structured identity and failure reports. |
| `justdata.core.open_inventory(snapshot_dir)`                          | Verify and reopen a completed inventory snapshot without original sources.                                           |
| `justdata.core.load_inventory(admitted, ...)`                         | Apply the shared pipeline to an admitted inventory, including raw epoch finalization.                                |
| `justdata.core.ExecutedConfig`                                        | Immutable, canonical snapshot returned by loaders when `return_config=True`.                                         |
| `justdata.vision.minic.create_minic_datasets(...)`                    | Constructs Mini-C corruption benchmark datasets from a shared preprocessed base dataset.                             |
| `justdata.acoustic.corruptions.create_audio_corruption_datasets(...)` | Constructs acoustic corruption benchmark datasets from a shared preprocessed base dataset.                           |
| `justdata.acoustic.dcase2025.make_source_dataset(...)`                | Builds DCASE Task 1 source-domain datasets with split-safety checks.                                                 |

Import `justdata.vision` before resolving built-in vision datasets or pipelines. Hugging Face vision datasets are
referenced with the `hf:` prefix (e.g., `hf:cifar10`), and WILDS image classification datasets are referenced with the
`wilds:` prefix (e.g., `wilds:camelyon17`).

Use the [strict local inventory API](docs/inventory.md) when every requested source and record must be accounted for. It
verifies expected content digests, preserves record IDs and order, and reports declared filtering. Existing `fetch_ds`
and `load_ds` retain their legacy source-skipping behavior and do not provide the strict admission guarantee.

______________________________________________________________________

## Data Pipeline Stages

### Stage 1: Preprocessing (before cache)

Format normalization is performed before the optional decoded-dataset cache. Operations include rank fixing (2D -> 3D),
CHW -> HWC transposition, grayscale -> RGB expansion, and RGBA -> RGB projection. The output shape is forced to
`[None, None, 3]`.

### Stage 2: Augmentation (per-sample, training only)

Per-sample spatial and photometric augmentations are applied after the optional cache stage, ensuring that each training
epoch receives independently sampled augmentations. All augmentation functions use `tf.random.split` for stateless
randomization, enabling full reproducibility.

### Stage 3: Postprocessing (every sample)

Resize, channel-wise normalization to zero mean and unit standard deviation, and optional HWC -> CHW transposition. This
stage runs unconditionally for both training and evaluation.

Set `cache_model_inputs=True` with `model_input_cache_path` to cache these resized, normalized tensors on local SSD. For
`dataset_type="train"`, also set `allow_train_model_input_cache=True` and use it only when the training view is
deterministic; stochastic crops or photometric augmentation will be materialized into the cache on first fill.

### Stage 4: Late Augmentation (per-batch, training only)

Batch-level operations—Mixup, CutMix, and random erasing—are applied after batching. These operations are strictly
training-only and require a formed batch to operate across the sample dimension.

______________________________________________________________________

## Supervised Learning — Training Pipelines

To ensure mathematical correctness, the training pipeline is strictly segregated by execution domain. Spatial and
photometric distortions are applied to `[0, 255]` image tensors (PIL-equivalent domain) before zero-mean tensor
normalization.

### 1. Image Domain Augmentations

#### Geometric Augmentations

- **CIFAR-10/100:** `RandomCrop(32, padding=4, padding_mode='zeros')` -> `RandomHorizontalFlip(p=0.5)`.
- **ImageNet-1K:** `RandomResizedCrop(size=224)` (or **256**). Scale: **(0.08, 1.0)**, Aspect ratio: **(0.75, 1.33)**,
  Interpolation: Bicubic. -> `RandomHorizontalFlip(p=0.5)`.

#### Photometric Augmentations (Mutually Exclusive Branches)

The photometric augmentation strategy is architecture-dependent and the two branches are mutually exclusive.

- **Modern Branch (ViT / ConvNeXt):** Apply `RandAugment(num_ops=2, magnitude=9)`, `TrivialAugment`, or
  `TrivialAugmentWide` (see the
  [Automatic Augmentation Policies](#automatic-augmentation-policies-randaugment-trivialaugment-and-trivialaugmentwide)
  section for full specifications). Color Jitter is explicitly disabled to prevent redundant and destructive color space
  distortion.
- **Legacy Branch (ResNet):** Apply `ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1)`. RandAugment is
  disabled.

### 2. Tensor Domain

- **Conversion & Scaling:** `ToImage()` -> `ToDtype(float32, scale=True)`. Maps **[0, 255]** -> **[0.0, 1.0]**.
- **Normalization:**
  - **CIFAR-10:** `mean=(0.4914, 0.4822, 0.4465)`, `std=(0.2023, 0.1994, 0.2010)`
  - **CIFAR-100:** `mean=(0.5071, 0.4867, 0.4408)`, `std=(0.2675, 0.2565, 0.2761)`
  - **ImageNet-1K:** `mean=(0.485, 0.456, 0.406)`, `std=(0.229, 0.224, 0.225)`
- **Random Erasing (Cutout):** Applied to the normalized tensor. `p=0.25`, `scale=(0.02, 0.33)`, `ratio=(0.3, 3.3)`.

### 3. Mini-Batch Domain

Applied across the batch dimension during training.

- **Repeated Augmentation (RA):** Enabled for ViTs. Typically **3** repetitions per sample per mini-batch.
- **Mixup & CutMix:** Controlled by `mixup_prob=1.0` (probability of batch mixing) and `switch_prob=0.5` (probability of
  selecting CutMix over Mixup).
  - **Mixup:** $\tilde{x} = \lambda x_i + (1 - \lambda) x_j$, where $\lambda \sim \mathrm{Beta}(0.8,, 0.8)$.
  - **CutMix:** Replaces a rectangular bounding box region; $\lambda \sim \mathrm{Beta}(1.0,, 1.0)$.
  - *Note: The $\alpha$ values (0.8 and 1.0) reflect the DeiT baseline. Mixup/CutMix parameters are strictly
    recipe-dependent; see the A1/A2/A3 section for ResNet-specific variations.*
- **Label Smoothing:** Cross-entropy loss modification with $\varepsilon = 0.1$.

______________________________________________________________________

## Automatic Augmentation Policies: RandAugment, TrivialAugment, and TrivialAugmentWide

`justdata` provides native TensorFlow implementations of three closely related automatic augmentation policies:
**RandAugment** (Cubuk et al., 2020), **TrivialAugment** (Müller & Hutter, 2021), and **TrivialAugmentWide** (Müller &
Hutter, 2021). All three are registered under the `augment_strategy` registry and share a common operation pool and
magnitude discretization framework.

### 1. Pipeline Position and Domain Constraints

To guarantee deterministic reproduction of published results, all three policies are applied exclusively in the **image
domain** (integer `uint8` tensors, pixel values $\in [0, 255]$), before tensor conversion to floating-point and
channel-wise normalization. The canonical execution order within the per-sample augmentation stage is:

1. Random Resized Crop (or Random Pad Crop for CIFAR)
1. **RandAugment / TrivialAugment / TrivialAugmentWide**
1. Random Horizontal Flip *(integrated into the crop strategy)*
1. `ToTensor` — scales to float $[0.0, 1.0]$ *(postprocessing stage)*
1. `Normalize` — subtracts dataset mean, divides by standard deviation *(postprocessing stage)*

Applying photometric and geometric distortions prior to floating-point conversion ensures that operations such as
`Posterize` and `Solarize`, which are defined on integer pixel arithmetic, remain numerically well-founded, and that
fill values for geometric operations are expressed in the same integer domain as the source image.

### 2. The RA Operation Space ($K = 14$)

All three algorithms draw from the **RA augmentation space**, a fixed pool of $K = 14$ operations. The pool is
partitioned into two subsets based on magnitude dependency.

