> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/models/evaluate/training-results.md).
# View Training Results
## About
Roboflow produces training graphs and summary metrics for every model you train on the platform, letting you monitor a job while it runs and evaluate the resulting model once it finishes. The metrics shown depend on your project type - precision, recall, and mAP for object detection; accuracy for classification; mAP for segmentation and keypoint models; and perplexity for multimodal models. You can also test a finished model on sample or custom images before wiring it into an application.
Training graphs let you see the status of your model during training. Training graphs are available for all models trained on Roboflow.
You will also see metrics that summarise your model performance.
The metrics you will see will depend on your model type:
* Object detection projects show the precision, recall, and mAP of the model.
* Classification projects show accuracy.
* Segmentation and keypoint models show mAP score.
* Multimodal models show perplexity.
## During Training
After you start training a model, a message will appear on the dataset version page associated with the model you are training. This message will first say that a training machine is starting.
If a version has [more than one training](/models/train/train-a-model.md#train-multiple-models-on-a-version), select a training on the version page to view its graphs and metrics.
You will then see training graphs appear live as your model trains:
Training graphs as a model trains.
### NAS Training Charts
For models trained with Neural Architecture Search (NAS) on object detection or instance segmentation projects, a specialized paired chart view is displayed instead of standard training graphs:
* **Training Progress Band**: A gradient-filled band showing the range of model performance (upper and lower bounds) over training epochs. Hover over any epoch to inspect metrics at that point. A pulsing indicator shows the current training front during live training.
* **Epoch Snapshot**: A Pareto curve showing the trade-off between model latency and accuracy for the architectures evaluated at each epoch. The gradient spectrum ranges from faster (lighter) to more accurate (darker) models.
Unlike Standard and Plus NAS runs, Fast NAS runs have no baseline configurations to report a spread of metrics for during training, so they report metrics only for the single supernet being trained. These runs show the standard training graph instead of the paired band and snapshot view.
## After Training
When your model has finished training, you can view metrics from the training process.
To find your training graphs for a model after training, first click the Models tab in the sidebar:
Then, click on the [training (or model)](/models/versions-trainings-and-models.md) whose training graphs you want to see.
Scroll down on the page until you see the Training Graphs section:
## Test Your Model
You can test your model on an image from the Test page. This is a good way to run quick checks to see how your model performs before you build your application logic in Workflows.
The Test feature works for object detection, segmentation, classification, and keypoint models. Test is not supported for multimodal projects.
To test a model, click "Test" on the right sidebar. The Test page will then open:
The Test page will show several images from your test set from which you can select to run on your model. You can also upload your own images and videos, or try with your webcam.
To find deployment instructions for your model, click Try on My Machine. You can also deploy your model on Roboflow Workflows.
## HTTP API
### Get Training Results
Retrieve the training results for a dataset version. For NAS (Neural Architecture Search) training runs, this returns a bundled view including all child models, their metrics, hardware-specific recommendations, and mining frontier data. For standard fine-tune runs, it returns a minimal summary.
This endpoint consolidates multiple reads into a single call, so you don't need to fetch model data separately.
#### Endpoint
```
GET /:workspace/:project/:version/training/results
```
**Authentication:** Requires an API key with `version.read` scope.
#### Request
```bash
curl "https://api.roboflow.com/{workspace}/{project}/{version}/training/results?api_key=$ROBOFLOW_API_KEY"
```
#### Response (NAS Training)
```json
{
"trainingId": "my-workspace/my-project/5",
"versionId": "5",
"status": "finished",
"jobType": "nas",
"modelType": "rfdetr-nas-parent",
"modelGroup": "rfdetrNasGroup-2026-04-01",
"modelCount": 8,
"recommendedByHardware": {
"jetson-orin-nano": "nas-child-1",
"t4": "nas-child-2"
},
"mining": {
"mining": {
"progress": 0.42,
"frontier": [{"name": "subnet-1", "mAP": 0.81}]
},
"baseline": [{"modelTypeDisplay": "rfdetr-nas-S"}]
},
"models": [
{
"modelId": "nas-child-1",
"modelType": "rfdetr-nas-S",
"status": "finished",
"nasFamily": "child",
"metrics": {
"map50": 81,
"map5095": 64,
"f1": 78,
"hardware": "jetson-orin-nano",
"latency": 12.4,
"paretoOptimalFor": ["jetson-orin-nano"]
},
"recommended": true
}
]
}
```
#### Response (Fine-Tune Training)
For non-NAS training runs, the response is a minimal bundle:
```json
{
"trainingId": "my-workspace/my-project/3",
"versionId": "3",
"status": "finished",
"jobType": "fine-tune",
"modelGroup": null,
"modelCount": 0,
"models": []
}
```
#### Response Fields
| Field | Type | Description |
| ----------------------- | -------------- | ---------------------------------------------------------------------------- |
| `trainingId` | string | Composite ID in `workspace/project/versionId` format |
| `status` | string | Training status (e.g. `finished`, `running`, `failed`) |
| `jobType` | string | `nas` for NAS runs, `fine-tune` for standard training |
| `modelType` | string | The model architecture used for training |
| `modelGroup` | string \| null | Group identifier for NAS child models. `null` for non-NAS runs |
| `modelCount` | number | Number of models produced by the training run |
| `recommendedByHardware` | object | Map of hardware target to the recommended model ID for that target. NAS only |
| `mining` | object | Mining frontier and snapshot data, if available. NAS only |
| `models` | array | Per-model summaries (see below) |
**Model summary fields:**
| Field | Type | Description |
| ------------- | -------------- | -------------------------------------------------------------------------------------------------------------- |
| `modelId` | string | Public model identifier (URL slug). This is the same value used in `POST /:workspace/models/:modelId/favorite` |
| `modelType` | string | Model architecture variant |
| `status` | string | Training status for this model |
| `nasFamily` | string | `child` or `baseline`. NAS only |
| `metrics` | object \| null | Training metrics (mAP, latency, etc.), scaled to percentages. `null` if training is still in progress |
| `recommended` | boolean | Present and `true` if this model is a recommended pick |
#### Errors
| Status | Description |
| ------ | ---------------------------------- |
| 404 | Version not found |
| 401 | Invalid or missing API key |
| 403 | API key lacks `version.read` scope |