getitune-preparing-datasets

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Original

English

🇨🇳

Translation

Chinese

Preparing datasets for getitune

为getitune准备数据集

When you pass a filesystem path to

data=

(Python API) or

--data_root

(CLI),

getitune

uses Datumaro to auto-detect the dataset format — you point at the dataset root and the same call works regardless of the underlying format.

Run everything from

library/

当你将文件系统路径传递给

data=

（Python API）或

--data_root

（CLI）时，

getitune

会使用Datumaro来 自动检测数据集格式——你只需指向数据集根目录，无论底层格式是什么，同一个调用都能生效。

所有操作请在

library/

目录下执行。

Supported formats and how they are detected

支持的格式及其检测方式

Format	Detected by
COCO	an `annotations/` directory with COCO JSON files
YOLO	a `data.yaml` file (Ultralytics layout)
Pascal VOC	`JPEGImages/` , `Annotations/` , `ImageSets/` directories
Datumaro (native)	`metadata.json` + `data.parquet` at the root

Zip archives are accepted too; Datumaro extracts them on import.
Point
```
data=
```
at the dataset root — the directory that directly contains the marker files/folders above, not a parent of it.

格式	检测依据
COCO	包含COCO JSON文件的 `annotations/` 目录
YOLO	`data.yaml` 文件（Ultralytics布局）
Pascal VOC	`JPEGImages/` 、 `Annotations/` 、 `ImageSets/` 目录
Datumaro（原生）	根目录下的 `metadata.json` + `data.parquet` 文件

也支持Zip归档文件；Datumaro会在导入时自动解压。
将
```
data=
```
指向数据集根目录——即直接包含上述标记文件/文件夹的目录，而非其父目录。

Workflow

工作流程

python

from getitune.engine import create_engine

python

from getitune.engine import create_engine

Same call for any supported format — just point at the root

任何支持格式都可使用相同调用——只需指向根目录

engine = create_engine( model="src/getitune/recipe/detection/yolox_s.yaml", data="/path/to/dataset_root", ) engine.train()


1. **Lay the dataset out as one supported format** and confirm the marker
   files/folders sit at the root you will pass.
   - Done when: the root matches exactly one row in the table above.
2. **Match the dataset to the task.** A detection dataset needs bounding-box
   annotations; segmentation needs masks; classification needs per-image labels.
   Task and labels must agree with the model you choose in the
   `getitune-training-a-model` skill.
   - Done when: `create_engine(...)` builds a datamodule without a
     feature/label mismatch error.
3. **Smoke-test loading** with a tiny run (`engine.train(max_epochs=1)`) before a
   full run.
   - Done when: one train + one validation batch load without shape errors.

engine = create_engine( model="src/getitune/recipe/detection/yolox_s.yaml", data="/path/to/dataset_root", ) engine.train()


1. **按照一种支持的格式布局数据集**，并确认标记文件/文件夹位于你要传递的根目录下。
   - 完成标志：根目录与上表中的某一行完全匹配。
2. **使数据集与任务匹配**。检测数据集需要边界框标注；分割数据集需要掩码；分类数据集需要逐图像标签。任务和标签必须与你在`getitune-training-a-model`技能中选择的模型一致。
   - 完成标志：`create_engine(...)`构建数据模块时未出现特征/标签不匹配错误。
3. **通过小型运行进行加载冒烟测试**（`engine.train(max_epochs=1)`），再进行完整运行。
   - 完成标志：一个训练批次和一个验证批次加载时未出现形状错误。

Ultralytics YOLO datasets

Ultralytics YOLO数据集

If you train an Ultralytics YOLO model, pass the Ultralytics

data.yaml

file directly as

data=

(or

--data_root

). Ultralytics support requires an install from source with the

ultralytics

extra (it is not in the PyPI package).

如果你训练Ultralytics YOLO模型，请直接将Ultralytics的

data.yaml

文件作为

data=

（或

--data_root

）传递。Ultralytics支持需要从源码安装并添加

ultralytics

扩展（该扩展未包含在PyPI包中）。

Debugging auto-detection

自动检测调试

Wrong/failed format detection: the root probably has extra nesting or a missing marker. Verify the exact marker files (
```
annotations/
```
for COCO,
```
data.yaml
```
for YOLO, the three VOC dirs,
```
metadata.json
```
+
```
data.parquet
```
for native) are directly under the path you pass.
Feature/label mismatch during training: the dataset's annotation type does not match the task — cross-check with
```
getitune-training-a-model
```
and pass an explicit
```
task=
```
.
Backend dataset conversion: the Geti application converts Geti-internal datasets to/from COCO/VOC via
```
application/backend/app/datumaro_converter/
```
; that is a separate, app-side path from library
```
data=
```
usage.

格式检测错误/失败：根目录可能存在额外嵌套或缺少标记文件。请验证你传递的路径下是否直接存在确切的标记文件（COCO对应
```
annotations/
```
，YOLO对应
```
data.yaml
```
，VOC对应三个目录，原生格式对应
```
metadata.json
```
+
```
data.parquet
```
）。
训练期间特征/标签不匹配：数据集的标注类型与任务不匹配——请与
```
getitune-training-a-model
```
交叉核对，并显式传递
```
task=
```
参数。
后端数据集转换：Geti应用通过
```
application/backend/app/datumaro_converter/
```
在Geti内部数据集与COCO/VOC之间进行转换；这是应用端的独立路径，与库的
```
data=
```
使用方式无关。