#### Magnitude-Independent Operations (3 ops)

These operations ignore the sampled magnitude $m$ entirely.

| Operation      | Description                                                                                                 |
| :------------- | :---------------------------------------------------------------------------------------------------------- |
| `Identity`     | Returns the image unmodified.                                                                               |
| `AutoContrast` | Linearly scales the pixel intensity histogram so that the darkest pixel maps to 0 and the brightest to 255. |
| `Equalize`     | Equalizes the image histogram per channel using a cumulative distribution function.                         |

#### Magnitude-Dependent Operations (11 ops)

These operations scale their physical intensity as a function of the sampled magnitude index $m$.

**Sign randomization.** For all geometric operations (`Rotate`, `ShearX`, `ShearY`, `TranslateX`, `TranslateY`) and all
color enhancement operations (`Brightness`, `Color`, `Contrast`, `Sharpness`), the direction of the applied
transformation is randomized bidirectionally. Given a raw physical magnitude $v$ computed from $m$, the applied value
is:

$$v' = v \cdot s, \quad s \sim \mathcal{U}\{-1, +1\}$$

This sign randomization is sampled independently per operation per sample, using a stateless seed derived from the
layer's random state.

**Geometric fill.** When pixels are shifted outside the image boundary by `Rotate`, `ShearX`, `ShearY`, `TranslateX`, or
`TranslateY`, the vacated regions are filled with a constant value (default: 128). Geometric operations use the
implementation's fixed nearest-neighbor interpolation for images.

For segmentation pipelines, these geometric operations reuse the same sampled parameters for the image and segmentation
map. The image uses bilinear interpolation, while the map uses nearest-neighbor interpolation and preserves its integer
dtype. Vacated map pixels use the configurable `mask_fill_value` (default `255`), which must be treated as an ignore
label by the training loss. Photometric operations continue to affect only the image.

**Solarize semantics.** `Solarize` inverts all pixel values that are greater than or equal to the computed threshold
$\tau$. Formally, for each pixel $p$:

$$p' = \begin{cases} 255 - p & \text{if } p \geq \tau \\ p & \text{otherwise} \end{cases}$$

where $\tau = 255 \cdot (1 - m / B)$. At $m = 0$, the threshold is 255 (no pixels inverted); at $m = B$, the threshold
is 0 (all pixels inverted).

### 3. Magnitude Discretization and Physical Mappings

The magnitude scale is a **31-bin discrete framework** with bin indices $m \in \{0, 1, \ldots, 30\}$ and $B = 30$ (the
maximum bin index). This parameterization is the native scale of the `torchvision` (v0.13+) implementation of both
RandAugment and TrivialAugmentWide.

The table below specifies the physical mapping formula and the operation-specific bound for each of the two spaces. $W$
and $H$ denote the input image width and height at the moment the augmentation is applied.

| Operation        | Sign Rand. | Physical Mapping                                                | Standard Bound (RA / TA)          | Wide Bound (TA-Wide)              |
| :--------------- | :--------: | :-------------------------------------------------------------- | :-------------------------------- | :-------------------------------- |
| **Rotate**       |    Yes     | $(m / B) \cdot \text{MaxDeg} \cdot s$                           | 30.0°                             | 135.0°                            |
| **TranslateX**   |    Yes     | $(m / B) \cdot \text{MaxPx}_X \cdot s$                          | $(150 / 331) \times W$            | 32.0 px                           |
| **TranslateY**   |    Yes     | $(m / B) \cdot \text{MaxPx}_Y \cdot s$                          | $(150 / 331) \times H$            | 32.0 px                           |
| **ShearX**       |    Yes     | $(m / B) \cdot \text{MaxShear} \cdot s$                         | 0.3                               | 0.99                              |
| **ShearY**       |    Yes     | $(m / B) \cdot \text{MaxShear} \cdot s$                         | 0.3                               | 0.99                              |
| **Brightness**   |    Yes     | $1.0 + (m / B) \cdot \text{MaxDelta} \cdot s$                   | 0.9                               | 0.99                              |
| **Color**        |    Yes     | $1.0 + (m / B) \cdot \text{MaxDelta} \cdot s$                   | 0.9                               | 0.99                              |
| **Contrast**     |    Yes     | $1.0 + (m / B) \cdot \text{MaxDelta} \cdot s$                   | 0.9                               | 0.99                              |
| **Sharpness**    |    Yes     | $1.0 + (m / B) \cdot \text{MaxDelta} \cdot s$                   | 0.9                               | 0.99                              |
| **Posterize**    |     No     | $8 - \mathrm{round}\!\left((m / B) \cdot \text{MaxBits}\right)$ | MaxBits = 4 (min 4 bits retained) | MaxBits = 6 (min 2 bits retained) |
| **Solarize**     |     No     | $255.0 \cdot (1 - m / B)$                                       | threshold down to 0               | threshold down to 0               |
| **Identity**     |     —      | —                                                               | —                                 | —                                 |
| **AutoContrast** |     —      | —                                                               | —                                 | —                                 |
| **Equalize**     |     —      | —                                                               | —                                 | —                                 |

> **Note on TranslateX / TranslateY:** Translation is a deliberate exception to the "wider bounds" pattern. The TA-Wide
> space specifies a fixed ceiling of 32 px for both axes. For any image dimension exceeding approximately 71 px, the
> Standard RA space—whose ceiling scales proportionally with the image dimension—admits a **larger** maximum translation
> than the Wide space.

### 4. Algorithm Specifications

#### 4.1 RandAugment (RA)

RandAugment (Cubuk et al., 2020) reduces the search space of AutoAugment from $\mathcal{O}(K^N)$ policies to two scalar
hyperparameters: the number of sequential operations $N$ and the global magnitude $M$.

**Procedure.** Given a training image $x$:

1. Independently sample $N$ operation indices $k_1, \ldots, k_N$ uniformly at random **with replacement** from the pool
   of $K$ operations.
1. For each selected operation $T_{k_i}$, map the fixed global magnitude $M$ to a physical parameter via the
   operation-specific formula in the table above, applying sign randomization where applicable.
1. Apply the operations sequentially: $x \leftarrow T_{k_N}(\cdots T_{k_1}(x))$.

The magnitude $M$ is expressed on the **31-bin scale** ($M \in \{0, \ldots, 30\}$) used throughout `justdata`. An
optional magnitude perturbation $\sigma > 0$ samples the effective level per layer from $\mathcal{N}(M, \sigma^2)$,
clipped to $[0, B]$.

**API parameters** (`augment_strategy = "rand_augment"` or direct call to `rand_augment`):

| Parameter            | Type                | Default | Description                                                                                           |
| :------------------- | :------------------ | :------ | :---------------------------------------------------------------------------------------------------- |
| `num_layers`         | `int`               | 2       | Number of operations $N$ applied per image.                                                           |
| `magnitude`          | `float`             | 9.0     | Global magnitude $M$ on the 31-bin scale.                                                             |
| `magnitude_std`      | `float`             | 0.0     | Per-layer Gaussian magnitude noise $\sigma$. Set to 0.5 to replicate `timm` stochastic magnitude.     |
| `prob_to_apply`      | `float \| None`     | `None`  | If set, each layer is skipped with probability $1 - p$.                                               |
| `rotate_max`         | `float`             | 30.0    | Maximum rotation angle in degrees.                                                                    |
| `shear_max`          | `float`             | 0.3     | Maximum shear coefficient (Standard RA space).                                                        |
| `enhance_max`        | `float`             | 0.9     | Maximum delta for brightness, color, contrast, sharpness.                                             |
| `posterize_max_bits` | `int`               | 4       | Bits removed at maximum magnitude; min retained = $8 - \text{MaxBits}$.                               |
| `translate_const`    | `float`             | 100.0   | Absolute translate ceiling in pixels. For Standard RA, set to $(150/331) \times \text{image\_width}$. |
| `exclude_ops`        | `list[str] \| None` | `None`  | Operations to exclude from the sampling pool.                                                         |

