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This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.

** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.

  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.

Pretrained Checkpoints

ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B

** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.
** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001
** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1
** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

Requirements

Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:

$ pip install -U -r requirements.txt

Tutorials

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Inference

Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.

$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream

To run inference on examples in the ./inference/images folder:

$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output

Training

Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).

$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16

Citation

DOI

About Us

Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:

  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.

For business inquiries and professional support requests please visit us at https://www.ultralytics.com.

Contact

Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.

About

YOLOv5 in PyTorch > ONNX > CoreML > iOS

www.ultralytics.com

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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
BranchesTags
Repository files navigation
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
About
YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
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Repository files navigation
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
About
YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
Resources
Readme
GPL-3.0 license
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
BranchesTags
Repository files navigation
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
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This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
About
YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
BranchesTags
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This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
About
YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
BranchesTags
Repository files navigation
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
About
YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
Resources
Readme
GPL-3.0 license
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GitHub - graphbuilder/yolov5: YOLOv5 in PyTorch > ONNX > CoreML > iOS · GitHub
Skip to content
BranchesTags
Repository files navigation
This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. All code and models are under active development, and are subject to modification or deletion without notice. Use at your own risk.
** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS.
  • June 22, 2020: PANet updates: new heads, reduced parameters, faster inference and improved mAP 364fcfd.
  • June 19, 2020: FP16 as new default for smaller checkpoints and faster inference d4c6674.
  • June 9, 2020: CSP updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP).
  • May 27, 2020: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations.
  • April 1, 2020: Start development of future YOLOv3/YOLOv4-based PyTorch models in a range of compound-scaled sizes.
Pretrained Checkpoints
ModelAPvalAPtestAP50SpeedGPUFPSGPUparamsFLOPS
YOLOv5s36.636.655.82.1ms4767.5M13.2B
YOLOv5m43.443.462.43.0ms33321.8M39.4B
YOLOv5l46.646.765.43.9ms25647.8M88.1B
YOLOv5x48.448.466.96.1ms16489.0M166.4B
YOLOv3-SPP45.645.565.24.5ms22263.0M118.0B
** APtest denotes COCO test-dev2017 server results, all other AP results in the table denote val2017 accuracy.

** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by python test.py --data coco.yaml --img 736 --conf 0.001

** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --data coco.yaml --img 640 --conf 0.1

** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
Requirements
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txt
Tutorials
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
Inference
Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output.
$ python detect.py --source 0 # webcam
file.jpg # image 
file.mp4 # video
path/ # directory
path/*.jpg # glob
rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream
http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream
To run inference on examples in the ./inference/images folder:
$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4
Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')
Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)
Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s)
image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s)
image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s)
Results saved to /content/yolov5/inference/output
Training
Download COCO, install Apex and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest --batch-size your GPU allows (batch sizes shown for 16 GB devices).
$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
yolov5m 48
yolov5l 32
yolov5x 16
Citation
DOI
About Us
Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including:
  • Cloud-based AI systems operating on hundreds of HD video streams in realtime.
  • Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
  • Custom data training, hyperparameter evolution, and model exportation to any destination.
For business inquiries and professional support requests please visit us at https://www.ultralytics.com.
Contact
Issues should be raised directly in the repository. For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
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YOLOv5 in PyTorch > ONNX > CoreML > iOS
www.ultralytics.com
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