Garbage-detection-yolov5

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YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

See the YOLOv5 Docs for full documentation on training, testing and deployment.

Install

Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.

git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install

Inference

YOLOv5 PyTorch Hub inference. Models download automatically from the latest YOLOv5 release.

import torch # Model model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom # 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.

Inference with detect.py

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release and saving results to runs/detect.

python detect.py --source 0 # webcam img.jpg # image vid.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/Zgi9g1ksQHc' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream

Training

The commands below reproduce YOLOv5 COCO results. Models and datasets download automatically from the latest YOLOv5 release. Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the largest --batch-size possible, or pass --batch-size -1 for YOLOv5 AutoBatch. Batch sizes shown for V100-16GB.

python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128 yolov5s 64 yolov5m 40 yolov5l 24 yolov5x 16

Tutorials

Train Custom Data 🚀 RECOMMENDED

Tips for Best Training Results ☘️ RECOMMENDED

Weights & Biases Logging 🌟 NEW

Roboflow for Datasets, Labeling, and Active Learning 🌟 NEW

PyTorch Hub ⭐ NEW

TFLite, ONNX, CoreML, TensorRT Export 🚀

Transfer Learning with Frozen Layers ⭐ NEW

Architecture Summary ⭐ NEW

Get started in seconds with our verified environments. Click each icon below for details.

Weights and Biases

Roboflow ⭐ NEW

Automatically track and visualize all your YOLOv5 training runs in the cloud with

Label and export your custom datasets directly to YOLOv5 for training with

YOLOv5-P5 640 Figure (click to expand)

Figure Notes (click to expand)

COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.

GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.

EfficientDet data from google/automl at batch size 8.

Reproduce by python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt

Model

size

(pixels)

mAP

val

0.5:0.95

mAP

val

0.5

Speed

CPU b1

(ms)

Speed

V100 b1

(ms)

Speed

V100 b32

(ms)

params

(M)

FLOPs

@640 (B)

640

28.0

45.7

45

6.3

0.6

1.9

4.5

640

37.4

56.8

98

6.4

0.9

7.2

16.5

640

45.4

64.1

224

8.2

1.7

21.2

49.0

640

49.0

67.3

430

10.1

2.7

46.5

109.1

640

50.7

68.9

766

12.1

4.8

86.7

205.7

1280

36.0

54.4

153

8.1

2.1

3.2

4.6

1280

44.8

63.7

385

8.2

3.6

12.6

16.8

1280

51.3

69.3

887

11.1

6.8

35.7

50.0

1280

53.7

71.3

1784

15.8

10.5

76.8

111.4

+

1280

1536

55.0

55.8

72.7

72.7

3136

-

26.2

-

19.4

-

140.7

-

209.8

-

Table Notes (click to expand)

All checkpoints are trained to 300 epochs with default settings. Nano and Small models use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.

mAPval values are for single-model single-scale on COCO val2017 dataset.Reproduce by python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65

Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.Reproduce by python val.py --data coco.yaml --img 640 --task speed --batch 1

TTA Test Time Augmentation includes reflection and scale augmentations.Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!

For YOLOv5 bugs and feature requests please visit GitHub Issues. For business inquiries or professional support requests please visit https://ultralytics.com/contact.