**Published optimal hyperparameters and 31-bin scale conversion.** The original paper (Cubuk et al., 2020) reports
hyperparameters on an **11-level scale** ($M_{\text{paper}} \in \{0, \ldots, 10\}$). To replicate those results using
the 31-bin scale employed by `justdata`, apply the conversion $m = M_{\text{paper}} \times 3$.

| Dataset     | Architecture           | $N$ | $M_{\text{paper}}$ | $m$ (31-bin) |
| :---------- | :--------------------- | :-: | :----------------: | :----------: |
| CIFAR-10    | WideResNet-28-2        |  3  |         4          |      12      |
| CIFAR-10    | WideResNet-28-10       |  3  |         5          |      15      |
| CIFAR-10    | PyramidNet + ShakeDrop |  3  |         7          |      21      |
| CIFAR-10    | Shake-Shake            |  3  |         9          |      27      |
| ImageNet-1K | ResNet-50              |  2  |         9          |      27      |
| ImageNet-1K | EfficientNet-B7        |  2  |         —          |     28†      |

> †The EfficientNet-B7 value was reported on an extended scale beyond 10; cross-verify against the target codebase
> before use. The `torchvision` default of `magnitude=9` on the 31-bin scale corresponds to moderate augmentation and is
> **not** equivalent to the paper's $M_{\text{paper}} = 9$.

#### 4.2 TrivialAugment (TA)

TrivialAugment (Müller & Hutter, 2021) eliminates hyperparameter search entirely by selecting a single operation and
sampling its magnitude uniformly at random from the full discrete range on each forward pass.

**Procedure.** Given a training image $x$:

1. Sample one operation index $k \sim \mathcal{U}\{1, \ldots, K\}$ uniformly from the 14-op RA pool.
1. Sample a magnitude $m \sim \mathcal{U}\{0, 1, \ldots, 30\}$ uniformly from the full 31-bin range.
1. Map $m$ to a physical parameter via the **Standard** bounds in the table above, applying sign randomization where
   applicable.
1. Apply the single operation: $x \leftarrow T_k(x)$.

The proportional translate ceiling is computed dynamically as $(150 / 331) \times W$ (image width), matching the
Standard RA space definition.

**API:** `augment_strategy = "trivial_augment"`. Accepts `translate_const` (float, optional; computed from image width
if not supplied) and `exclude_ops` (list of strings, optional).

#### 4.3 TrivialAugmentWide (TA-Wide)

TrivialAugmentWide (Müller & Hutter, 2021) is the native `torchvision` variant of TrivialAugment. It uses the same
zero-hyperparameter, single-operation protocol as baseline TA, but operates over the **Wide** magnitude bounds,
substantially expanding the geometric and photometric search range.

**Procedure.** Identical to TrivialAugment, with two differences:

1. The **Wide** physical bounds from the table above are applied in place of the Standard bounds.
1. The translate ceiling is a fixed **32 px** (not image-proportional).

Wide bounds summary:

| Transformation                                             | Standard RA / TA              | TA-Wide   |
| :--------------------------------------------------------- | :---------------------------- | :-------- |
| Rotation range                                             | ±30°                          | **±135°** |
| Shear range (X and Y)                                      | ±0.3                          | **±0.99** |
| Enhancement delta (Brightness, Color, Contrast, Sharpness) | ±0.9                          | **±0.99** |
| Posterize (minimum bits retained)                          | 4                             | **2**     |
| Translation (fixed ceiling)                                | $(150/331) \times \text{dim}$ | **32 px** |

**API:** `augment_strategy = "trivial_augment_wide"`. Accepts `exclude_ops` (list of strings, optional). The wide bounds
are fixed by design and cannot be overridden via kwargs; to use custom bounds, call `rand_augment` directly with
`num_layers=1` and the desired parameters.

### 5. Algorithm Comparison

| Property                           | RandAugment                           | TrivialAugment                                 | TrivialAugmentWide                             |
| :--------------------------------- | :------------------------------------ | :--------------------------------------------- | :--------------------------------------------- |
| **Operations applied per image**   | $N$ (tunable, with replacement)       | 1 (fixed)                                      | 1 (fixed)                                      |
| **Magnitude $m$**                  | Fixed global $M$, constant across ops | Sampled: $m \sim \mathcal{U}\{0, \ldots, 30\}$ | Sampled: $m \sim \mathcal{U}\{0, \ldots, 30\}$ |
| **Hyperparameter search required** | Grid search over $(N, M)$             | None                                           | None                                           |
| **Transformation bounds**          | Standard RA space                     | Standard RA space                              | Wide space                                     |
| **Translate ceiling**              | Configurable (default 100 px)         | $(150/331) \times W$ (image-proportional)      | 32 px (fixed)                                  |
| **Reference implementation**       | `torchvision`                         | `automl/trivialaugment`                        | `torchvision`                                  |
| **`justdata` registry key**        | `rand_augment`                        | `trivial_augment`                              | `trivial_augment_wide`                         |

### 6. Non-RA Operations

`Invert`, `Cutout`, `SolarizeAdd`, and `Grayscale` are outside the fixed 14-op pool and cannot be selected through
RandAugment, TrivialAugment, or TrivialAugmentWide. The public `exclude_ops` parameter only removes operations from the
pool; listing these names has no effect. The `cutout_const` argument remains accepted for compatibility and has no
effect. Random erasing is available through the `random_erasing` late-augmentation stage.

______________________________________________________________________

## Supervised Learning — Validation Pipelines

Validation pipelines are strictly deterministic. The objective shifts from regularization to feature preservation and
scale alignment.

### CIFAR-10 / CIFAR-100 Standard Validation

Because CIFAR images are inherently **32×32** and contain minimal background, spatial cropping destroys the primary
subject.

**Pipeline:** `ToImage()` -> `ToDtype(float32, scale=True)` -> `Normalize` (training-set statistics as above).

### ImageNet-1K Standard Baseline (The 0.875 Rule)

For standard supervised models and baseline linear probing, the canonical **0.875** crop ratio discards peripheral
background.

**Pipeline:** `Resize(256, interpolation=Bicubic)` -> `CenterCrop(224)` -> `ToImage()` -> `ToDtype(float32, scale=True)`
-> `Normalize`.

### ImageNet-1K Modern Recipes (FixRes Strategy)

Modern recipes correct train-test resolution discrepancies by manipulating the validation crop percentage (`crop_pct`).

- **A3 (Light) Validation:** Train at **160×160**, validate at **224×224**. Resize shorter edge to **≈236**, then
  `CenterCrop(224)`.
- **A1 / A2 (Heavy/Moderate) Validation:** Default test at **224×224** (`crop_pct=1.0`, so `Resize(224)` ->
  `CenterCrop(224)`). Accuracy improves further via FixRes evaluation at **288×288** (`Resize(288)` ->
  `CenterCrop(288)`) as demonstrated in the RSB paper.

______________________________________________________________________

## The `timm` ImageNet Recipes (ResNet Strikes Back: A1/A2/A3)

The "ResNet Strikes Back" (RSB) recipes dynamically scale augmentation intensity and training schedules to match model
capacity.

