Not runnable todayClassificationVerified 2026-11-05
Ultralytics YOLO11 classify
Ultralytics/YOLO11 (yolo11n-cls.pt)
Ultralytics YOLO11 classify is a image classification model (AGPL-3.0). Not runnable on Mixpeek today.
Something blocks it today, such as the license, remote code or a GPU-only runtime. The reason is below.
No demo for Ultralytics YOLO11 classify yet
A video, image, slideshow or playground goes here once we record one on our own data.
What it is good and bad at
- Best at
- Fast classify for the fixed ImageNet label set; n size runs in real time on CPU.
- Worst at
- Closed label set (needs fine-tuning for new classes); license blocks closed commercial use; small or crowded objects at 640 input.
- Task
- Classification
- Input
- Image
- Output
- Labels, Scores
- Use cases
- Classification, Tagging
Templates that use it
No template uses this model yet. Browse templates.
Input and output
Input
- image
- resolution
- 224x224
- color
- RGB
- prompts
- none (fixed classes)
Output
- labels
- ImageNet-1k (1000 classes), top-5
How Mixpeek wires it
- Extractors
- None today
- Feature URIs
- None today
- Vector indexes
- None
- Retriever stages
- None
- Combines with
- frame labels -> payload field -> attribute filter
How it runs
- Status
- Not runnable today Blocked by AGPL-3.0 and pickle .pt weights. Would run as a custom extractor plugin that exports the checkpoint to ONNX once (ultralytics export format=onnx) and serves it with onnxruntime on CPU, emitting boxes, labels and crops.
- Serve apps
- None
- Runtime
- ultralytics YOLO (or onnxruntime after export)
- Weights
- Pickle, no remote code
- Parameters
- 1.6 M
- Size on disk
- Not recorded
- Preprocessing
- Resize and center-crop to 224.
- Postprocessing
- Softmax over 1000 classes; top-k.
- Serving
- both
- Hardware
- Runs on CPU (slower without a GPU)
Scale to zero
- Minimum replicas
- 0
- Maximum replicas
- 1
- Idle timeout
- 600 s
- Memory
- 1 GB
- CPUs
- 1
- GPUs
- 0
- Cold start, node warm
- Not recorded
- Cold start, new node
- Not recorded
Measured cost
Unmeasured
No committed measurement; plan row 3 measures it in the standalone image.
Our evaluations
Unmeasured
We have not evaluated this model on our own data yet. Publisher results are in the sources below.
Questions
- What is Ultralytics YOLO11 classify best at?
- Fast classify for the fixed ImageNet label set; n size runs in real time on CPU.
- What are the limits of Ultralytics YOLO11 classify?
- Closed label set (needs fine-tuning for new classes); license blocks closed commercial use; small or crowded objects at 640 input.
- Can Ultralytics YOLO11 classify be used commercially?
- No. AGPL-3.0: offering it over a network obliges publishing the whole application's source under AGPL; closed commercial use needs an Ultralytics Enterprise license.
- How does Ultralytics YOLO11 classify run on Mixpeek?
- Blocked by AGPL-3.0 and pickle .pt weights. Would run as a custom extractor plugin that exports the checkpoint to ONNX once (ultralytics export format=onnx) and serves it with onnxruntime on CPU, emitting boxes, labels and crops.