List Project Models

You can retrieve all trained models in a project using the /:workspace/:project/models endpoint. This returns both version-trained models and standalone models (such as NAS children).

With Sign In With Roboflow (Getting Started), use Authorization: Bearer and scope model:infer instead of api_key:

curl -H "Authorization: Bearer $ACCESS_TOKEN" \
  "https://api.roboflow.com/:workspace/:project/models"

List All Models

curl "https://api.roboflow.com/:workspace/:project/models?api_key=$ROBOFLOW_API_KEY"

Filter by NAS Group

To list only the models from a specific NAS run, pass the group query parameter:

curl "https://api.roboflow.com/:workspace/:project/models?group=GROUP_ID&api_key=$ROBOFLOW_API_KEY"

The group value is the NAS run identifier returned on each model object. If the group doesn't match any models, the endpoint returns an empty array.

Response

The endpoint returns a JSON array of model objects:

[
  {
    "url": "my-workspace/my-project/3",
    "version": "3",
    "train": { "status": "finished" },
    "modelType": "rfdetr-base",
    "name": "My Model",
    "created": "2026-04-01T00:00:00.000Z",
    "metrics": {
      "map50": 91.0,
      "precision": 88.0,
      "recall": 85.0
    }
  }
]

NAS Model Fields

Models produced by Neural Architecture Search include additional fields:

{
  "url": "my-workspace/my-project-410-nas-gpu-066866",
  "train": { "status": "finished" },
  "modelType": "rfdetr-nas-S",
  "name": "NAS Child 1",
  "created": "2026-04-01T00:00:00.000Z",
  "nasFamily": "child",
  "group": "pVYKOWUB6AUIVJMgPc7u-410-rfdetrNasGroup",
  "favorites": {},
  "recommended": true,
  "metrics": {
    "map50": 81.2,
    "map5095": 64.3,
    "f1": 78.0,
    "hardware": "gpu",
    "latency": 1.157,
    "paretoOptimalFor": ["gpu"]
  }
}

Field	Type	Description
nasFamily	string	"child" for NAS-discovered models, null for the baseline
group	string	NAS run identifier, shared by all models in the same run
favorites	object	Map of user IDs to favorite status
recommended	boolean	Present and true when this model is the recommended pick for at least one metric/hardware combination
metrics.map5095	number	mAP@50-95 score (percentage)
metrics.f1	number	F1 score (percentage)
metrics.hardware	string	Hardware target the model was benchmarked on (e.g. "gpu", "jetson-orin-nano")
metrics.latency	number	Inference latency in milliseconds on the target hardware
metrics.paretoOptimalFor	string[]	Hardware targets for which this model sits on the Pareto frontier

These fields are only present on NAS models. Standard trained models are not affected.