> **Crucial context:** These recipes were designed specifically for **ResNet-family architectures**. ViTs typically use
> distinct recipes (e.g., DeiT, BEiT) with different Mixup $\alpha$, optimizers (AdamW), and loss functions. While the
> principle of scaling augmentation with model capacity generalizes, the specific hyperparameters below do not transfer
> directly to ViTs.

| Parameter               | A1 (Heavy)                                           | A2 (Moderate)          | A3 (Light)                                    |
| :---------------------- | :--------------------------------------------------- | :--------------------- | :-------------------------------------------- |
| **Target Architecture** | Large ResNets (e.g., ResNet-152/200) or high compute | ResNet-50 (standard)   | ResNet-50 (fast) or smaller (e.g., ResNet-18) |
| **Training Resolution** | 224                                                  | 224                    | **160** (FixRes strategy)                     |
| **Test Resolution**     | 224 (scales to 288 via FixRes)                       | 224                    | **224**                                       |
| **Epochs**              | **600**                                              | **300**                | **100**                                       |
| **Optimizer**           | LAMB                                                 | LAMB                   | LAMB                                          |
| **LR Schedule**         | Cosine with warmup                                   | Cosine with warmup     | Cosine with warmup                            |
| **Loss Function**       | **BCE** (per-class binary)                           | **BCE**                | **CE** (standard)                             |
| **RandAugment**         | $m \approx 7$, $n = 2$                               | $m \approx 6$, $n = 2$ | $m \approx 6$, $n = 2$                        |
| **Random Erasing**      | `p=0.35`                                             | `p=0.25`               | **Disabled** (`p=0.0`)                        |
| **Repeated Aug (RA)**   | Enabled (3×)                                         | Enabled (3×)           | **Disabled**                                  |
| **Mixup** $\alpha$      | 0.2                                                  | 0.2                    | 0.1                                           |
| **CutMix** $\alpha$     | 1.0                                                  | 1.0                    | 1.0                                           |
| **Stochastic Depth**    | Capacity-dependent (e.g., 0.05+)                     | 0.0                    | 0.0                                           |
| **EMA**                 | Yes                                                  | Yes                    | No (or lighter)                               |
| **Weight Decay**        | 0.02                                                 | 0.02                   | 0.02                                          |

*Note: RandAugment parameter $n$ defaults to 2 per the original specification, though exact magnitude strings fluctuate
across `timm` versions.*

______________________________________________________________________

## Self-Supervised Learning Pipeline (DINOv2)

DINOv2 employs a Teacher-Student knowledge distillation framework operating on multi-crop asymmetry to force the
learning of semantic invariance over low-level frequency matching.

### Multi-Crop Geometric Strategy

- **Global Crops (Context):** 2 crops at **224×224**. Scale: **(0.32, 1.0)**. Passed to both Teacher and Student.
- **Local Crops (Detail):** 8 crops at **96×96** (default). *Use **98×98** ($14 \times 7$) to avoid positional embedding
  interpolation when using ViT-14 backbones.* Scale: **(0.05, 0.32)**. Passed to Student only.

### Asymmetric Pipeline Implementation

The per-crop asymmetry is structurally enforced by three distinct `Compose` pipelines rather than conditional branching
within a single block. Each source image passes through all three pipelines to produce the full 10-crop suite.

- **`global_transform_1`**: Global Crop 1. Enforces strict blurring (`p=1.0`), disables solarization (`p=0.0`).
- **`global_transform_2`**: Global Crop 2. Minimizes blurring (`p=0.1`), enables solarization (`p=0.2`).
- **`local_transform`**: The 8 Local Crops. Moderate blurring (`p=0.5`), disables solarization (`p=0.0`).

### Execution Order and Domain Separation (Per Crop)

| Step | Operation              | Parameters / Per-Crop Asymmetry                                                                                  | Domain             |
| :--- | :--------------------- | :--------------------------------------------------------------------------------------------------------------- | :----------------- |
| 1    | `RandomResizedCrop`    | Aspect ratio **(0.75, 1.33)**, Bicubic interpolation.                                                            | Image              |
| 2    | `RandomHorizontalFlip` | `p=0.5` (all crops)                                                                                              | Image              |
| 3    | `ColorJitter`          | `p=0.8` (all crops). `b=0.4, c=0.4, s=0.2, h=0.1`                                                                | Image              |
| 4    | `RandomGrayscale`      | `p=0.2` (all crops)                                                                                              | Image              |
| 5    | `GaussianBlur`         | $\sigma \sim \mathrm{Uniform}(0.1, 2.0)$, dynamic kernel. Global 1: `p=1.0` · Global 2: `p=0.1` · Local: `p=0.5` | Image              |
| 6    | `Solarization`         | Invert pixels $> 128$. Global 1: `p=0.0` · Global 2: `p=0.2` · Local: `p=0.0`                                    | Image              |
| 7    | Convert & Scale        | `ToImage()` -> `ToDtype(float32, scale=True)`                                                                    | Image -> Tensor    |
| 8    | `Normalize`            | ImageNet `mean` and `std`.                                                                                       | Tensor (zero-mean) |

### SSL Downstream Evaluation

**Image-Level Classification (Linear Probing / $k$-NN):** Standard supervised inference transforms:
`Resize(256, Bicubic)` -> `CenterCrop(224)` -> `ToImage()` -> `ToDtype(float32)` -> `Normalize`. Features are extracted
from the `[CLS]` token or a concatenation of `[CLS]` and average-pooled patch tokens.

**Dense Tasks (Segmentation / Depth Validation — Patch Alignment):** To avoid dropping boundary pixels or forcing
complex interpolation during dense evaluation, images are resized to the target scale and padded (reflection or zero) on
the bottom and right edges such that both height and width are exact multiples of the ViT patch size (e.g., 14). This is
implemented in `justdata.vision.transforms.pad_to_patch_multiple`.

For semantic segmentation with replayable geometry, select `vision/segmentation` with `apply_presets=False` and explicit
`geometry_kwargs`. It supports paired aspect-preserving training resize and crops, longer-side evaluation caps,
independent RGB/ignore padding, per-pixel validity, and original-coordinate score restoration. See
[the dense geometry contract](docs/vision.md#12-replayable-dense-segmentation) for configuration, record fields, replay,
and batching requirements.

For instance-aware labels, select `vision/panoptic_segmentation` with a segment-ID map and category table. It preserves
thing instances, merges stuff by category, and excludes crowd pixels from panoptic supervision. The
[panoptic contract](docs/vision.md#13-panoptic-segmentation) and
[LaRS example](docs/vision.md#14-loading-local-lars-archives) show how to load either semantic or panoptic labels from
local LaRS v1.0.0 archives and plot five images under the A1/A2/A3 presets with both label views.

______________________________________________________________________

## Registry System

All extensible components in `justdata` use a decorator-based registry pattern with thread-safe lookups.

| Registry           | Decorator                                         | Lookup                                                         |
| :----------------- | :------------------------------------------------ | :------------------------------------------------------------- |
| Crop strategies    | `@register_crop_strategy(name)`                   | `justdata.vision.augmentations.get_crop_strategy(name)`        |
| Augment strategies | `@register_augment_strategy(name)`                | `justdata.vision.augmentations.get_augment_strategy(name)`     |
| Corruptions        | `@register_corruption(name, descriptor=...)`      | `justdata.vision.corruptions.apply_corruption(...)`            |
| Dataset adapters   | `@register_adapter(dataset_name)`                 | `justdata.core.get_adapter(dataset_name)`                      |
| Source loaders     | `@register_source_loader(prefix)`                 | `justdata.core.get_source_loader(dataset_name)`                |
| Pipelines          | `@register_pipeline("modality/task")`             | `justdata.core.get_pipeline(...)`                              |
| Dataset metadata   | `register_dataset(name, task_type, modality=...)` | `justdata.core.get_dataset_info(name)`                         |
| Audio frontends    | `@register_audio_frontend(name)`                  | `justdata.acoustic.get_audio_frontend(name)`                   |
| Audio components   | `@register_audio_*` decorators                    | `justdata.acoustic.get_audio_*`, `list_audio_*`, `has_audio_*` |

`get_pipeline` is the high-level resolver: it infers the task type from the dataset name, merges preset defaults with
user-supplied kwargs (via smart merge; see below), and invokes the appropriate pipeline factory.

