# Yolo Inference > Use when testing, running, or deploying Ultralytics YOLO inference in Platform or code on images, video, webcams, or streams — Platform Predict and dedicated endpoints, model.predict()/yolo predict, Results API boxes/masks/keypoints, persistent tracking, annotated video, and Solutions such as counting, heatmaps, speed, queues, and parking. For optimized runtime exports, see yolo-export. - Skill: `ultralytics/yolo-inference` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add ultralytics/yolo-inference` - Raw SKILL.md: https://api.skillmd.com/api/skills/ultralytics/yolo-inference/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: ultralytics (https://skillmd.com/u/ultralytics) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ultralytics/yolo-inference --- # 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: ```python 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](https://docs.ultralytics.com/platform/deploy/inference) and [Dedicated Endpoints](https://docs.ultralytics.com/platform/deploy/endpoints). ## Quickstart ```python 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() ``` ```bash 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: ```python 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//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). ```python 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) ```python 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 ```python 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.