roboflow-training-and-evaluation

name	roboflow-training-and-evaluation
description	Use when training Roboflow models, improving accuracy, or setting up a production feedback loop — covers architecture selection, model IDs, checkpoints, evaluation metrics, the iterative improvement playbook, and active learning via the Dataset Upload workflow block.

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

Architecture	Sizes	Default Resolution	Notes
RF-DETR	Pico, Nano, Small, Base, Medium, Large, XL, 2XL	384-880 (varies by size)	Best accuracy, recommended default
Roboflow 3.0	Fast, Accurate, Medium, Large, XL	640x640	YOLOv8-based. Medium+ require paid plan
YOLO26	n/s/m/l/x	640x640	Also supports seg + pose
YOLOv12	n/s/m/l/x	640x640	OD only
YOLOv11	n/s/m/l/x	640x640	Also supports seg + pose
YOLOv8	n/s/m/l/x	640x640	Also supports seg + pose
YOLO-NAS	Small, Medium	640x640
YOLOLite CPU	n/s/m/l/x	640x640	Edge-optimized, beta
YOLOLite GPU	n/s/m/l/x	640x640	Edge-optimized, beta
Roboflow Instant	single	N/A (no resize)	Few-shot, free, OD only

Instance Segmentation

Architecture	Sizes	Default Resolution
RF-DETR Seg	Nano, Small, Medium, Large, XL, 2XL	312-768 (varies)
Roboflow 3.0 Seg	Fast, Accurate, Medium, Large, XL	640x640
YOLO-seg	v8/v11/v26 (n/s/m/l/x each)	640x640
SAM 3 (Segment Anything 3)	Large	1008x1008

Semantic Segmentation

Architecture	Sizes	Default Resolution
DeepLabV3+	Base	>=512x512

Classification

Architecture	Sizes	Default Resolution
ViT	Base	224x224
ResNet	18/34/50/101	224x224
DINOv3	Base, Small	224x224

Keypoint / Pose

Architecture	Sizes	Default Resolution
YOLO-pose	v8/v11/v26 (n/s/m/l/x each)	640x640

Multimodal / VLM

Architecture	Sizes	Default Resolution
Qwen3.5	0.8B, 2B	448x448
Qwen3 VL	2B	448x448
SmolVLM	256M, 2B	384x384
Florence 2	Base, Large	768x768
PaliGemma 2	3B	448x448
Qwen2.5 VL	7B	448x448

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

Family	model_id values
RF-DETR (recommended)	rfdetr-pico, rfdetr-nano, rfdetr-small, rfdetr-base, rfdetr-medium, rfdetr-large, rfdetr-xlarge, rfdetr-2xlarge
YOLO26	yolo26n, yolo26s, yolo26m, yolo26l, yolo26x
YOLOv12	yolov12n, yolov12s, yolov12m, yolov12l, yolov12x
YOLOv11	yolov11n, yolov11s, yolov11m, yolov11l, yolov11x
YOLOv8	yolov8n, yolov8s, yolov8m, yolov8l, yolov8x
YOLO-NAS	yolo_nas_s, yolo_nas_m, yolo_nas_l
YOLOLite CPU	yololite-edge-n, yololite-edge-s, yololite-edge-m, yololite-edge-l, yololite-edge-xl
YOLOLite GPU	yololite-n, yololite-s, yololite-m, yololite-l, yololite-xl

Instance Segmentation

Family	model_id values
RF-DETR Seg (recommended)	rfdetr-seg-nano, rfdetr-seg-small, rfdetr-seg-medium, rfdetr-seg-large, rfdetr-seg-xlarge, rfdetr-seg-2xlarge
YOLO26 Seg	yolo26n-seg, yolo26s-seg, yolo26m-seg, yolo26l-seg, yolo26x-seg
YOLOv11 Seg	yolov11n-seg, yolov11s-seg, yolov11m-seg, yolov11l-seg, yolov11x-seg
YOLOv8 Seg	yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg
SAM3	sam3-large

Keypoint / Pose

Family	model_id values
YOLO26 Pose	yolo26n-pose, yolo26s-pose, yolo26m-pose, yolo26l-pose, yolo26x-pose
YOLOv11 Pose	yolov11n-pose, yolov11s-pose, yolov11m-pose, yolov11l-pose, yolov11x-pose
YOLOv8 Pose	yolov8n-pose, yolov8s-pose, yolov8m-pose, yolov8l-pose, yolov8x-pose

