name: data-transforms-datasets
description: "Prepare and validate GluonCV datasets, data transforms, batchify
functions, metrics, and visualization helpers."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
GluonCV data transforms and datasets
Use this sub-skill when the task is about GluonCV dataset roots/layouts, object-detection records, segmentation/classification/video/tracking/re-id/depth datasets, image or bounding-box transforms, preset data transforms, DataLoader batchification, data metrics, or visualization helpers.
Natural triggers
Load this sub-skill for requests mentioning:
- GluonCV datasets or
gluoncv.data.* classes.
- Pascal VOC, COCO, ADE20K, Cityscapes, ImageNet, Kinetics, UCF101, HMDB51, Something-Something-V2, Market1501, OTB, KITTI, MHP, VisDrone, LST, or RecordIO.
- Bounding-box transforms, image resize/crop/flip/ten-crop, mask/pose/video transforms, or detector presets for SSD, Faster R-CNN, Mask R-CNN, YOLO, and CenterNet.
Stack, Pad, Append, Tuple, FasterRCNNTrainBatchify, MaskRCNNTrainBatchify, DetectionDataLoader, or RandomTransformDataLoader.- Dataset preparation helpers, frame extraction, train/val list building,
--download-dir, --no-download, --overwrite, or ImageRecord conversion.
- Detection annotation validation before passing records into GluonCV transforms or training scripts.
Route away
- Model name selection, model instantiation, custom heads, and MXNet model dry-runs: use
../mxnet-model-zoo/.
- PyTorch action-recognition models, configs, DirectPose, or Torch DDP: use
../torch-video-workflows/.
- Full training/evaluation/demo command assembly from the script zoo: use
../training-evaluation-scripts/.
- AutoGluon wrappers, deployment, ONNX, TVM, or export: use
../automl-deployment-export/.
First workflow choice
- Identify the data family and root. Use datasets-and-transforms.md to pick a dataset class, expected layout, split argument, and optional dependencies. Prefer passing explicit
root=/setting= instead of relying on default user home directories when an experiment uses project-local data.
- Validate custom detection annotations early. For simple JSON records, run validate_detection_record.py before involving MXNet/GluonCV. For LST/RecordIO or VOC-like data, compare against the schemas in datasets-and-transforms.md.
- Wire transforms with label semantics. Geometric image transforms that change size, crop, or flip require the corresponding bbox/mask/pose transform. Use api-reference.md for coordinate order, size argument order, preset transform inputs, and batchify pairing.
- Choose batchify by output shape. Use
Stack only for same-shaped arrays, Pad for variable-length labels, Append for ragged per-sample structures, and Tuple(...) to apply a per-field function to (image, label, ...) samples.
- Add diagnostics and visual checks. Use GluonCV visualization helpers (
plot_bbox, plot_image, segmentation palettes) and metrics only after validating data format. Keep display/non-headless concerns separate from dataset parsing.
- If data is large or network-bound, plan without running downloads. Dataset preparation scripts are reference-only for this skill; record the required archives, flags, generated folders/lists/RecordIO files, and storage constraints before asking to run a large command.
Core facts to preserve
- Detection labels consumed by GluonCV dataset/transforms use
[xmin, ymin, xmax, ymax, class_id] plus optional extra columns. VOC labels include an additional difficult column.
- Bbox size tuples are
(width, height), while image tensors are HWC. Mixing these orders is a common source of silent annotation corruption.
- COCO detection/instance/keypoints classes rely on
pycocotools and the COCO category order matching the class list.
- Video datasets generally require decoded frame folders plus a setting/list file;
decord is optional for direct video loading.
- Most built-in datasets default under
~/.mxnet/datasets/...; production workflows should pass explicit roots when data is not there.
References and helper