Dataset Classes

This page is the API reference for all dataset classes in pytorch_segmentation_models_trainer.

Most classes live in:

from pytorch_segmentation_models_trainer.dataset_loader.dataset import <ClassName>

RasterPatchDataset lives in its own module:

from pytorch_segmentation_models_trainer.dataset_loader.raster_patch_dataset import RasterPatchDataset

Class Hierarchy

torch.utils.data.Dataset
├── RasterPatchDataset          ← sliding-window, folder-based (no CSV)
└── AbstractDataset             ← CSV / DataFrame-based
    ├── ImageDataset
    │   └── TiledInferenceImageDataset
    ├── SegmentationDataset
    │   ├── SegmentationDatasetFromFolder
    │   └── FrameFieldSegmentationDataset
    ├── RandomCropSegmentationDataset
    ├── ObjectDetectionDataset
    │   └── InstanceSegmentationDataset
    └── PolygonRNNDataset

`RasterPatchDataset`

from pytorch_segmentation_models_trainer.dataset_loader.raster_patch_dataset import RasterPatchDataset

Systematic sliding-window dataset for semantic segmentation directly over full-size raster files. Discovers image/mask pairs by recursive folder scan, computes all valid patch_size × patch_size windows for each image, and maps a global 1-D index to the correct (image, row, column) in O(log N) via binary search. Only the requested window pixels are read from disk at __getitem__ time — full images are never loaded into memory.

See the Sliding-Window Patch Dataset user guide for usage examples and the full YAML reference.

Constructor Parameters

Parameter	Type	Default	Description
`image_dir`	`str \| Path`	required	Root directory of input images. Scanned recursively.
`mask_dir`	`str \| Path`	required	Root directory of segmentation masks. Matching is by relative path.
`extension`	`str`	`".tif"`	Image file extension. Leading dot is optional and normalized internally.
`patch_size`	`int`	`256`	Side length of each square patch in pixels.
`stride`	`int`	`128`	Step between patch origins. `stride < patch_size` produces overlapping patches.
`mask_extension`	`str \| None`	`None`	Mask file extension. When `None`, uses the same value as `extension`.
`augmentation_list`	`list \| A.Compose \| None`	`None`	Albumentations augmentation pipeline. Image is passed as `(H, W, C)`, mask as `(H, W)`.
`data_loader`	`Any \| None`	`None`	DataLoader sub-configuration. Stored as `ds.data_loader`; consumed by the Lightning `Model`.
`selected_bands`	`List[int] \| None`	`None`	1-based rasterio band indices to load. `None` loads all bands.
`image_dtype`	`str`	`"uint8"`	Array dtype after reading. Accepted: `"uint8"`, `"uint16"`, `"float32"`, `"native"`.

Raises

Exception	When
`ValueError`	`image_dtype` is not one of the accepted values.
`ValueError`	`selected_bands` contains a non-positive integer.
`ValueError`	No valid image/mask pairs are found after scanning both directories.
`UserWarning`	An image is smaller than `patch_size` (skipped silently).
`UserWarning`	A mask file is missing for a discovered image (skipped silently).

Key Attributes

Attribute	Type	Description
`image_info`	`List[Dict]`	Per-image metadata: `img_path`, `mask_path`, `height`, `width`, `patches_per_row`, `patches_per_col`.
`patch_size`	`int`	Patch side length.
`stride`	`int`	Step between patch origins.
`image_dtype`	`str`	Configured dtype.
`selected_bands`	`List[int] \| None`	Band selection (1-based).
`data_loader`	`Any`	Stored DataLoader config.

`len`

Returns the total number of patches across all images, not the number of images.

len(ds) = Σ_i  patches_per_row_i × patches_per_col_i

`getitem` Returns

Dict[str, torch.Tensor] with keys:

Key	dtype	shape	Notes
`"image"`	`torch.float32`	`(C, patch_size, patch_size)`	Normalised ÷255 (`uint8`) or ÷65535 (`uint16`) when no transform; unchanged for `float32`/`native`.
`"mask"`	`torch.int64`	`(patch_size, patch_size)`	Raw pixel values from the mask file.

When augmentation_list is set the return type matches whatever the Albumentations pipeline produces (typically the same keys with transformed tensors after ToTensorV2).

`AbstractDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import AbstractDataset

Base class for all datasets. Handles CSV loading, root directory resolution, and augmentation setup. Subclasses must implement __getitem__.