Custom pipelines may register `@register_pipeline(name, config_resolver=resolver)` when they support strict overrides
and executed-configuration export. The resolver receives a defensive copy of the merged configuration plus `is_training`
and returns the resolved four-stage configuration and model-input contract. Legacy factories without a resolver remain
buildable, but strict resolution and `return_config=True` reject them because their behavior cannot be described
completely.

**Built-in crop strategies:** `random_resized`, `random_resized_hvflip`, `random_pad`, `random_hflip`,
`resize_random_hflip`, `random_rot90_hflip`.

**Built-in augment strategies:** `rand_augment`, `trivial_augment`, `trivial_augment_wide`, `color_jitter`, `none`.

Crop-specific parameters are passed through `aug_kwargs["crop_kwargs"]`. `size`, `seed`, `interpolation`, `padding`, and
`pad_mode` remain controlled by the existing top-level augmentation fields and are rejected inside `crop_kwargs`,
avoiding ambiguous precedence. For example, the FMoW M1 crop is fully declarative:

```python
pipeline = get_pipeline(
    dataset="wilds:fmow",
    aug_kwargs={
        "enable": True,
        "image_size": 224,
        "crop_type": "random_resized_hvflip",
        "interpolation": "bicubic",
        "crop_kwargs": {
            "scale": (0.85, 1.0),
            "ratio": (0.90, 1.10),
            "horizontal_flip_probability": 0.5,
            "vertical_flip_probability": 0.5,
        },
    },
)
```

The acoustic package registers audio decoders, resamplers, channel strategies, segment strategies, frontends,
augmentations, normalizations, eval views, postprocessors, and corruptions. Use `list_audio_*` helpers to inspect
registered acoustic components.

______________________________________________________________________

## Presets and Smart Merging

`justdata.vision.presets` stores dataset-specific default kwargs for all four vision pipeline stages. The vision
`_default` preset (ImageNet statistics, 224px, RandAugment) serves as the fallback for vision only.

**Available presets:**

| Preset            | Description                                                     |
| :---------------- | :-------------------------------------------------------------- |
| `_default`        | ImageNet-1K statistics, 224px, RandAugment, modern branch       |
| `cifar`           | 32px, TrivialAugmentWide, CIFAR-10 normalization, no resizing   |
| `cifar100`        | 32px, TrivialAugmentWide, CIFAR-100 normalization               |
| `imagenet_resnet` | ImageNet statistics, legacy branch with ColorJitter             |
| `imagenet_a1`     | RSB A1: heavy augmentation, BCE loss, 600 epochs                |
| `imagenet_a2`     | RSB A2: moderate augmentation, BCE loss, 300 epochs             |
| `imagenet_a3`     | RSB A3: light augmentation, CE loss, 160px training             |
| `dinov2`          | Asymmetric multi-crop SSL pipeline                              |
| `wilds:*`         | WILDS benchmark defaults; matching `_strong` presets are opt-in |

`merge_with_presets(dataset, user_kwargs)` implements a **smart merge**: user-supplied kwargs that are identical to the
`_default` preset values are treated as "not explicitly overridden," allowing dataset-specific preset values to take
precedence. Only kwargs that genuinely differ from the defaults are considered intentional user overrides.

For an authoritative recipe, pass `overrides=` to `get_pipeline`. Explicit values win even when they are `False`, zero,
`None` (where supported), or equal to the modality default. Nested mappings preserve siblings from the selected preset:

```python
pipeline = get_pipeline(
    dataset="cifar10",
    overrides={
        "aug_kwargs": {"enable": False, "image_size": 224},
        "laug_kwargs": {"enable": False},
        "postproc_kwargs": {
            "image_size": 224,
            "val_resize_size": None,
            "normalization_params": (
                (0.485, 0.456, 0.406),
                (0.229, 0.224, 0.225),
            ),
        },
    },
)
```

Supplying `overrides={}` selects strict resolution without changing values. Built-in resolvers reject unknown nested
keys, missing required settings, contradictory configurations, and attempts to set runtime-owned fields before a loader
opens its source. Existing keyword arguments retain smart-merge compatibility semantics.

Resolved presets are serializable and hashable across modalities. Use `get_resolved_preset(name)` from
`justdata.vision.presets`, `justdata.acoustic.presets`, or `justdata.core.presets` to obtain an object with `.to_json()`
and `.hash()`. Hashes use canonical JSON with sorted keys and a 16-character SHA-256 prefix.

Acoustic presets are typed with `AudioPreset` and nested frozen config dataclasses for preprocessing, segmentation,
frontends, labels, normalization, train augment settings, eval views, layout, and metadata policy. The `justdata.audio`
namespace is a compatibility alias for `justdata.acoustic`.

See [docs/presets.md](docs/presets.md) for acoustic preset contracts, including EfficientAT/DyMN, PaSST, and CED.

### Executed configuration snapshots

Pass `return_config=True` to `load_ds` or `load_inventory` to receive `(dataset, n_batches, config)`. The immutable
`ExecutedConfig` records the selected pipeline and preset, resolved four-stage configuration, derived model-input
contract, train/evaluation mode, randomness, shuffle, batching, metadata, caches, execution limits, prefetching, and
NumPy conversion. Its canonical versioned encoding is available as `config.to_bytes()`:

```python
import hashlib
from pathlib import Path

dataset, n_batches, config = load_ds(
    dataset_names_arg="cifar10",
    splits_arg="test",
    dataset_type="validation",
    batch_size=128,
    seed=42,
    pipeline=pipeline,
    num_classes=10,
    return_config=True,
)
encoded = config.to_bytes()
Path("executed-justdata-config.json").write_bytes(encoded)
full_digest = hashlib.sha256(encoded).hexdigest()
```

For a JD-01 offline inventory, use the same return option after verified admission:

```python
import hashlib
from pathlib import Path

from justdata.core import load_inventory, open_inventory

admitted = open_inventory("/data/lars-admitted")
dataset, n_batches, config = load_inventory(
    admitted,
    dataset_type="validation",
    batch_size=8,
    seed=42,
    pipeline=pipeline,
    return_config=True,
)
encoded = config.to_bytes()
Path("lars-justdata-execution.json").write_bytes(encoded)
full_digest = hashlib.sha256(encoded).hexdigest()
```

With `return_raw_ds=True`, the loader returns `(prepared, tools, config)` and marks the remaining stages as pending.
Both `tools["finalize_fn"]` and `tools["finalize_epoch"]` accept `return_config=True` and append a snapshot of their
actual finalization settings. Snapshot export rejects opaque callback replacements because their behavior cannot be
represented by configuration data.

The snapshot describes JustData's configured execution. A consuming product must separately bind admitted source
inventory identities, external transformations, exact library revisions, model/checkpoint identities, and other run
artifacts.

### Numeric identity and metadata sidecars

With `metadata_mode="numeric_only"`, pass `metadata_sidecar=` for a complete source mapping prepared before iteration.
For an admitted inventory, use `MetadataSidecar.from_inventory(admitted)`; for another finite source, use
`MetadataSidecar.from_metadata(source_records)`. Each real batch row then has `metadata.row_id`, an `int64` join key,
and `metadata.row_fingerprint`, a numeric check against stale cache entries. Resolve keys through `sidecar.records` only
where `padding_mask` is true. The sidecar preserves original string IDs and static source metadata while numeric
geometry remains with each view.

