How to use Yolo v5 without pytorch

[VYRION-Ai]

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Question

Hi I am running YOLO v5 on FPGA, but I face a problem on installing torch .
Can I run Yolo v5 without torch.

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[glenn-jocher]

👋 Hello! Thanks for asking about Export Formats. YOLOv5 🚀 offers export to almost all of the common export formats. See our TFLite, ONNX, CoreML, TensorRT Export Tutorial for full details.

Formats

YOLOv5 inference is officially supported in 11 formats:

💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks.
💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See GPU Benchmarks.

Format	export.py --include	Model
PyTorch	-	yolov5s.pt
TorchScript	torchscript	yolov5s.torchscript
ONNX	onnx	yolov5s.onnx
OpenVINO	openvino	yolov5s_openvino_model/
TensorRT	engine	yolov5s.engine
CoreML	coreml	yolov5s.mlmodel
TensorFlow SavedModel	saved_model	yolov5s_saved_model/
TensorFlow GraphDef	pb	yolov5s.pb
TensorFlow Lite	tflite	yolov5s.tflite
TensorFlow Edge TPU	edgetpu	yolov5s_edgetpu.tflite
TensorFlow.js	tfjs	yolov5s_web_model/
PaddlePaddle	paddle	yolov5s_paddle_model/

Benchmarks

Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook . To reproduce:

python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0

Colab Pro V100 GPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)

Benchmarks complete (458.07s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623                10.19
1             TorchScript        0.4623                 6.85
2                    ONNX        0.4623                14.63
3                OpenVINO           NaN                  NaN
4                TensorRT        0.4617                 1.89
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623                21.28
7     TensorFlow GraphDef        0.4623                21.22
8         TensorFlow Lite           NaN                  NaN
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Colab Pro CPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)

Benchmarks complete (241.20s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623               127.61
1             TorchScript        0.4623               131.23
2                    ONNX        0.4623                69.34
3                OpenVINO        0.4623                66.52
4                TensorRT           NaN                  NaN
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623               123.79
7     TensorFlow GraphDef        0.4623               121.57
8         TensorFlow Lite        0.4623               316.61
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Export a Trained YOLOv5 Model

This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. yolov5s.pt is the 'small' model, the second smallest model available. Other options are yolov5n.pt, yolov5m.pt, yolov5l.pt and yolov5x.pt, along with their P6 counterparts i.e. yolov5s6.pt or you own custom training checkpoint i.e. runs/exp/weights/best.pt. For details on all available models please see our README table.

python export.py --weights yolov5s.pt --include torchscript onnx

💡 ProTip: Add --half to export models at FP16 half precision for smaller file sizes

Output:

export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.2-104-ge3e5122 Python-3.7.13 torch-1.12.1+cu113 CPU

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 274MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients

PyTorch: starting from yolov5s.pt with output shape (1, 25200, 85) (14.1 MB)

TorchScript: starting export with torch 1.12.1+cu113...
TorchScript: export success ✅ 1.7s, saved as yolov5s.torchscript (28.1 MB)

ONNX: starting export with onnx 1.12.0...
ONNX: export success ✅ 2.3s, saved as yolov5s.onnx (28.0 MB)

Export complete (5.5s)
Results saved to /content/yolov5
Detect:          python detect.py --weights yolov5s.onnx 
Validate:        python val.py --weights yolov5s.onnx 
PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Visualize:       https://netron.app/

The 3 exported models will be saved alongside the original PyTorch model:

Netron Viewer is recommended for visualizing exported models:

Exported Model Usage Examples

detect.py runs inference on exported models:

python detect.py --weights yolov5s.pt                 # PyTorch
                           yolov5s.torchscript        # TorchScript
                           yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                           yolov5s_openvino_model     # OpenVINO
                           yolov5s.engine             # TensorRT
                           yolov5s.mlmodel            # CoreML (macOS only)
                           yolov5s_saved_model        # TensorFlow SavedModel
                           yolov5s.pb                 # TensorFlow GraphDef
                           yolov5s.tflite             # TensorFlow Lite
                           yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                           yolov5s_paddle_model       # PaddlePaddle

val.py runs validation on exported models:

python val.py --weights yolov5s.pt                 # PyTorch
                        yolov5s.torchscript        # TorchScript
                        yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                        yolov5s_openvino_model     # OpenVINO
                        yolov5s.engine             # TensorRT
                        yolov5s.mlmodel            # CoreML (macOS Only)
                        yolov5s_saved_model        # TensorFlow SavedModel
                        yolov5s.pb                 # TensorFlow GraphDef
                        yolov5s.tflite             # TensorFlow Lite
                        yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                        yolov5s_paddle_model       # PaddlePaddle

Use PyTorch Hub with exported YOLOv5 models:

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
                                                       'yolov5s.torchscript ')       # TorchScript
                                                       'yolov5s.onnx')               # ONNX Runtime
                                                       'yolov5s_openvino_model')     # OpenVINO
                                                       'yolov5s.engine')             # TensorRT
                                                       'yolov5s.mlmodel')            # CoreML (macOS Only)
                                                       'yolov5s_saved_model')        # TensorFlow SavedModel
                                                       'yolov5s.pb')                 # TensorFlow GraphDef
                                                       'yolov5s.tflite')             # TensorFlow Lite
                                                       'yolov5s_edgetpu.tflite')     # TensorFlow Edge TPU
                                                       'yolov5s_paddle_model')       # PaddlePaddle

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

OpenCV DNN inference

OpenCV inference with ONNX models:

python export.py --weights yolov5s.pt --include onnx

python detect.py --weights yolov5s.onnx --dnn  # detect
python val.py --weights yolov5s.onnx --dnn  # validate

C++ Inference

YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:

• https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp
• https://github.com/doleron/yolov5-opencv-cpp-python

YOLOv5 OpenVINO C++ inference examples:

• https://github.com/dacquaviva/yolov5-openvino-cpp-python
• https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp

Good luck 🍀 and let us know if you have any other questions!

[pythonscriptsPro]

Could you please share how did you run it on fpga

[VYRION-Ai]

@pythonscriptsPro try to use onnx environment

[pythonscriptsPro]

@pythonscriptsPro try to use onnx environment
Thanks for your reply,

But the onnx has to be converted to a fpga format, is that right?

[VYRION-Ai]

@pythonscriptsPro no , there is fpga kit that run ubuntu os, you will work like raspberry or jetson nano

[pythonscriptsPro]

Ok, many thanks 😊

[glenn-jocher]

@pythonscriptsPro you're welcome! If you have any more questions, feel free to ask. Good luck with your YOLOv5 implementation on the FPGA kit! 😊

[pythonscriptsPro]

@glenn-jocher , thanks a lot for your reply,

I deployed YOLOv5 on a KRIA260 device using DPU Overlay PYNQ. Some layers unsupported by KRIA were replaced, affecting performance. The quantization process led to a slight quality degradation, but the performance is comparable to the original code, achieving around 50 fps.

[glenn-jocher]

@pythonscriptsPro that's fantastic to hear! Kudos to your successful deployment on the KRIA260 device using DPU Overlay PYNQ! Achieving a comparable performance of around 50 fps, despite the slight quality degradation, speaks volumes about the capability of YOLOv5. The effort you've put into overcoming the challenges is commendable. Keep up the great work and feel free to reach out if you have any further questions or need assistance. 😊

[pythonscriptsPro]

@glenn-jocher Thank you for your kind words! Your dedication to coding and making it accessible is invaluable.

[glenn-jocher]

@pythonscriptsPro thank you for your kind words! I'm glad I could help. The dedication of the YOLO community and the Ultralytics team to making coding accessible is truly invaluable. Keep up the great work, and if you have any further questions or need assistance, don't hesitate to reach out. 😊