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* Add Huawei Ascend NPU deploy through PaddleLite CANN * Add NNAdapter interface for paddlelite * Modify Huawei Ascend Cmake * Update way for compiling Huawei Ascend NPU deployment * remove UseLiteBackend in UseCANN * Support compile python whlee * Change names of nnadapter API * Add nnadapter pybind and remove useless API * Support Python deployment on Huawei Ascend NPU * Add models suppor for ascend * Add PPOCR rec reszie for ascend * fix conflict for ascend * Rename CANN to Ascend * Rename CANN to Ascend * Improve ascend * fix ascend bug * improve ascend docs * improve ascend docs * improve ascend docs * Improve Ascend * Improve Ascend * Move ascend python demo * Imporve ascend * Improve ascend * Improve ascend * Improve ascend * Improve ascend * Imporve ascend * Imporve ascend * Improve ascend * acc eval script * acc eval * remove acc_eval from branch huawei * Add detection and segmentation examples for Ascend deployment * Add detection and segmentation examples for Ascend deployment * Add PPOCR example for ascend deploy * Imporve paddle lite compiliation * Add FlyCV doc * Add FlyCV doc * Add FlyCV doc * Imporve Ascend docs * Imporve Ascend docs * Improve PPOCR example
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YOLOv7 Prepare the model for Deployment
-
YOLOv7 deployment is based on YOLOv7 branching code, and COCO Pre-Trained Models.
- （1）The *.pt provided by the Official Library can be deployed after the [export ONNX model](#export ONNX model) operation; *.trt and *.pose models do not support deployment.
- （2）As for YOLOv7 model trained on customized data, please follow the operations guidelines in Export ONNX model and then refer to Detailed Deployment Tutorials to complete the deployment.
Export ONNX Model
# Download yolov7 model file
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt
# Export onnx file (Tips: in accordance with YOLOv7 release v0.1 code)
python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
# If your code supports exporting ONNX files with NMS, please use the following command to export ONNX files, then refer to the example of `yolov7end2end_ort` or `yolov7end2end_ort`
python models/export.py --grid --dynamic --end2end --weights PATH/TO/yolov7.pt
Download the pre-trained ONNX model
To facilitate testing for developers, we provide below the models exported by YOLOv7, which developers can download and use directly. (The accuracy of the models in the table is sourced from the official library)
| Model | Size | Accuracy |
|---|---|---|
| YOLOv7 | 141MB | 51.4% |
| YOLOv7x | 273MB | 53.1% |
| YOLOv7-w6 | 269MB | 54.9% |
| YOLOv7-e6 | 372MB | 56.0% |
| YOLOv7-d6 | 511MB | 56.6% |
| YOLOv7-e6e | 579MB | 56.8% |
Detailed Deployment Tutorials
Version
- This tutorial and related code are written based on YOLOv7 0.1