For a view with additional string metadata, use the numeric `metadata.view_id` with `row_id` to read
`sidecar.view_records[(row_id, view_id)]`.

`sidecar.write_jsonl(path, policy="create")` saves a complete mapping and `MetadataSidecar.read_jsonl(path)` reopens it.
The streaming `sidecar_metadata_path=` route resumes and validates an existing file by default;
`sidecar_metadata_policy="create"` rejects reuse and `"overwrite"` explicitly replaces it. Streaming artifacts can be
partial after interrupted iteration. Executed configuration records the sidecar policy or full mapping digest.

### Automatic preset resolution

When a vision dataset has a registered preset (e.g., `cifar10` -> `cifar`, `cifar100` -> `cifar100`), `get_pipeline`
automatically applies it. Vision datasets without a dedicated preset fall back to the vision `_default`.

```python
import justdata.vision
from justdata.core.registry import get_pipeline

# cifar10 automatically gets the 'cifar' preset (32px, TrivialAugmentWide, CIFAR-10 stats)
pipeline = get_pipeline(dataset="cifar10")
```

### Applying a named preset to any dataset

The `dataset` argument in `get_pipeline` drives preset lookup, not just task inference. To apply a specific named preset
to a dataset that does not have its own preset (e.g., using `imagenet_a3` for `imagenette`), pass it as `preset`:

```python
import justdata.vision
from justdata.core.loader import load_ds
from justdata.core.registry import get_pipeline

# Resolve the A3 (RSB light) pipeline for imagenette
pipeline = get_pipeline(
    dataset="imagenette",
    preset="imagenet_a3",       # drives preset lookup: 160px train, 224px val, RandAugment m=6
)

train_ds, N = load_ds(
    dataset_names_arg=["imagenette"],  # actual dataset to load
    splits_arg={"imagenette": ["train"]},
    dataset_type="train",
    batch_size=128,
    seed=42,
    pipeline=pipeline,
    num_classes=10,
    cache_dataset=False,
)
```

The validation pipeline uses the same preset name; the A3 preset's FixRes strategy (`train_image_size=160`,
`val_resize_size=236`) is applied automatically:

```python
# The same pipeline can be used for validation; `load_ds` automatically sets `is_training=False` based on `dataset_type`
# and applies the deterministic center-crop resize (FixRes 236 → CenterCrop 224)
val_ds, N = load_ds(
    dataset_names_arg=["imagenette"],
    splits_arg={"imagenette": ["validation"]},
    dataset_type="validation",
    batch_size=256,
    seed=0,
    pipeline=pipeline,
    num_classes=10,
    cache_dataset=False,
)
```

______________________________________________________________________

## Acoustic Pipelines

Import `justdata.acoustic` before resolving built-in acoustic datasets or pipelines. Acoustic samples use the canonical
keys `waveform`, `sample_rate`, optional `label`, `features`, `duration`, and `metadata`.

```python
import justdata.acoustic
from justdata.core.loader import load_ds
from justdata.core.registry import get_pipeline

pipeline = get_pipeline(
    dataset="dcase2025_task1",
    preset="dcase2025_task1_efficientat_32k_1s",
)

ds, n = load_ds(
    dataset_names_arg=["dcase2025:task1"],
    splits_arg={"dcase2025:task1": ["dev_train_25"]},
    dataset_type="train",
    batch_size=64,
    seed=0,
    pipeline=pipeline,
    num_classes=10,
    cache_dataset=False,
    data_dir="/path/to/dcase",
    metadata_mode="numeric_only",
    as_numpy=True,
)
```

Modality documentation:

- [docs/vision.md](docs/vision.md): vision schema, four stages, preset contracts, metadata modes, Mini-C, and parity
  matrix.
- [docs/acoustic.md](docs/acoustic.md): canonical schema, four stages, metadata modes, golden tests, corruption
  benchmark, and parity matrix.
- [docs/dcase2025.md](docs/dcase2025.md): DCASE Task 1 source/target helpers and split safety.
- [docs/presets.md](docs/presets.md): vision and acoustic preset selection and hashable contracts.
- [docs/golden_tests.md](docs/golden_tests.md): optional golden compatibility test workflow.

Examples are split by modality under `examples/vision/` and `examples/acoustic/`. Each directory includes loading,
Hugging Face source, statistics, and corruption benchmark examples.

______________________________________________________________________

## Dataset Adapters

Core adapters normalize raw records into the modality-specific schema expected by a selected pipeline. `justdata.core`
itself is schema-neutral, and the identity adapter is applied when no dataset-specific adapter is registered.

### Vision adapter

Vision pipelines expect `image`, optional `label`, and task-specific fields such as `mask`.

```python
from justdata.core.adapters import register_adapter

@register_adapter("my_dataset")
def my_adapter(sample):
    return {"image": sample["img"], "label": sample["class_id"]}
```

### Acoustic adapter

Acoustic pipelines expect `waveform`, `sample_rate`, and an optional `label`.

```python
from justdata.core.adapters import register_adapter

@register_adapter("my_audio_dataset")
def my_audio_adapter(sample):
    result = {
        "waveform": sample["audio"],
        "sample_rate": sample["sampling_rate"],
    }
    if "target" in sample:
        result["label"] = sample["target"]
    return result
```

______________________________________________________________________

## Mini-C Corruption Benchmark

`create_minic_datasets` constructs finalized corruption evaluation datasets by forking from a preprocessed RGB dataset.
Corruption runs after decoding and rank/channel normalization, but before resize, float conversion, normalization,
batching, and padding. Pass `cache_dataset=True` with an explicit disk path when avoiding source re-reads across
corruptions is worth the storage cost; otherwise the base remains streaming.

The original Mini-C family names remain pinned to their `1.0.0` behavior:

| Name      | Version 1 implementation                                              |
| :-------- | :-------------------------------------------------------------------- |
| `blur`    | Defocus blur with a disk kernel and alias Gaussian filter             |
| `noise`   | Additive Gaussian noise using the legacy absolute standard deviations |
| `digital` | Nearest-neighbor pixelation                                           |
| `weather` | Synthetic snow                                                        |

Five exact-name TensorFlow-native operators are also available at version `1.0.0`: `gaussian_blur`, `gaussian_noise`,
`jpeg_compression`, `contrast_reduction`, and `brightness_reduction`. Their immutable descriptors contain the severity
table, domains, dtype/shape contract, seed policy, clipping and rounding policy, backend identity, and codec/filter
settings. Use `get_corruption_descriptor(name, version)` or `list_corruption_descriptors()` to serialize and fingerprint
these semantics.

For a caller-owned scientific seed, use exact version dispatch directly:

```python
import tensorflow as tf

from justdata.vision.corruptions import (
    apply_corruption,
    get_corruption_descriptor,
)

descriptor = get_corruption_descriptor("gaussian_noise", "1.0.0")
corrupted = apply_corruption(
    decoded_rgb_uint8,
    name=descriptor.name,
    version=descriptor.version,
    severity=3,
    seed=tf.constant([123, 456], dtype=tf.int32),
)
```

The complete seed is supplied by the caller; no global RNG is read. `gaussian_noise@1.0.0` uses TensorFlow Philox. The
JPEG descriptor fixes encode and decode options and is repeatable for a fixed TensorFlow/codec build, but does not claim
byte identity across unrelated TensorFlow or codec versions.

```python
import justdata.vision
from justdata.core.registry import get_pipeline
from justdata.vision.minic import create_minic_datasets

pipeline = get_pipeline(dataset="imagenet")

datasets, n = create_minic_datasets(
    corruption_types=["gaussian_noise", "jpeg_compression", "blur"],
    severity=3,
    dataset_names_arg=["imagenet2012"],
    splits_arg={"imagenet2012": ["validation"]},
    dataset_type="validation",
    batch_size=256,
    seed=0,
    pipeline=pipeline,
    num_classes=1000,
)
```

With `metadata_mode="full"`, finalized samples include `corruption`, `corruption_version`, `corruption_identity`,
`corruption_identity_hash`, `severity`, and `corruption_domain`. `numeric_only` retains the numeric identity hash and
severity; `none` removes metadata according to the shared core contract. The dataset helper preserves legacy
enumeration-position seed salting. Consumers with their own sample-level PRNG lineage should call `apply_corruption`
directly.