Classification

Family	model_id values
ViT	vit-base-patch16-224-in21k
ResNet	resnet18, resnet34, resnet50, resnet101
DINOv3	vit_base_patch16_dinov3.lvd1689m, vit_small_patch16_dinov3.lvd1689m

Semantic Segmentation

Family	model_id values
DeepLabV3+	deeplabv3plus

Multimodal / VLM

Family	model_id values
Qwen3.5 VL	qwen3_5-2b-peft, qwen3_5-0.8b-peft
Qwen3 VL	qwen3vl-2b-instruct, qwen3vl-2b-instruct-peft
SmolVLM	smolvlm2-peft, smolvlm-256m-peft
Florence 2	florence-2-base, florence-2-large, florence-2-base-peft, florence-2-large-peft
PaliGemma 2	paligemma2-3b-pt-224, paligemma2-3b-pt-448, paligemma2-3b-pt-896, paligemma2-3b-pt-224-peft
Qwen2.5 VL	qwen25-vl-7b, qwen25-vl-7b-peft

Other

Model	model_id	Notes
SAM3 (zero-shot, workflows)	sam3/sam3_final	Always set class_names; no other props unless user asks
Custom / workspace	workspace/model or dataset/version	e.g., construction-safety/2

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

Goal	Recommended
Best accuracy, object detection	RF-DETR (Large or XL)
Fast inference, object detection	RF-DETR Nano or YOLOv11n
Best speed/accuracy tradeoff for specific hardware	RF-DETR NAS (see section below)
Best accuracy, instance segmentation	RF-DETR Seg
Quick proof-of-concept (<1000 images)	Roboflow Instant
Classification	ViT or DINOv3
Multimodal / text prompts	Qwen3.5 or SmolVLM

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:

Exclusion	Why
OCR / text detection (characters, serial numbers, labels, receipts, license plates)	SAM3 cannot reliably segment individual characters
Blueprints, floor plans, schematics, technical drawings	Abstract symbols and line-based elements not handled by SAM3 text prompting
More than 5 target classes	SAM3 text prompting accuracy degrades significantly with many classes
Fine-grained visual distinctions (correct vs incorrect orientation, pass/fail, subtle defects)	SAM3 cannot differentiate nearly identical objects; fine-tuned model needed
High-precision measurement / metrology (distances, dimensions, tolerances)	SAM3 auto-labeling annotation precision insufficient for calibrated measurement

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

Checkpoint Training

Option	When to use
Public Checkpoint (COCO)	First model version, default recommended
Universe Checkpoint	Star a Universe project first, then it appears as checkpoint option. Good for domain-specific transfer learning
Previous Version	Already have a good model, want to improve with more data (all types except classification and SAM3)
Random Initialization	Advanced users only, usually worse results

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:

Project Type	Metrics Shown
Object Detection	mAP@50, Precision, Recall, F1
Classification	Accuracy
Instance Segmentation / Keypoint	mAP@50, Precision, Recall
Semantic Segmentation	mIoU
Multimodal	Perplexity

Model Evaluation (Paid Plans)

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

Feature	What it shows
Production Metrics Explorer	Precision/Recall/F1 at all confidence thresholds; recommends optimal confidence
Model Improvement Recommendations	Actionable suggestions (false negatives, false positives, confused classes, insufficient data)
Performance by Class	Correct predictions, misclassifications, false negatives, false positives per class; filterable
Confusion Matrix	Ground truth vs predictions grid; click cells to see specific images; adjustable confidence threshold
Vector Explorer	Interactive embedding clusters showing where model succeeds/fails

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

Action	Tool
Generate version	versions_generate
Start training	trainings_create
Check training status	models_get_training_status
Get model info	models_get
List models	models_list

Related Pages

• roboflow://skills/training-and-evaluation/improvement-playbook — diagnostic decision tree, confusion matrix guide, per-class metrics, architecture switching, iterative improvement checklist
• roboflow://skills/training-and-evaluation/active-learning — production feedback loop: Dataset Upload workflow block, confidence-based sampling, review and retrain cycle