| Large Models <sup>[3](https://github.com/meituan/YOLOv6/tree/main#benchmark)</sup>| 1280x1280 |
This table provides a quick reference to understand the different input dimensions commonly used in
various YOLO models inputs. These are standard input shapes. Make sure you use input size that you
trained model with, if it is differed from from the size mentioned in the table.
The next critical element in the process involves understanding the specifics of image pre-processing
for YOLO detectors. While the fundamental pre-processing approach remains consistent across the YOLO
family, there are subtle yet crucial differences that must be accounted for to avoid any degradation
in performance. Key among these are the `resize type` and the `padding value` applied post-resize.
For instance, the [YOLOX model](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/data/data_augment.py#L142)
utilizes a `LetterBox` resize method and a padding value of `114.0`. It is imperative to ensure that
these parameters, along with the normalization constants, are appropriately matched to the model being
exported.
Regarding the model's output, it typically takes the form of a tensor with dimensions [BxNxC+5] or
[BxNxC+4], where 'B' represents the batch size, 'N' denotes the number of anchors, and 'C' signifies
the number of classes (for instance, 80 classes if the model is trained on the COCO dataset).
The additional 5 in the former tensor structure corresponds to the objectness score (obj), confidence
score (conf), and the bounding box coordinates (cx, cy, w, h). Notably, the YOLOv8 model's output
is shaped as [BxNxC+4], where there is no explicit objectness score, and the object score is directly
inferred from the class score. For the YOLOX model, specifically, it is also necessary to incorporate
anchor points to rescale predictions back to the image domain. This step will be integrated into
the ONNX graph, a process that we will detail further in the subsequent sections.
### PyTorch Model Export
Now that we know know the parameters of the pre-precessing we can go on and export the model from
Pytorch to ONNX graph. Since in this tutorial we are using YOLOX as our sample model, lets use its
export for demonstration purposes (the process is identical for the rest of the YOLO detectors except `YOLOv10` model, see details on how to export it later in the post).
To exporting YOLOX we can just use [export script](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/tools/export_onnx.py). Particularly we need following commands:
**NOTE:** Here `--decode_in_inference` is to include anchor box creation in the ONNX graph itself.
It sets [this value](https://github.com/Megvii-BaseDetection/YOLOX/blob/ac58e0a5e68e57454b7b9ac822aced493b553c53/yolox/models/yolo_head.py#L210C16-L210C39)
to `True`, which subsequently includes anchor generation function.
Below we demonstrated the minimal version of the export script (which could be used for models other
than YOLOX) in case it is needed. However, usually each YOLO repository has predefined export script.
In oder to run YOLOv10 one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for on how to cut off the postprocessing, there is this [forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. The forked branch cuts of the postprocessing by [returning output](https://github.com/Abdurrahheem/yolov10/blob/4fdaafd912c8891642bfbe85751ea66ec20f05ad/ultralytics/nn/modules/head.py#L522) of the model before postprocessing procedure itself. To convert torch model to ONNX follow this proceduce.
- --yolo: YOLO model version (e.g., YOLOv3, YOLOv4, etc.).
- --padvalue: Padding value used in pre-processing (e.g., 114.0).
- --paddingmode: Method for handling image resizing and padding. Options: 0 (resize without extra processing), 1 (crop after resize), 2 (resize with aspect ratio preservation).
- --backend: Selection of computation backend (0 for automatic, 1 for Halide, 2 for OpenVINO, etc.).
- --target: Selection of target computation device (0 for CPU, 1 for OpenCL, etc.).
- --device: Camera device number (0 for default camera). If `--input` is not provided camera with index 0 will used by default.
This will run `YOLOv10` detector on first camera found on your system. If you want to run it on a image/video file, you can use `--input` option to specify the path to the file.
