# AI use cases for Yolo Web Agent Starter (fengzhizi715/YoloWebAgentStarter)

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Use cases: 5 | Page: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/

A local, single-user web workbench for YOLO datasets that covers image and video import, manual annotation, validation, YOLO and COCO exchange, local training, native YOLO evaluation, and an evidence-based AI assistant.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Label images and train a YOLO model on your own machine: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/label-and-train-yolo-dataset-locally/
- Turn MP4, MOV, or AVI footage into a dataset of frames: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/turn-video-footage-into-labeled-frames/
- Pre-label a dataset with a trained model, then review before training: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/auto-label-then-review-before-training/
- Score a trained model on a held-out split and inspect its mistakes: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/evaluate-model-and-review-error-samples/
- Ask the built-in assistant to diagnose a failed training run: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/diagnose-training-with-approval-gated-agent/

## How to label images and train a YOLO model on your own machine with Yolo Web Agent Starter

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Difficulty: Intermediate | Time: 2 hours | Basis: From the README

Annotate detect, segment, OBB, or classify data in a browser and start a local training job.

**Who it is for:** A computer vision engineer with a folder of photos who needs a first custom YOLO model and wants all data kept on a local machine.

**The problem:** Preparing YOLO data by hand means juggling label files, splits, and training scripts across several tools.

**The outcome:** A validated dataset with a persisted train/val/test split, exported YOLO data, and best.pt and last.pt registered in the managed model library.

### Steps

1. Run `python3.12 -m venv .venv`, then `.venv/bin/pip install -r backend/requirements.txt -r backend/requirements-dev.txt`, then `npm --prefix frontend install`.
2. Start both services with `./run-all.sh` and open the web app in a browser.
3. Create a detect, segment, obb, or classify dataset, upload images, or place images under data/imports/ and scan them in the interface.
4. Add classes and annotate with bbox, polygon, OBB, or a single classify label per image.
5. Make sure train and val each have at least one annotated image, run dataset validation, then export or import YOLO data.
6. On the training page, pick a matching YOLOv8, YOLO11, or YOLO26 base weight and start a local job, then check the managed best.pt and last.pt in the model workspace.

### Prompt to paste into your agent

```text
Using the YoloWebAgentStarter repo at <repo path>, set up the local workbench and create a detect dataset from the images in <image folder>. Confirm that validation passes and that the dataset has annotated images in both train and val. Report the dataset ID, the validation summary, and any blocking errors.
```

### Caveat

Named base weights may be downloaded by Ultralytics on first use, so confirm the license before using them, and do not commit weights, datasets, or outputs.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/label-and-train-yolo-dataset-locally/

## How to turn MP4, MOV, or AVI footage into a dataset of frames with Yolo Web Agent Starter

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Extract frames from a video in the Import Center and keep each frame traceable to its source.

**Who it is for:** A field operator or researcher with recorded video who needs training frames without extracting them by hand.

**The problem:** Pulling usable frames from video by hand is slow and loses track of which clip each frame came from.

**The outcome:** A new dataset of JPEG frames, each saved with its source video, checksum, task ID, frame number, and timestamp, and split by the chosen strategy.

### Steps

1. Start the workbench with `./run-all.sh`.
2. Open Dataset, then Import Dataset, and choose the video file option.
3. Upload an MP4, MOV, or AVI file, name the new dataset, pick the task type and initial split, and set the FPS, time interval, or frame interval.
4. Review the decoded sample frame, resolution, duration, expected frame count, and estimated JPEG output size, then start the import.
5. If you choose time-block assignment, set the window and the train/val/test ratios so each block stays in one split, then check the frame split before labeling.

### Prompt to paste into your agent

```text
In the YoloWebAgentStarter repo at <repo path>, import the video <video file> as a new dataset with a frame interval. Verify that the import finishes and that the frame records keep the source video, checksum, and timestamp. Return the dataset ID and the frame count.
```

### Caveat

Consecutive video frames are usually very similar, so random splitting can inflate evaluation results, and the README advises checking whether the time-block strategy fits your capture scenario.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/turn-video-footage-into-labeled-frames/

## How to pre-label a dataset with a trained model, then review before training with Yolo Web Agent Starter

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Run a local auto-label job with a managed PT model and keep the results as reviewable auto labels.

**Who it is for:** A dataset owner who already has a trained model and wants to speed up labeling on a new batch of images.