______________________________________________________________________

## Audio Corruption Benchmark

`create_audio_corruption_datasets` mirrors Mini-C for acoustic evaluation. It applies deterministic severity 1-5
corruptions in the waveform or spectrogram domain, attaches corruption metadata, runs the normal postprocessing stage,
and preserves `padding_mask`.

```python
import justdata.acoustic
from justdata.acoustic.corruptions.datasets import create_audio_corruption_datasets
from justdata.core.registry import get_pipeline

pipeline = get_pipeline(
    dataset="dcase2025_task1",
    preset="dcase2025_task1_efficientat_32k_1s",
)

datasets, n = create_audio_corruption_datasets(
    corruption_types=["additive_white_noise", "clipping"],
    severity=3,
    base_dataset="dcase2025:task1",
    preset="dcase2025_task1_efficientat_32k_1s",
    split="dev_test",
    pipeline=pipeline,
    batch_size=64,
    seed=0,
    num_classes=10,
    metadata_mode="numeric_only",
)
```

______________________________________________________________________

## Usage

### Basic Classification Pipeline

```python
import justdata.vision
from justdata.core.loader import load_ds
from justdata.core.registry import get_pipeline

pipeline = get_pipeline(dataset="cifar10")

train_ds, N = load_ds(
    dataset_names_arg=["cifar10"],
    splits_arg={"cifar10": ["train"]},
    dataset_type="train",
    batch_size=128,
    seed=42,
    pipeline=pipeline,
    num_classes=10,
    # CIFAR-10 is small enough for an intentional memory cache.
    cache_dataset=True,
)
```

### Reusable, Explicitly Seeded Epochs

Use `return_raw_ds=True` to prepare the source, pipeline, preprocessing, cache, and filters once, then materialize the
complete remaining training pipeline for each epoch:

```python
prepared_train, tools = load_ds(
    dataset_names_arg=["cifar10"],
    splits_arg={"cifar10": ["train"]},
    dataset_type="train",
    batch_size=128,
    seed=0,
    pipeline=pipeline,
    num_classes=10,
    cache_dataset=False,
    deterministic=True,
    return_raw_ds=True,
)

for epoch in range(num_epochs):
    train_iterator, n_batches = tools["finalize_epoch"](
        prepared_train,
        seed=derive_epoch_seed(epoch),
        as_numpy=True,
    )
    train_one_epoch(train_iterator)
```

`finalize_epoch` applies standard augmentation, shuffle, postprocessing, batching, late augmentation, padding, and
prefetching. The epoch seed is split into standard- and late-augmentation seeds, with source and batch indices folded
in. Shuffle uses the epoch seed with reshuffling disabled on the materialized dataset. With `deterministic=True`,
repeating a seed therefore reproduces the epoch independently of previous iterator creation or consumption, including
when the returned TensorFlow dataset is iterated more than once.

For an augmentation callback that depends only on its input sample and supplied seed, pass `augment_is_stateless=True`
to `finalize_epoch` to run standard augmentation concurrently using `map_parallel_calls` (or `AUTOTUNE`). With
`deterministic=True`, sample order and indexed RNG seeds remain unchanged:

```python
train_iterator, n_batches = tools["finalize_epoch"](
    prepared_train,
    seed=derive_epoch_seed(epoch),
    augment_is_stateless=True,
    as_numpy=True,
)
```

This flag is the caller's assertion that the augmentation callback uses no stateful RNG, mutable state, or side effects.
It defaults to `False`, preserving serial standard augmentation for deterministic epochs. It does not change the
one-shot `load_ds` or `finalize_fn` seed scheduling.

With `cache_dataset=False`, source samples, preprocessing, and filters still run on every iteration; only loader and
graph preparation are reused. A fresh NumPy iterator is created by each `finalize_epoch(..., as_numpy=True)` call.

`finalize_epoch` rejects `cache_model_inputs=True` when training or evaluation augmentation is enabled because that
downstream cache intentionally freezes the first sampled augmented views. Existing one-shot `load_ds` and `finalize_fn`
behavior is unchanged.

For a verified local inventory, `load_replay_epoch` binds an immutable snapshot and executed configuration to an
explicit committed-batch cursor. It rebuilds the full epoch before skipping committed batches. See
[`docs/inventory.md`](docs/inventory.md#deterministic-epoch-replay) for checkpoint and resume usage. Count real rows
with `count_real_examples(batch)` or the returned epoch's `remaining_examples`; padded rows have a false `padding_mask`.
The epoch path also accepts `prefetch=False` when finalization must precede a caller-controlled prefetch stage.

### Bounded input execution and protected caches

Pass `prefetch=1` (or another positive batch count) to `load_ds`, `load_inventory`, or `finalize_fn` to bound final
prefetch. `finalize_epoch` inherits the configured value unless overridden. `False` disables prefetch; `True` retains
automatic tuning. A bounded input profile also sets positive integer `map_parallel_calls`, `private_threadpool_size`,
and `max_intra_op_parallelism`, plus a finite shuffle buffer. The executed configuration records the effective values.
Replay prefetches after skipping committed batches and defaults to one batch.

When another framework uses the accelerator, configure TensorFlow before creating tensors or datasets:

```python
from justdata.core import configure_tensorflow_cpu

device_report = configure_tensorflow_cpu(intra_op_threads=1, inter_op_threads=1)
assert device_report["logical_gpus"] == []
```

The report includes physical and logical device names and process thread settings. The helper raises if the runtime has
initialized too early for the requested change. Core, vision, acoustic, and audio imports permit this setup.

`CachePolicy` enables identity-checked file caches on the existing preprocessing and model-input cache stages. The cache
path is a directory whose parent exists. The input identity must cover ordered content. `load_inventory` binds its
verified manifest automatically; other sources require a content-derived `input_identity`. Callers must declare
deterministic callbacks because arbitrary callbacks cannot be inspected for stateful random operations.

```python
from justdata.core import CachePolicy, inspect_cache, load_inventory

policy = CachePolicy(
    max_bytes=1_073_741_824,
    max_examples=256,
    materialization="lazy",  # choose "eager" to finish before returning
    callbacks_are_deterministic=True,
)
batches, count, config = load_inventory(
    admitted, "validation", 2, 17,
    pipeline=pipeline,
    cache_dataset=True,
    cache_path="/existing/cache-parent/prepared",
    cache_policy=policy,
    return_config=True,
    map_parallel_calls=2,
    private_threadpool_size=2,
    max_intra_op_parallelism=1,
    prefetch=1,
)
for batch in batches:
    consume(batch)
assert policy.status("preprocess")["state"] == "complete"
assert inspect_cache("/existing/cache-parent/prepared")["state"] == "complete"
```

Lazy caches publish completion only after the source is fully exhausted. Eager caches finish before loading returns.
Both modes record count, byte size, checksum, and a versioned stage/configuration identity. Changed input,
normalization, geometry, or augmentation configuration rejects reuse at the same path. An incomplete or incompatible
cache raises `CacheError`; choose a new path after inspecting or clearing the caller-owned artifact. Cache errors expose
`code` and `path`. During lazy iteration TensorFlow may wrap the exception; `policy.status(stage)` retains its
structured failure code. Quotas bound record count and serialized record bytes. Protected model-input caches require
evaluation without active augmentation; preprocessing caches remain before per-epoch augmentation. Existing cache flags
without a policy retain their behavior.

