DEDUPEDWhat do you want to do?
5 use cases
-
Label images and train a YOLO model on your own machine
Annotate detect, segment, OBB, or classify data in a browser and start a local training job.
- REPO
fengzhizi715/YoloWebAgentStarter - Software and DevOps
- Intermediate
View prompt
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.
- REPO
-
Turn MP4, MOV, or AVI footage into a dataset of frames
Extract frames from a video in the Import Center and keep each frame traceable to its source.
- REPO
fengzhizi715/YoloWebAgentStarter - Software and DevOps
- Intermediate
View prompt
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.
- REPO
-
Pre-label a dataset with a trained model, then review before training
Run a local auto-label job with a managed PT model and keep the results as reviewable auto labels.
View prompt
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.
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Score a trained model on a held-out split and inspect its mistakes
Run native YOLO val on a managed PT model and review up to 200 saved error samples.
- REPO
fengzhizi715/YoloWebAgentStarter - Software and DevOps
- Intermediate
View prompt
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.
- REPO
-
Ask the built-in assistant to diagnose a failed training run
Use the Agent MVP to read evidence about a training job, then approve or reject any task it proposes.
- REPO
fengzhizi715/YoloWebAgentStarter - Software and DevOps
- Intermediate
View prompt
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.
- REPO
Quick start with Yolo Web Agent Starter
- Check you have what it needs: macOS or Linux; Python 3.11 or 3.12; Node.js 20 or newer.
- Open fengzhizi715/YoloWebAgentStarter on GitHub and read the README and license.
- Run
python3.12 -m venv .venv, then.venv/bin/pip install -r backend/requirements.txt -r backend/requirements-dev.txt, thennpm --prefix frontend install. - Start both services with
./run-all.shand open the web app in a browser. - Create a detect, segment, obb, or classify dataset, upload images, or place images under data/imports/ and scan them in the interface.
- Add classes and annotate with bbox, polygon, OBB, or a single classify label per image.
These steps come from our top use case, Label images and train a YOLO model on your own machine, which also has a copy-paste agent prompt.
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About this repo
- Who it is forComputer vision engineers or hobbyists on macOS or Linux who need to label images, train a YOLO model, and check results on their own machine without a hosted service.
- Why it is hot125 stars, launched 76 days ago
- LanguagePython
- Sourcegithub.com/fengzhizi715/YoloWebAgentStarter · Back to Hot repos
Requirements
- macOS or Linux
- Python 3.11 or 3.12
- Node.js 20 or newer
- A set of images to label, or the built-in tiny demo generator
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Questions about Yolo Web Agent Starter
What is Yolo Web Agent Starter?
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. It is an open-source project on GitHub: https://github.com/fengzhizi715/YoloWebAgentStarter.
Who is Yolo Web Agent Starter for?
Computer vision engineers or hobbyists on macOS or Linux who need to label images, train a YOLO model, and check results on their own machine without a hosted service.
What can I do with Yolo Web Agent Starter?
5 use cases so far, for example: Label images and train a YOLO model on your own machine; Turn MP4, MOV, or AVI footage into a dataset of frames; Pre-label a dataset with a trained model, then review before training. Each has steps and a copy-paste agent prompt.
What do I need to run Yolo Web Agent Starter?
macOS or Linux; Python 3.11 or 3.12; Node.js 20 or newer; A set of images to label, or the built-in tiny demo generator.
What are alternatives to Yolo Web Agent Starter?
Other hot AI repos in Software and DevOps: Codex LB Enhanced, Reuse Before Build, Aibuildai LLM Posttrain Agent, FlowX, Codex Guard.
Is Yolo Web Agent Starter safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.