**The problem:** Labeling a large batch from scratch takes hours, while blindly training on machine labels can teach the model its own mistakes.

**The outcome:** Auto-generated labels stored with the auto source tag, reviewed by a person, and then used in a later training run.

### Steps

1. Start the workbench with `./run-all.sh` and open the dataset card for the target dataset.
2. Start a local auto-label task, choose a matching managed PT model, and confirm the class mapping explicitly.
3. Adjust the confidence and IoU thresholds, decide whether to skip images that already have labels, and choose whether to clear old labels.
4. Watch progress in the logs, cancel if needed, and review each auto label before changing it.
5. Run training only after the reviewed labels are in place, since training and auto-labeling cannot run at the same time on this machine.

### Prompt to paste into your agent

```text
In the YoloWebAgentStarter repo at <repo path>, start a local auto-label task for dataset <dataset ID> using the managed PT model <model ID>. Verify that the resulting labels are stored with the auto source and that no training job starts before review. Report the number of labeled images and the class mapping used.
```

### Caveat

Auto-labeling only supports local managed Ultralytics PT models, so a model trained outside the workbench cannot be used for this step.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/auto-label-then-review-before-training/

## How to score a trained model on a held-out split and inspect its mistakes with Yolo Web Agent Starter

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Run native YOLO val on a managed PT model and review up to 200 saved error samples.

**Who it is for:** A model developer who has a trained checkpoint and needs honest metrics on the validation or test split before deciding to use it.

**The problem:** A single mAP number hides where the model fails, and rerunning evaluation by hand loses context.

**The outcome:** Saved metrics, logs, confusion matrix, PR curves when generated, and a list of missed detections, false positives, and low-confidence samples to review.

### Steps

1. Start the workbench with `./run-all.sh`.
2. Open the model workspace and pick a managed PT model produced by a training run.
3. Create an evaluation task on the train, val, or test split and wait for the background job to finish.
4. Open the evaluation details to read the metrics, confusion matrix, and charts, then review the error samples.
5. Compare results across runs only on the same dataset, since the README notes that a data snapshot fingerprint is not yet verifiable.

### Prompt to paste into your agent

```text
In the YoloWebAgentStarter repo at <repo path>, run a background evaluation of managed model <model ID> on the test split of dataset <dataset ID>. Verify that the metrics, logs, and error sample list are saved and at most 200 entries. Return the box metrics and the count of each error type.
```

### Caveat

Chart files only appear when Ultralytics generates them, so a tiny random model may produce metrics and logs without any PR curve.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/evaluate-model-and-review-error-samples/

## How to ask the built-in assistant to diagnose a failed training run with Yolo Web Agent Starter

Repo: [fengzhizi715/YoloWebAgentStarter](https://github.com/fengzhizi715/YoloWebAgentStarter) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Use the Agent MVP to read evidence about a training job, then approve or reject any task it proposes.

**Who it is for:** A user whose training job failed and who wants a plain explanation with the evidence behind it, without the assistant starting work on its own.

**The problem:** Reading logs and config snapshots across several pages is slow, and an automated assistant that launches jobs could waste compute.

**The outcome:** A diagnostic answer backed by tool evidence, and optionally a confirmed training or evaluation task that the user approved before it ran.

### Steps

1. Start the workbench with `./run-all.sh`, then open the training page and click 询问助手 on the failed run to bind it to the assistant.
2. Keep the default Mock / rule mode, or open Settings, then Agent LLM, enable a provider, enter the base URL, model, and key, test the connection, and save.
3. Tick 仅查询 (query only) if you want a read-only answer, then ask why the run failed and wait for the async run to finish.
4. Review the tool evidence and reasoning record, and if the assistant proposes a task, check its object, parameters, and expected output before choosing 确认执行 or rejecting it.
5. If the run fails or is cancelled, retry it, which keeps the original mode, object, and read-only permission.

### Prompt to paste into your agent

```text
In the YoloWebAgentStarter repo at <repo path>, bind the Training Assistant to training run <run ID> and ask for the likely cause of the failure in read-only mode. Verify that the answer cites tool evidence and that no task is submitted. Return the cause and the evidence list.
```

### Caveat

A successful connection test does not prove the model can complete the full tool-calling workflow, so check a real diagnosis before relying on the assistant.

Risk, credentials: If you enable an external LLM, your question, object context, and tool results are sent to that service, and the API key must be entered in Settings and never shared in logs.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/diagnose-training-with-approval-gated-agent/