Constructor Parameters

Parameter	Type	Default	Description
`input_csv_path`	`Path`	`None`	Path to the CSV file. Required when `df` is not provided.
`df`	`pd.DataFrame`	`None`	Pre-loaded DataFrame. When provided, `input_csv_path` is ignored for loading but still stored.
`root_dir`	`Any`	`None`	Root directory prepended to all relative file paths read from the CSV.
`augmentation_list`	`Any`	`None`	Albumentations augmentation list or `A.Compose` object. `None` disables augmentation.
`data_loader`	`Any`	`None`	DataLoader sub-configuration (see DataLoader Config Keys below).
`image_key`	`str`	`"image"`	CSV column name for image paths.
`mask_key`	`str`	`"mask"`	CSV column name for mask paths.
`n_first_rows_to_read`	`int`	`None`	If set, only the first N rows are read from the CSV via `pd.read_csv(..., nrows=N)`.

Key Methods

Method	Signature	Description
`__len__`	`() -> int`	Returns the number of rows in the dataset DataFrame.
`get_path`	`(idx, key=None, add_root_dir=True) -> str`	Returns the file path for item `idx` under the given CSV column key.
`load_image`	`(idx, key=None, is_mask=False, force_rgb=False, is_binary_mask=True) -> np.ndarray`	Loads and returns a numpy array for the given item.
`update_df`	`(new_df) -> None`	Replaces the internal DataFrame and updates `self.len`.

`ImageDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import ImageDataset

Image-only dataset. Returns a dict with the loaded image and its file path. Suitable for inference pipelines that do not require ground-truth masks.

Constructor Parameters

Inherits all parameters from AbstractDataset. The mask_key parameter is accepted by the parent but not used.

Parameter	Type	Default	Description
`input_csv_path`	`Path`	`None`	Path to the CSV file.
`df`	`pd.DataFrame`	`None`	Pre-loaded DataFrame.
`root_dir`	`Any`	`None`	Root directory for path resolution.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`image_key`	`str`	`"image"`	CSV column name for image paths.
`n_first_rows_to_read`	`int`	`None`	Limit on rows to read.

`getitem` Returns

Dict[str, Any] with keys:

Key	Type	Description
`"image"`	`np.ndarray` or `torch.Tensor`	The loaded image, optionally transformed.
`"path"`	`str`	Absolute path to the source image file.

`SegmentationDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import SegmentationDataset

Semantic segmentation dataset. Loads image-mask pairs and supports both PIL (RGB) and rasterio (multi-band/multispectral) image loading.

Constructor Parameters

Parameter	Type	Default	Description
`input_csv_path`	`Path`	`None`	Path to the CSV file. Mutually exclusive with `df`; at least one must be provided.
`df`	`pd.DataFrame`	`None`	Pre-built DataFrame with `image` and `mask` columns. Allows creating the dataset without a CSV file on disk (e.g. via `SegmentationDatasetFromFolder`).
`root_dir`	`Any`	`None`	Root directory for path resolution.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`image_key`	`str`	`"image"`	CSV column name for image paths.
`mask_key`	`str`	`"mask"`	CSV column name for mask paths.
`n_first_rows_to_read`	`int`	`None`	Limit on rows to read.
`n_classes`	`int`	`2`	Number of segmentation classes. When `2`, masks are binarized (`> 0`).
`selected_bands`	`Optional[List[int]]`	`None`	1-based list of band indices to load via rasterio. E.g. `[1, 2, 3]` loads the first three bands. When `None`, all bands are loaded.
`use_rasterio`	`bool`	`False`	When `True`, forces rasterio for image loading (recommended for multispectral imagery).
`image_dtype`	`str`	`"uint8"`	Data type applied to the image array after rasterio loading. Accepted values: `"uint8"`, `"uint16"`, `"float32"`, `"native"`. Only takes effect when rasterio is used (`use_rasterio=True` or `selected_bands` is set). `"native"` skips the cast entirely. Raises `ValueError` for unrecognised values.
`reset_augmentation_function`	`bool`	`False`	When `True`, deep-copies the augmentation pipeline before each call to prevent memory leaks from Albumentations caching.

`getitem` Returns

Dict[str, Any] with keys:

Key	Type	Description
`"image"`	`torch.Tensor` (C, H, W)	Float tensor. When no transform is set, automatically normalized: `uint8` → `/255`, `uint16` → `/65535`, `float32`/`native` → no division.
`"mask"`	`torch.Tensor` (H, W)	Long tensor of class labels. Binary (0/1) when `n_classes == 2`.

