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General object detection model
License
GNU Affero General Public License v3.0
Commercial use
Downloads
1282
Tasks
Object Detection
Model Types
ONNX
Model Variants
Name
Version
Available For
Created At
Deploy
RVC2, RVC4
About 1 year ago
Model Details
Model Description
YOLOv10n model is a convolutional neural network designed for object detection. It introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive examples demonstrate its superior accuracy-latency trade-offs across multiple model scales. It was trained on the COCO data set. It is capable of detecting objects of 80 classes.
Developed by: Tsinghua University
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
is a large-scale object detection, segmentation, and captioning dataset.
Testing Details
Metrics
Results of the mAP and speed are evaluated on COCO dataset with the input resolution of 640×640. Results are taken from .
Model
AP
38.5
Technical Specifications
Input/Output Details
Input:
Name: image
Info: NCHW BGR un-normalized image
Outputs:
Name: multiple (see NN archive)
Info: Unprocessed outputs of a multitude of detections
Model Architecture
Backbone: an enhanced version of CSPNet (Cross Stage Partial Network)
Neck: includes PAN (Path Aggregation Network)
Head: One-to-One head
Please consult the for more information on model architecture.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 version of the model was quantized using a custom dataset.
This was created by taking a full 128-image dataset.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
The model is automatically parsed by DAI and it outputs the message (bounding boxes, labels, and scores of the detected objects).
Get model output(s):
while pipeline.isRuning():
nn_output: dai.ImgDetections = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
