# Upload a Dataset

`Workspace.upload_dataset()` uploads a structured dataset (images + matching annotations) to a Roboflow project. The project will be created if it doesn't exist; otherwise the new images get added to the existing project.

```python
import roboflow

rf = roboflow.Roboflow(api_key="YOUR_API_KEY")
workspace = rf.workspace()

workspace.upload_dataset(
    "./dataset/",                  # path to a structured dataset directory
    "my-detector",                 # project id (created if it doesn't exist)
    num_workers=10,
    project_license="MIT",
    project_type="object-detection",
    batch_name=None,
    num_retries=0,
    is_prediction=False,           # True for model-generated annotations awaiting review
)
```

## Parameters

* `dataset_path` (str) - path to the dataset root.
* `project_name` (str) - destination project's id. Created if it doesn't exist.
* `num_workers` (int, default `10`) - concurrent uploads. We recommend not exceeding 25.
* `project_license` (str, default `"MIT"`) - license for a newly-created project. Set to `"Private"` for private projects (paid plans only).
* `project_type` (str, default `"object-detection"`) - type for a newly-created project. Ignored if the project already exists.
* `batch_name` (str, optional) - group these uploads under a named batch. Useful for tracking the source of a labeling round.
* `num_retries` (int, default `0`) - retry transient upload failures.
* `is_prediction` (bool, default `False`) - set to `True` to upload annotations as model predictions awaiting review rather than ground truth.

## Expected directory layout

For a COCO dataset:

```
my_dataset/
├── train/
│   ├── image1.jpg
│   └── _annotations.coco.json
├── valid/
│   ├── image2.jpg
│   └── _annotations.coco.json
└── test/
    ├── image3.jpg
    └── _annotations.coco.json
```

For VOC, drop matching `.xml` files alongside each image. For YOLO, drop matching `.txt` files plus a `data.yaml` describing the class list.

## Note on SHA-256 dedup (v1.3.6+)

As of `roboflow` 1.3.6, the SDK uploads original image bytes rather than re-encoding via Pillow. This brings parity with the web uploader and lets the Roboflow server deduplicate uploads by SHA-256 - re-uploading the same image (e.g. into a different batch) succeeds without consuming additional storage credits.

## REST and CLI equivalents

* REST: see [Upload an Image (REST)](/developer/rest-api/manage-images/upload-an-image.md) for the per-image endpoint that `upload_dataset` calls under the hood.
* CLI: see [Upload a Dataset (CLI)](/developer/command-line-interface/upload-a-dataset.md).


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