`SegmentationDatasetFromFolder`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import SegmentationDatasetFromFolder

Extends SegmentationDataset to discover image/mask pairs recursively from two root folders, without requiring a CSV file on disk. Matching is performed by relative subfolder path and file stem (name without extension): only files present in both folders, inside the same subfolder and with the same filename, are included in the dataset.

Expected Folder Structure

images_root/
    area_a/
        tile_001.tif
        tile_002.tif
    area_b/
        tile_003.tif

masks_root/
    area_a/
        tile_001.tif   ← paired with images_root/area_a/tile_001.tif
        tile_002.tif
    area_b/
        tile_003.tif

Only files with a matching (subfolder, stem) key in both trees are included. Files present in only one folder are silently ignored.

Constructor Parameters

Parameter	Type	Default	Description
`image_folder`	`Union[str, Path]`	required	Root folder containing the images.
`mask_folder`	`Union[str, Path]`	required	Root folder containing the masks.
`image_extension`	`str`	`".tif"`	File extension for images. The leading dot is optional and normalized internally (e.g. `"tif"` and `".tif"` are equivalent).
`mask_extension`	`Optional[str]`	`None`	File extension for masks. When `None`, uses the same value as `image_extension`.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`n_classes`	`int`	`2`	Number of segmentation classes. When `2`, masks are binarized (`> 0`).
`selected_bands`	`Optional[List[int]]`	`None`	1-based band indices to load via rasterio. `None` loads all bands.
`use_rasterio`	`bool`	`False`	Forces rasterio for image loading.
`image_dtype`	`str`	`"uint8"`	Data type for rasterio-loaded images. See `SegmentationDataset`.
`reset_augmentation_function`	`bool`	`False`	Deep-copy the transform to prevent Albumentations memory leaks.

Raises

Exception	When
`ValueError`	No matching image/mask pairs are found (wrong extension, mismatched subfolder structure, etc.).

Instance Attributes

After construction the following extra attributes are available:

Attribute	Type	Description
`image_folder`	`Path`	Resolved root folder for images.
`mask_folder`	`Path`	Resolved root folder for masks.
`image_extension`	`str`	Normalized image extension (with leading dot).
`mask_extension`	`str`	Normalized mask extension (with leading dot).

`getitem` Returns

Same as SegmentationDataset:

Key	Type	Description
`"image"`	`torch.Tensor` (C, H, W), float32	Normalized image tensor.
`"mask"`	`torch.Tensor` (H, W), int64	Class-index mask.

Static Helper Methods

Method	Description
`_normalize_extension(ext)`	Ensures the extension starts with a dot.
`_build_dataframe(image_folder, mask_folder, image_extension, mask_extension)`	Scans both folders recursively, matches pairs, and returns a `pd.DataFrame` with `image` and `mask` columns. Raises `ValueError` if no pairs are found.

`FrameFieldSegmentationDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import FrameFieldSegmentationDataset

Extends SegmentationDataset for frame-field polygon learning. Loads multiple auxiliary masks (boundary, vertex, crossfield angle, distance transform, size map) in addition to the primary segmentation mask.

Constructor Parameters

Parameter	Type	Default	Description
`input_csv_path`	`Path`	required	Path to the CSV file.
`root_dir`	`Any`	`None`	Root directory for path resolution.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`image_key`	`str`	`"image"`	CSV column name for image paths.
`mask_key`	`str`	`"polygon_mask"`	CSV column name for the primary polygon mask.
`multi_band_mask`	`bool`	`False`	When `True`, all three masks (polygon, boundary, vertex) are packed into a single multi-band image file.
`boundary_mask_key`	`str`	`"boundary_mask"`	CSV column for the boundary mask.
`return_boundary_mask`	`bool`	`True`	Whether to load and return the boundary mask.
`vertex_mask_key`	`str`	`"vertex_mask"`	CSV column for the vertex mask.
`return_vertex_mask`	`bool`	`True`	Whether to load and return the vertex mask.
`n_first_rows_to_read`	`int`	`None`	Limit on rows to read.
`return_crossfield_mask`	`bool`	`True`	Whether to load the crossfield angle mask.
`crossfield_mask_key`	`str`	`"crossfield_mask"`	CSV column for the crossfield angle image.
`return_distance_mask`	`bool`	`True`	Whether to load the distance transform mask.
`distance_mask_key`	`str`	`"distance_mask"`	CSV column for the distance map.
`return_size_mask`	`bool`	`True`	Whether to load the size map.
`size_mask_key`	`str`	`"size_mask"`	CSV column for the size map.
`image_width`	`int`	`224`	Target image width used in the fallback transform when an augmentation crop is invalid.
`image_height`	`int`	`224`	Target image height used in the fallback transform.
`gpu_augmentation_list`	`Any`	`None`	Reserved for GPU-side augmentation (not currently applied inside `__getitem__`).

