Inference, Results API & tracking

Fastest route: test and deploy in Platform

Every Platform model has a Predict tab: upload an image, choose an example, or capture a webcam frame; inference runs automatically and shows the task overlay, summary, raw JSON, and timing. Adjust conf, iou, and imgsz with the same meanings used below.

For production, open Deploy, choose a nearby region, and wait for the dedicated endpoint to become Ready. Its card provides health, metrics, logs, browser prediction, and ready-to-use Python/JavaScript/cURL examples. Call /predict with a bearer API key:

import requests

with open("image.jpg", "rb") as image_file:
    response = requests.post(
        "https://YOUR_DEPLOYMENT_URL.run.app/predict",
        headers={"Authorization": "Bearer YOUR_API_KEY"},
        files={"file": image_file},
        data={"conf": 0.25, "iou": 0.7, "imgsz": 640},
    )
response.raise_for_status()
print(response.json())

Dedicated endpoints use scale-to-zero, so expect a cold start after idle periods. See Platform Inference and Dedicated Endpoints.

Quickstart

from ultralytics import YOLO

model = YOLO("yolo26n.pt")  # or your runs/detect/train/weights/best.pt
results = model("image.jpg")  # list[Results], one per image
results[0].show()

yolo predict model=yolo26n.pt source="image.jpg" save=True

The one rule for video/streams: stream=True. The default builds a list of ALL results in RAM — OOM on long videos. stream=True returns a generator:

for r in model("video.mp4", stream=True):
    ...

Sources

Accepted directly: image/video path, directory, glob, URL, webcam index (0), RTSP/RTMP/HTTP streams, YouTube URL (needs pytubefix), PIL image, numpy array (assumed BGR), torch tensor, or a list of these. vid_stride=N processes every Nth frame.

Arguments that matter

Arg	Default	Notes
conf	0.25	lower → more recall + more false positives
iou	0.7	NMS threshold; ignored by default YOLO26/YOLOv10 end-to-end inference
end2end	None	native .pt: set False before first prediction to enable NMS/iou; reload if already fused. Set during export for artifacts
imgsz	model	inherited from the checkpoint; set explicitly when a different inference shape is required
classes	None	keep only these ids, e.g. classes=[0]
max_det	300	raise for dense scenes
quantize	None	16 selects FP16 compute for PyTorch/TorchScript; artifact/runtime precision otherwise applies; see caveat below
batch	1	>1 speeds up folders/videos with stream=True
retina_masks	False	full-resolution masks (slower, crisper)
augment	False	test-time augmentation: +accuracy, ~3× slower
verbose	True	False in loops to silence per-frame logs

Saving/drawing: save, save_txt, save_conf, save_crop, show, line_width → runs/<task>/predict*/.

quantize=16 also rounds OpenVINO and Triton inputs to FP16 and returns Triton outputs as FP16. It replaces the deprecated half argument.

Results API

Each Results has the task's payload — .boxes, .masks, .keypoints, .probs (classify), .obb, .semantic_mask, .depth — plus .names (id→name), .orig_img (BGR), .speed, and methods .plot(), .show(), .save(), .save_txt(), .save_crop(), .summary(), and exports to_df() / to_csv() / to_json() (these three only).

r = results[0]
r.boxes.xyxy / .xywh / .xyxyn / .xywhn   # (N,4) boxes, pixel / normalized
r.boxes.conf, r.boxes.cls, r.boxes.id    # confidence, class ids, track ids (track mode)
r.masks.xy                                # list of (K,2) pixel polygons per instance
r.keypoints.xy, r.keypoints.conf          # (N,K,2), (N,K)
r.probs.top1, r.probs.top1conf            # classify; r.names[r.probs.top1] → label
r.obb.xyxyxyxy, r.obb.xywhr               # oriented boxes

from collections import Counter
counts = Counter(r.names[int(c)] for c in r.boxes.cls)   # count per class

model.embed("image.jpg") returns feature vectors (similarity search, clustering).

Tracking (persistent IDs across frames)

for r in model.track("video.mp4", stream=True):
    ids = r.boxes.id  # tensor of track ids, or None

# Frame-by-frame loop with your own capture: persist=True is REQUIRED
r = model.track(frame, persist=True)[0]  # else the tracker resets every frame

• Six trackers, selected with tracker=: tracktrack.yaml (default), botsort.yaml, bytetrack.yaml, ocsort.yaml, deepocsort.yaml, fasttrack.yaml. ByteTrack is lightest; BoT-SORT/TrackTrack/DeepOCSORT support ReID (with_reid: True) and camera-motion compensation (gmc_method).
• Custom behavior: copy the YAML from ultralytics/cfg/trackers/, tweak (track_buffer = frames a lost track survives, track_high_thresh), pass your path.
• Tracking accepts all predict args. Always guard r.boxes.id is not None.

Annotated-video writing pattern

import cv2

cap = cv2.VideoCapture("in.mp4")
w, h, fps = (int(cap.get(p)) for p in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter("out.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
for r in model.track("in.mp4", stream=True):
    out.write(r.plot())  # .plot() returns annotated BGR frame
cap.release()
out.release()

Performance checklist

1. For PyTorch/TorchScript on a supported GPU, benchmark quantize=16 against FP32.
2. Export to the target-native backend (TensorRT/OpenVINO/CoreML) and benchmark it (see yolo-export; exports load straight back into YOLO()).
3. Use a smaller model or imgsz.
4. Set batch>1 for offline folders; use vid_stride when every frame isn't needed.
5. Set verbose=False; skip .plot() when only coordinates are needed.
6. Use one YOLO() instance per thread — never share across threads.

Troubleshooting

Symptom	Cause / fix
No detections on visible objects	conf too high; wrong weights; imgsz far from training size
Boxes offset	you pre-resized manually — pass the raw image, preprocessing is internal
Wrong colors in saved crops	Results arrays are BGR; cv2.cvtColor(..., COLOR_BGR2RGB) for PIL/matplotlib
RAM climbs on video	missing stream=True
boxes.id is None crash	guard for None; persist=True in manual loops
Duplicate boxes	native .pt: reload, then use end2end=False and lower iou; exports: re-export with end2end=False; add agnostic_nms=True for cross-class duplicates
Caps at 300 objects	raise max_det
Slow first inference	warmup — benchmark from the second call

Related pages

• solutions.md (this folder) — read BEFORE hand-rolling counting, heatmaps, speed estimation, zone logic, queues, parking, workout counting, or privacy blurring: a prebuilt Solution almost certainly exists.

If the installed version rejects an argument, trust the error text and yolo cfg over this file.