Closed caiusdebucean closed 3 years ago
π Hello @caiusdebucean, thank you for your interest in π YOLOv5! Please visit our βοΈ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7
. To install run:
$ pip install -r requirements.txt
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
@caiusdebucean YOLOv5 models are trained using Automatic Mixed Precision (AMP). https://github.com/ultralytics/yolov5/blob/3e25f1e9e52069189fb140d50c09dddb5be3cff0/train.py#L290-L298
hai iam bigfan of yolov5
1.first u have to tarin on the fp32 then convert to onxx and convert tensorrt fp16 for inference .which can increases the your speed to more than as u expected in training bro
@glenn-jocher when I convert the .pt into .wts and .cfg via marcoslucianops/DeepStream-Yolo repo for further deployment on Jetson, it is required to have FP16 or INT8 weights if I want to use DLA.(It is required for me to leave the GPU for other processes) How should I procceed?
You can convert the .pt model to FP16/INT8. Check #10505 - here there are presented the export parameters. For example, --half
will export the model in FP16.
@caiusdebucean my output should be a .pt file aswell.
Trying --include
or --include -
does not work for me.
What should be the exact instruction to export it?
π Hello! Thanks for asking about Export Formats. YOLOv5 π offers export to almost all of the common export formats. See our TFLite, ONNX, CoreML, TensorRT Export Tutorial for full details.
YOLOv5 inference is officially supported in 11 formats:
π‘ ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks. π‘ 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 below run on a Colab Pro with the YOLOv5 tutorial notebook . To reproduce:
python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0
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 π v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete β
(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
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 π v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete β
(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
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
π‘ 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 π 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 β
1.7s, saved as yolov5s.torchscript (28.1 MB)
ONNX: starting export with onnx 1.12.0...
ONNX: export success β
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:
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 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
YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:
YOLOv5 OpenVINO C++ inference examples:
Good luck π and let us know if you have any other questions!
@glenn-jocher when I convert the .pt into .wts and .cfg via marcoslucianops/DeepStream-Yolo repo for further deployment on Jetson, it is required to have FP16 or INT8 weights if I want to use DLA.(It is required for me to leave the GPU for other processes) How should I proceed?
@Alberto1404 While converting within marcoslucianops/DeepStream-Yolo repo did you observe a drastic drop in accuracy of the model?
Hello @Kanan99! To ensure your model uses FP16 or INT8 weights for DLA on Jetson, you'll need to convert your .pt
model to the desired precision before exporting to .wts
and .cfg
. You can do this directly in the YOLOv5 repository using the --half
flag during the export for FP16. Hereβs a quick example:
python export.py --weights yolov5s.pt --include torchscript --half
This command converts the model to FP16, which you can then further convert using the DeepStream-Yolo repo. If you're experiencing a drastic drop in accuracy, it might be due to the precision reduction. It's a common trade-off for the speed gains on DLA. You might want to experiment with different quantization approaches or adjust the confidence thresholds to mitigate this. π
β Can you train yolov5 in FP16?
Additional context
I understand that the model is converted to FP16 at the end of the training cycle, and its inferenced in FP16. I was wondering if its possible to train in half-precision or mixed-precision, in order to speed up the training process. Thanks.