`getitem` Returns

Dict[str, Any] with keys:

Key	Present when	Type	Description
`"idx"`	always	`int`	Dataset index of this item.
`"path"`	always	`str`	File path of the source image.
`"image"`	always	`torch.Tensor`	Input image tensor.
`"gt_polygons_image"`	always	`torch.Tensor` (C, H, W)	Stacked polygon/boundary/vertex masks.
`"class_freq"`	always	`torch.Tensor`	Per-channel class frequency used for loss weighting.
`"gt_crossfield_angle"`	`return_crossfield_mask=True`	`torch.Tensor` (1, H, W)	Crossfield angle map in radians.
`"distances"`	`return_distance_mask=True`	`torch.Tensor` (1, H, W)	Normalized distance transform.
`"sizes"`	`return_size_mask=True`	`torch.Tensor` (1, H, W)	Object size map.

`ObjectDetectionDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import ObjectDetectionDataset

Object detection dataset. Loads images with bounding boxes and class labels from JSON annotation files.

Constructor Parameters

Parameter	Type	Default	Description
`input_csv_path`	`Path`	required	Path to the CSV file.
`root_dir`	`Any`	`None`	Root directory for path resolution.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline with bbox support.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`image_key`	`str`	`"image"`	CSV column name for image paths.
`mask_key`	`str`	`"mask"`	CSV column name (not used for detection but inherited).
`bounding_box_key`	`str`	`"bounding_boxes"`	CSV column pointing to JSON files with bounding box annotations.
`n_first_rows_to_read`	`int`	`None`	Limit on rows to read.
`bbox_format`	`str`	`"xywh"`	Input bounding box format (`"xywh"` or `"xyxy"`).
`bbox_output_format`	`str`	`"xyxy"`	Output bounding box format after conversion (`"xywh"` or `"xyxy"`).
`bbox_params`	`Any`	`None`	Albumentations `BboxParams` (or equivalent dict/config) passed to `A.Compose`.

`getitem` Returns

Tuple[torch.Tensor, Dict[str, torch.Tensor], int]:

Position	Type	Description
`[0]`	`torch.Tensor`	Image tensor (RGB, float32).
`[1]`	`dict`	Dict with keys `"boxes"` (float32 tensor of bounding boxes) and `"labels"` (int64 tensor of class indices).
`[2]`	`int`	Dataset index of the item.

`InstanceSegmentationDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import InstanceSegmentationDataset

Extends ObjectDetectionDataset with optional per-instance mask and keypoint loading.

Constructor Parameters

Inherits all parameters from ObjectDetectionDataset, plus:

Parameter	Type	Default	Description
`keypoint_key`	`str`	`"keypoints"`	CSV column pointing to JSON files with keypoint or polygon annotations.
`return_mask`	`bool`	`True`	When `True`, loads and returns per-instance binary masks.
`return_keypoints`	`bool`	`False`	When `True`, loads and attaches keypoint data from the JSON file at `keypoint_key`.

The mask_key default is overridden to "polygon_mask".

`getitem` Returns

Same tuple structure as ObjectDetectionDataset. When return_mask=True the target dict additionally contains:

Key	Type	Description
`"masks"`	`torch.Tensor` (uint8)	Per-instance binary mask tensor.

`PolygonRNNDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import PolygonRNNDataset

Dataset for Polygon-RNN training and validation. Reads pre-cropped images and normalized polygon JSON files produced by the Dataset Conversion pipeline.

