How to Train YOLO on a Custom Dataset Without Code

To train YOLO on your own images in TensorTurn, upload a labeled detection dataset (YOLO, COCO or VOC format, auto-detected), tell the chat which objects to detect, and it runs Ultralytics YOLO training on an isolated cloud GPU, self-healing any errors, then deploys the weights as a /predict API. With a few hundred well-labeled boxes per class you can reach genuinely useful detection accuracy; small, crowded or fine-grained objects need more data and land lower.

Classification vs. detection

YOLO is for object detection: it finds where objects are and draws bounding boxes around them, and it can detect several objects in one image. That is different from image classification, which labels the whole image as one class. Choose the YOLO workflow when you need location and counts — for example counting products on a shelf, spotting defects on a part, or detecting people and vehicles in a frame.

What data you need

• Images plus bounding-box annotations in YOLO txt, COCO json or VOC xml — TensorTurn auto-detects the format.
• Roughly 150–300+ labeled boxes per class as a minimum; small or highly varied objects need more.
• Consistent class definitions and tight, accurate boxes — loose or missing labels hurt more than missing images.
• Scenes that match production: the same backgrounds, distances and lighting you will run inference on. Upload as zip, rar or 7z, or from a URL.

How to train it on TensorTurn (no code)

• Upload your annotated dataset (format auto-detected).
• Run the image health check for duplicates, blur, corrupt files, odd resolutions and cross-split leakage, and fix what it flags.
• In chat: 'Train a YOLO detector for the classes helmet, vest and person.'
• TensorTurn runs Ultralytics YOLO training on a T4, L4 or A10G GPU and self-heals failing cells (up to 100 attempts).
• Review mAP50, mAP50-95, per-class precision and recall, and sample predictions with boxes drawn.
• Deploy a /predict endpoint serving the YOLO weights, with an inference playground.

Use your own GPU for longer runs

Detection training can be heavy, so you can connect your own GPU with a one-line command on Windows, macOS or Linux. The agent is outbound-HTTPS-only, so it works behind any NAT, firewall or VPN without opening ports, the token is stored as a SHA-256 hash, and you get live telemetry (CPU, RAM, GPU, VRAM, temperature, power). You can even combine several of your own machines into one run. Note the honest limit: this is your own single-tenant pool doing embarrassingly-parallel or periodic weight-averaging work, not tensor or pipeline parallelism across the WAN — it will not make a model too big for one machine suddenly fit.

How accurate can it be?

Detection accuracy is dominated by label quality and object size. For clear, large, well-labeled objects, mAP50 of 0.85–0.95 is achievable. For small, occluded or crowded objects, expect 0.40–0.70, and too few boxes per class produces weak, unreliable detectors. If results disappoint, the fix is almost always more and better-labeled examples of the hard cases, not a bigger model.

Deploy as an API

Save the best weights and publish an authenticated /predict endpoint on Modal that scales to zero when idle, secured with a SHA-256-hashed Bearer key and per-call logging. The playground gives cURL, JavaScript, Python and Rust snippets so you can send an image and receive boxes, classes and confidences.