For agents — source-of-truth: This skill is authored in roboflow/computer-vision-skills and shipped with the Roboflow plugin. If your client has loaded the plugin (you'll see roboflow:<name> skills in your available skills list), use those local skills — they're read fresh from disk every session. The same content served as MCP resources at roboflow://skills/<name>/... is a fallback for clients without the plugin and may lag this repo. Don't call ReadMcpResourceTool for roboflow://skills/... URIs when a local roboflow:<name> skill is available.

Training & Evaluation on Roboflow

Training Flow

Upload/Annotate Images
  → Generate Dataset Version (preprocessing + augmentation + train/val/test split)
    → Pick Model Architecture + Size
      → Pick Checkpoint (COCO, Universe model, or previous version)
        → Train
          → Evaluate (auto-runs for paid users)

Version = frozen snapshot. Changes to the project after version creation do not affect it. Configure preprocessing (resize, contrast, etc.) and augmentation (flip, rotate, mosaic, etc.) during version generation.

Available Model Architectures

Object Detection

Instance Segmentation

Semantic Segmentation

Classification

Keypoint / Pose

Multimodal / VLM

Model Selection Decision Tree

Follow this flowchart to pick the right model. Start at Step 1.

1. Task type? OD / Instance Seg / Keypoint → Step 2. Classification / Semantic Seg / VLM → use specialized block directly.
2. Target classes in COCO 80? Yes → Step 3. No → Step 6.
3. Real-time? No (images/recorded video) → Step 4. Yes (live video) → Step 5.
4. Non-real-time, COCO — Pick model family by task, default Medium size (Small for constrained HW, XL for accuracy-first): OD → RF-DETR, Inst Seg → RF-DETR Seg, Keypoint → YOLO26 pose. Done.
5. Real-time, COCO — Same families, pick Nano–Small, prioritize latency. Done.
6. Non-COCO, which sub-task? OD → Step 7. Inst Seg → Step 8. Keypoint → Step 9.
7. OD, non-COCO — Check Rapid exclusions (see below). If excluded → Step 13. Otherwise → recommend Roboflow Rapid (default) or SAM3 zero-shot as secondary option → Step 10.
8. Inst Seg, non-COCO — SAM3 zero-shot (sam3/sam3_final, set class_names). Rapid does not support segmentation → Step 10.
9. Keypoint, non-COCO — Not real-time → YOLO26 pose Medium–XL. Real-time → YOLO26 pose Nano–Small. Done.
10. Real-time? No → try model (Step 11). Yes → warn about latency, try model (Step 12).
11. Non-real-time trial — User confirms works → Done. Poor results → Step 13.
12. Real-time trial — User confirms works → Done. Poor results → Step 13.
13. Universe Model Search — search community models on Roboflow Universe. Good match → Done. No match → Step 14.
14. Custom Training — Fine-tune RF-DETR on user data. Size by HW constraints. Done.

Model ID Reference

Use these exact model_id values. Do not guess — wrong IDs cause training failures.

Object Detection

Instance Segmentation

Keypoint / Pose

Classification

Semantic Segmentation

Multimodal / VLM

Other

COCO 80 Classes (RF-DETR coverage)

person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush.

Model Selection Quick Guide

RF-DETR NAS (Neural Architecture Search)

Instead of picking a single RF-DETR size manually, NAS trains many variants and reports the speed/accuracy frontier so you can pick the one that fits your hardware budget.

• What: A NAS run explores the RF-DETR architecture search space, then trains the surviving candidates and reports each one's mAP and measured latency on a target hardware (e.g., Jetson, T4 GPU). The output is a set of models on a Pareto frontier, plus an auto-selected "recommended" model chosen using Roboflow's current ranking heuristic to balance validation accuracy and measured latency on the target hardware.
• Tasks: Object Detection (rfdetr-nas) and Instance Segmentation (rfdetr-nas-seg).
• When to use: When you want the best speed/accuracy tradeoff for a specific deployment target and don't want to A/B-test sizes manually. Especially valuable for edge hardware where latency budgets are tight.
• Phases:
  1. Mining — explores architectures and builds a Pareto frontier (latency vs mAP). Live updates while running.
  2. Training — trains each frontier candidate end-to-end. Each becomes a regular model you can deploy.
• Plan gating: Requires the canTrainNas workspace feature flag. Self-serve plans (basic/starter/sandbox/research/trial) need to upgrade; enterprise/legacy plans need to contact sales.
• Start a run: Train page with ?engine=nas (UI: pick Neural Architecture Search as the training engine). Results land at /{workspace}/{project}/nas-runs/{versionId}.
• Deploy: Each NAS-produced model deploys like any other — pick one (typically the recommended) and use it as a normal Roboflow model. Inference type is rfdetr-nas / rfdetr-nas-seg, but it's served through the standard inference paths.
• References: RF-DETR paper (arxiv), ICLR 2026, What is NAS? (blog).

Roboflow Instant / Rapid

Roboflow Instant

• What: Few-shot model, trains in minutes, free
• Task: Object Detection only
• When to use: PoC, <1000 images, quick iteration
• Auto-trains when you approve a batch and no Instant model exists yet
• No preprocessing/augmentation -- uses images as-is
• Deploy: Available in Workflows like any trained model
• Manual trigger: Project > Models > Train Model > Roboflow Instant Model

Roboflow Rapid

• What: Interactive annotation-and-training workflow — SAM3 pre-annotates a small image set, user reviews/corrects, a fast custom OD model trains automatically. Model keeps improving as it captures more production data.
• Task: Object Detection only, non-COCO classes
• When to use: Default path for non-COCO object detection when exclusions don't apply

Do NOT use Rapid when:

When Rapid is excluded → recommend custom training with RF-DETR fine-tuning.

Checkpoint Training

Training Controls

• Cancel Training: Stops job, no weights saved. Refund if early in training.
• Early Stopping: Stops job, saves weights. Use when graphs show convergence with many epochs remaining. Charges for used credits.
• NAS Training: Shows paired charts (mining progress + Pareto curve, then per-model training curves). May auto-stop on convergence. See RF-DETR NAS section below.

Post-Training Metrics

Metrics vary by project type:

Model Evaluation (Paid Plans)

Auto-runs after training. Access: Models > click model version > View Evaluation.

Viewing & Comparing Models

• Models page: Project sidebar > Models. Shows all Instant + fine-tuned models with metrics, architecture, license, dataset version used.
• Universe tab: Starred Universe models available for transfer learning.
• Visualize: Quick test on test-set images, uploaded images, or webcam. Works for OD, segmentation, classification, keypoint. Not supported for multimodal.

MCP Tools Reference

Related Pages

• roboflow://skills/roboflow-model-improvement/SKILL — diagnostic decision tree, confusion matrix guide, per-class metrics, architecture switching, iterative improvement checklist