Constructor Parameters

Parameter	Type	Default	Description
`input_csv_path`	`Path`	required	Path to the CSV file (typically the output of `PolygonRNNDatasetConversionStrategy`).
`sequence_length`	`int`	`60`	Maximum number of polygon vertices in the output sequence. Shorter polygons are padded.
`root_dir`	`Any`	`None`	Root directory for path resolution.
`augmentation_list`	`Any`	`None`	Albumentations augmentation pipeline.
`data_loader`	`Any`	`None`	DataLoader sub-configuration.
`image_key`	`str`	`"image"`	CSV column for cropped image paths.
`mask_key`	`str`	`"mask"`	CSV column for polygon JSON paths.
`image_width_key`	`str`	`None`	CSV column for image width (not used internally but stored for downstream use).
`image_height_key`	`str`	`None`	CSV column for image height.
`scale_h_key`	`str`	`"scale_h"`	CSV column for vertical scale factor.
`scale_w_key`	`str`	`"scale_w"`	CSV column for horizontal scale factor.
`min_col_key`	`str`	`"min_col"`	CSV column for the crop left boundary (column).
`min_row_key`	`str`	`"min_row"`	CSV column for the crop top boundary (row).
`original_image_path_key`	`str`	`"original_image_path"`	CSV column for the source full-image path.
`original_polygon_key`	`str`	`"original_polygon_wkt"`	CSV column for the WKT representation of the original polygon.
`n_first_rows_to_read`	`str`	`None`	Limit on rows to read.
`dataset_type`	`str`	`"train"`	Either `"train"` or `"val"`. Validation mode returns additional metadata fields per item.
`grid_size`	`int`	`28`	Grid resolution used for polygon label encoding.

`getitem` Returns

Dict[str, Any] with keys:

Key	Present when	Type	Description
`"x1"`	always	`torch.Tensor` (float32)	Polygon label array at step 2 (first input to RNN).
`"x2"`	always	`torch.Tensor` (float32)	Polygon label array from step 0 to `T-2` (main RNN input sequence).
`"x3"`	always	`torch.Tensor` (float32)	Polygon label array from step 1 to `T-1` (shifted sequence).
`"ta"`	always	`torch.Tensor` (int64)	Target index array from step 2 to `T` (supervised labels).
`"image"`	always	`torch.Tensor` (float32)	Cropped image tensor.
`"polygon_wkt"`	`dataset_type="val"`	`str`	WKT of the original polygon for metric computation.
`"scale_h"`	`dataset_type="val"`	`float`	Vertical scale factor for back-projection.
`"scale_w"`	`dataset_type="val"`	`float`	Horizontal scale factor for back-projection.
`"min_col"`	`dataset_type="val"`	`float`	Crop left boundary for back-projection.
`"min_row"`	`dataset_type="val"`	`float`	Crop top boundary for back-projection.
`"original_image_path"`	`dataset_type="val"`	`str`	Source full-image path for visualization.

`TiledInferenceImageDataset`

from pytorch_segmentation_models_trainer.dataset_loader.dataset import TiledInferenceImageDataset

Extends ImageDataset for large-image inference. Slices each image into overlapping tiles so that a fixed-input-size model can process arbitrarily large images. Uses pytorch_toolbelt.inference.tiles.ImageSlicer internally.

Constructor Parameters

Inherits all parameters from ImageDataset, plus:

Parameter	Type	Default	Description
`normalize_output`	`bool`	`True`	When `True`, applies `A.Normalize()` (ImageNet mean/std) to each image before tiling.
`pad_if_needed`	`bool`	`False`	When `True`, pads the image to the nearest multiple of `model_input_shape` before tiling. Requires `model_input_shape` to be set.
`model_input_shape`	`Any`	`None`	Tuple `(H, W)` of the model's expected input tile size. Required when `pad_if_needed=True`.
`step_shape`	`Any`	`(224, 224)`	Tile step (stride) for the `ImageSlicer`. Smaller values increase overlap.

The augmentation_list parameter is accepted but ignored; normalization is controlled by normalize_output instead.

`getitem` Returns

Dict[str, Any] with keys:

Key	Type	Description
`"path"`	`str`	File path of the source image.
`"tiles"`	`torch.Tensor` (N_tiles, C, H, W)	Stack of all tiles from this image.
`"tile_image_idx"`	`torch.Tensor` (N_tiles,)	Image index repeated for each tile (for reassembly).
`"tiler_object"`	`ImageSlicer`	Slicer object needed to merge tile predictions back.
`"original_shape"`	`tuple`	`(width, height)` of the original image.

A collate_fn static method is provided for use with DataLoader to correctly batch variable-tile-count images.

DataLoader Config Keys

All dataset classes accept a data_loader sub-configuration. When used with Hydra this is typically a nested config node. The following keys are recognised:

Key	Type	Default	Description
`shuffle`	`bool`	`True`	Whether to shuffle the dataset each epoch. Typically `True` for training, `False` for validation.
`num_workers`	`int`	`0`	Number of worker processes for data loading.
`pin_memory`	`bool`	`False`	Whether to pin memory for faster GPU transfer.
`batch_size`	`int`	`8`	Number of samples per batch.
`drop_last`	`bool`	`False`	Whether to drop the last incomplete batch.