### Loading a Hugging Face Dataset

```python
train_ds, n_batches = load_ds(
    dataset_names_arg=["hf:cifar10"],
    splits_arg={"hf:cifar10": ["train"]},
    cache_dataset=False,
    ...
)
```

`load_ds` returns the dataset and its batch cardinality. The cardinality is a Python integer when TensorFlow can
determine it and `None` for unknown or infinite pipelines.

For remote/downloaded sources, `data_dir` is a cache root. justdata namespaces source-owned caches under it: `tfds/`,
`hf/vision/`, `hf/acoustic/`, `wilds/`, and `zenodo/`. If omitted, the root is `~/.cache/justdata`.

Loader caches are separate from source-owned caches. `cache_dataset/cache_path` caches decoded, preprocessed samples
before augmentation. Loader caching is off by default, so datasets remain streaming when `cache_dataset` is omitted. Set
`cache_dataset=True` with an empty `cache_path` to opt into memory caching, or provide a nonempty path to cache on the
filesystem. Large datasets should use an explicit disk path or remain uncached. `cache_model_inputs` and
`model_input_cache_path` cache resized, normalized model inputs before batching. Use different paths for the two cache
stages. This default changes performance, not output values.

### Loading a WILDS Dataset

WILDS image classification datasets use the `wilds:` source prefix. Source options such as FMoW temporal split schemes
live in the dataset string; when using a `splits_arg` dictionary, reuse that exact string as the key. Downloads are
disabled by default (`download=false`). To download through WILDS under `data_dir/wilds`, add `download=true` to the
dataset string. Use `&` between query options, not a second `?`. The automatic presets are benchmark-faithful:
Camelyon17 96 px, FMoW 224 px, iWildCam 448 px, and RxRx1 256 px with per-image channel standardization. Use
`preset="wilds:fmow_strong"` or another `_strong` name to opt into stronger training augmentation.

```python
local_ssd = "/local_ssd/justdata"
wilds_ds = "wilds:fmow?split_scheme=time_after_2016&download=true"
pipeline = get_pipeline(dataset="wilds:fmow")

train_ds, N = load_ds(
    dataset_names_arg=[wilds_ds],
    splits_arg={wilds_ds: ["train"]},
    dataset_type="train",
    batch_size=32,
    seed=42,
    pipeline=pipeline,
    num_classes=62,
    data_dir=f"{local_ssd}/sources",
    cache_dataset=True,
    cache_path=f"{local_ssd}/decoded/fmow-train",
    cache_model_inputs=True,
    model_input_cache_path=f"{local_ssd}/model-inputs/fmow-train-224",
    allow_train_model_input_cache=True,
    metadata_mode="numeric_only",
)
```

FMoW source inventories can opt into authoritative sequence identity and acquisition time without changing the default
WILDS signature:

```python
from justdata.core import fetch_ds

fmow = (
    "wilds:fmow?split_scheme=official&version=1.1&download=false"
    "&source_metadata=location_id,timestamp"
)
inventory = fetch_ds([fmow], {fmow: ["train", "id_val", "id_test", "val"]})

for sample in inventory.as_numpy_iterator():
    source = sample["metadata"]["wilds_source"]
    location_id = source["location_id"]  # UTF-8 bytes
    timestamp = source["timestamp"]      # UTF-8 bytes
```

The option accepts only `location_id` and `timestamp`. JustData maps every emitted `wilds_index` through WILDS'
authoritative `full_idxs` mapping: `location_id` is the exact original FMoW sequence-directory basename, and `timestamp`
preserves the source timezone-aware ISO-8601 text. Invalid fields, target-equivalent fields, missing source columns, or
unreliable mappings fail closed. The `wilds_source` mapping is absent without the opt-in; it is retained by
`metadata_mode="full"` and removed by `numeric_only`.

The base `wilds:fmow` preset has no stochastic per-sample training augmentation, so train model-input caching is safe
when explicitly opted in. Do not use train model-input caching with `wilds:fmow_strong` unless freezing the first pass
of stochastic augmentation is intentional.

### Applying a Named Preset

To use a specific preset for any dataset, pass the preset name as `preset`:

```python
# Load imagenette with the RSB A3 (light) recipe: 160px training, RandAugment m=6, Mixup α=0.1
pipeline = get_pipeline(
    dataset="imagenette",
    preset="imagenet_a3",  # apply the A3 preset to the imagenette task
)

train_ds, N = load_ds(
    dataset_names_arg=["imagenette"],
    splits_arg={"imagenette": ["train"]},
    dataset_type="train",
    batch_size=128,
    seed=42,
    pipeline=pipeline,
    num_classes=10,
    cache_dataset=False,
)
```

### Overriding Preset Parameters

```python
pipeline = get_pipeline(
    dataset="imagenet",
    aug_kwargs={"augment_type": "trivial_augment"},  # overrides preset default
)
```

### DINOv2 SSL Pipeline

```python
pipeline = get_pipeline(
    preset="dinov2",
    pipeline_name="vision/classification",
)
```

______________________________________________________________________

## Development

This project uses [devenv](https://devenv.sh/) (Nix-based) with `uv` for Python dependency management.

```bash
devenv shell        # enter the development environment
direnv allow        # approve automatic devenv activation once
uv sync             # install/update dependencies
uv run pytest       # run all tests
uv run ruff check . # run lint checks
uv run ruff format --check .  # check formatting
uv run ty check src/          # type check the package
uv run mdformat --check --wrap 120 ./*.md benchmarks docs examples src tests  # check Markdown formatting
uv run pytest tests/path/to/test_file.py::test_name  # run a single test
```

The project targets Python 3.12 (see `.python-version`). `LD_LIBRARY_PATH` is configured by devenv for native libraries.

See [Performance checks](benchmarks/README.md) for reproducible CPU benchmarks and the performance audit results.

GitLab runs `sh tests/doctor/test_lint.sh` in a pinned Debian-based uv image. It checks Python with Ruff and Markdown
with mdformat using the GFM, footnote, and MyST plugins from the `lint` dependency group alone, then type checks `src/`
with ty against the locked development environment so that third-party imports resolve. TensorFlow attaches `Tensor`
operators at runtime, so ty treats `tensorflow` imports as `Any` (see `[tool.ty.analysis]` in `pyproject.toml`). The
image provides the standard Linux dynamic loader required by the Ruff and ty PyPI executables, which the Nix image
lacks.

### Releases

GitLab builds a wheel and source distribution after lint, the default test suite, and all Python 3.11–3.13
package-support jobs pass. Build artifacts are retained for one week. Tag pipelines publish those artifacts to
[PyPI](https://pypi.org/project/justdata/) after the existing manual `golden` job also passes; start that job in GitLab
to complete a release.

Before tagging, update `pyproject.toml`, `src/justdata/__init__.py`, and `CHANGELOG.md`, then run `uv lock`. Push a tag
matching the package version, with an optional `v` prefix (for example, `v1.2.1` or `1.2.1`). Publishing rejects tags
that disagree with either version declaration. Branch pipelines build artifacts without publishing.

Configure a [PyPI GitLab trusted publisher](https://docs.pypi.org/trusted-publishers/adding-a-publisher/) for the
`justdata` project with namespace `rhizome-labs/public`, project `justdata`, pipeline path `.gitlab-ci.yml`, and
environment `release`. The `publish` job requests a GitLab ID token with audience `pypi` and requires trusted
publishing; no stored PyPI API token is needed.