Example:

data_loader:
  shuffle: true
  num_workers: 4
  pin_memory: true
  batch_size: 16
  drop_last: false

CSV Format Conventions

Each dataset type expects specific columns in the input CSV:

`SegmentationDataset` / `FrameFieldSegmentationDataset`

Column	Required	Description
`image`	yes	Path to the input image (RGB or multi-band).
`mask` / `polygon_mask`	yes	Path to the ground-truth mask image.
`boundary_mask`	`FrameField` only	Path to the boundary mask image.
`vertex_mask`	`FrameField` only	Path to the vertex mask image.
`crossfield_mask`	`FrameField` only	Path to the crossfield angle image.
`distance_mask`	`FrameField` only	Path to the distance transform image.
`size_mask`	`FrameField` only	Path to the size map image.
`class_freq`	optional	Pre-computed class frequency string to skip per-sample computation.

`ObjectDetectionDataset` / `InstanceSegmentationDataset`

Column	Required	Description
`image`	yes	Path to the input image.
`bounding_boxes`	yes	Path to a JSON file with a list of `{"bbox": [...], "class": N}` entries.
`polygon_mask`	`InstanceSeg` only	Path to the binary mask image.
`keypoints`	`InstanceSeg` only	Path to a JSON file with a `"keypoints"` key.

`PolygonRNNDataset`

Column	Required	Description
`image`	yes	Path to the cropped object image.
`mask`	yes	Path to the normalized polygon JSON file.
`scale_h`	yes	Vertical scale factor for coordinate back-projection.
`scale_w`	yes	Horizontal scale factor.
`min_col`	yes	Left boundary of the original crop.
`min_row`	yes	Top boundary of the original crop.
`original_image_path`	yes (val)	Path to the source full-size image.
`original_polygon_wkt`	yes (val)	WKT of the original polygon.

Class Hierarchy​

RasterPatchDataset​

Constructor Parameters​

Raises​

Key Attributes​

__len__​

__getitem__ Returns​

AbstractDataset​

Constructor Parameters​

Key Methods​

ImageDataset​

Constructor Parameters​

__getitem__ Returns​

SegmentationDataset​

Constructor Parameters​

__getitem__ Returns​

SegmentationDatasetFromFolder​

Expected Folder Structure​

Constructor Parameters​

Raises​

Instance Attributes​

__getitem__ Returns​

Static Helper Methods​

FrameFieldSegmentationDataset​

Constructor Parameters​

__getitem__ Returns​

ObjectDetectionDataset​

Constructor Parameters​

__getitem__ Returns​

InstanceSegmentationDataset​

Constructor Parameters​

__getitem__ Returns​

PolygonRNNDataset​

Constructor Parameters​

__getitem__ Returns​

TiledInferenceImageDataset​

Constructor Parameters​

__getitem__ Returns​

DataLoader Config Keys​

CSV Format Conventions​

SegmentationDataset / FrameFieldSegmentationDataset​

ObjectDetectionDataset / InstanceSegmentationDataset​

PolygonRNNDataset​

Class Hierarchy

`RasterPatchDataset`

Constructor Parameters

Raises

Key Attributes

`len`

`getitem` Returns

`AbstractDataset`

Constructor Parameters

Key Methods

`ImageDataset`

Constructor Parameters

`getitem` Returns

`SegmentationDataset`

Constructor Parameters

`getitem` Returns

`SegmentationDatasetFromFolder`

Expected Folder Structure

Constructor Parameters

Raises

Instance Attributes

`getitem` Returns

Static Helper Methods

`FrameFieldSegmentationDataset`

Constructor Parameters

`getitem` Returns

`ObjectDetectionDataset`

Constructor Parameters

`getitem` Returns

`InstanceSegmentationDataset`

Constructor Parameters

`getitem` Returns

`PolygonRNNDataset`

Constructor Parameters

`getitem` Returns

`TiledInferenceImageDataset`

Constructor Parameters

`getitem` Returns

DataLoader Config Keys

CSV Format Conventions

`SegmentationDataset` / `FrameFieldSegmentationDataset`

`ObjectDetectionDataset` / `InstanceSegmentationDataset`

`PolygonRNNDataset`