yolov5_ZWgra/tutorial.ipynb

This notebook was written by Ultralytics LLC, and is freely available for redistribution under the GPL-3.0 license. For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com.

Setup

Clone repo, install dependencies and check PyTorch and GPU.

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!git clone https://github.com/ultralytics/yolov5  # clone repo
%cd yolov5
%pip install -qr requirements.txt  # install dependencies

import torch
from IPython.display import Image, clear_output  # to display images

clear_output()
print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))

Setup complete. Using torch 1.8.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16160MB, multi_processor_count=80)

1. Inference

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv5 release.

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!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/
Image(filename='runs/detect/exp/zidane.jpg', width=600)

Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', exist_ok=False, img_size=640, iou_thres=0.45, name='exp', project='runs/detect', save_conf=False, save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])
YOLOv5 🚀 v4.0-137-g9b11f0c torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)

Fusing layers... 
Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.008s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.008s)
Results saved to runs/detect/exp
Done. (0.087)

Results are saved to runs/detect. A full list of available inference sources:

2. Test

Test a model on COCO val or test-dev dataset to evaluate trained accuracy. Models are downloaded automatically from the latest YOLOv5 release. To show results by class use the --verbose flag. Note that pycocotools metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation.

COCO val2017

Download COCO val 2017 dataset (1GB - 5000 images), and test model accuracy.

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# Download COCO val2017
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')
!unzip -q tmp.zip -d ../ && rm tmp.zip

HBox(children=(FloatProgress(value=0.0, max=819257867.0), HTML(value='')))

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# Run YOLOv5x on COCO val2017
!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65

Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_hybrid=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])
YOLOv5 🚀 v4.0-137-g9b11f0c torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5x.pt to yolov5x.pt...
100% 168M/168M [00:02<00:00, 59.1MB/s]

Fusing layers... 
Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS
val: Scanning '../coco/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 3236.68it/s]
val: New cache created: ../coco/val2017.cache
               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [01:20<00:00,  1.95it/s]
                 all        5000       36335       0.749       0.619        0.68       0.486
Speed: 5.3/1.7/6.9 ms inference/NMS/total per 640x640 image at batch-size 32

Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...
loading annotations into memory...
Done (t=0.43s)
creating index...
index created!
Loading and preparing results...
DONE (t=5.10s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=88.52s).
Accumulating evaluation results...
DONE (t=17.17s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.501
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.687
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.544
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.338
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.548
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.637
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.378
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.628
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.680
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.520
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.729
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826
Results saved to runs/test/exp

COCO test-dev2017

Download COCO test2017 dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images, no labels). Results are saved to a *.json file which should be zipped and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794.

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# Download COCO test-dev2017
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')
!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels
!f="test2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7GB,  41k images
%mv ./test2017 ../coco/images  # move to /coco

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# Run YOLOv5s on COCO test-dev2017 using --task test
!python test.py --weights yolov5s.pt --data coco.yaml --task test

3. Train

Download COCO128, a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (note actual training is typically much longer, around 300-1000 epochs, depending on your dataset).

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# Download COCO128
torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')
!unzip -q tmp.zip -d ../ && rm tmp.zip

HBox(children=(FloatProgress(value=0.0, max=22091032.0), HTML(value='')))

Train a YOLOv5s model on COCO128 with --data coco128.yaml, starting from pretrained --weights yolov5s.pt, or from randomly initialized --weights '' --cfg yolov5s.yaml. Models are downloaded automatically from the latest YOLOv5 release, and COCO, COCO128, and VOC datasets are downloaded automatically on first use.

All training results are saved to runs/train/ with incrementing run directories, i.e. runs/train/exp2, runs/train/exp3 etc.

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# Tensorboard (optional)
%load_ext tensorboard
%tensorboard --logdir runs/train

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# Weights & Biases (optional)
%pip install -q wandb  
!wandb login  # use 'wandb disabled' or 'wandb enabled' to disable or enable

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# Train YOLOv5s on COCO128 for 3 epochs
!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache

github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v4.0-137-g9b11f0c torch 1.8.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)

Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', entity=None, epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], linear_lr=False, local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)
wandb: Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)
Start Tensorboard with "tensorboard --logdir runs/train", view at http://localhost:6006/
2021-03-14 04:18:58.124672: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
hyperparameters: lr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0
Downloading https://github.com/ultralytics/yolov5/releases/download/v4.0/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 63.1MB/s]


                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        
  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPS

Transferred 362/362 items from yolov5s.pt
Scaled weight_decay = 0.0005
Optimizer groups: 62 .bias, 62 conv.weight, 59 other
train: Scanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2956.76it/s]
train: New cache created: ../coco128/labels/train2017.cache
train: Caching images (0.1GB): 100% 128/128 [00:00<00:00, 205.30it/s]
val: Scanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 604584.36it/s]
val: Caching images (0.1GB): 100% 128/128 [00:00<00:00, 144.17it/s]
Plotting labels... 

autoanchor: Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946
Image sizes 640 train, 640 test
Using 2 dataloader workers
Logging results to runs/train/exp
Starting training for 3 epochs...

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
       0/2     3.29G   0.04237   0.06417   0.02121    0.1277       183       640: 100% 8/8 [00:03<00:00,  2.41it/s]
               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:04<00:00,  1.04s/it]
                 all         128         929       0.642       0.637       0.661       0.432

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
       1/2     6.65G   0.04431   0.06403     0.019    0.1273       166       640: 100% 8/8 [00:01<00:00,  5.73it/s]
               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.21it/s]
                 all         128         929       0.662       0.626       0.658       0.433

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
       2/2     6.65G   0.04506   0.06836   0.01913    0.1325       182       640: 100% 8/8 [00:01<00:00,  5.51it/s]
               Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.35it/s]
                 all         128         929       0.658       0.625       0.661       0.433
Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB
Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB
3 epochs completed in 0.007 hours.

4. Visualize

Weights & Biases Logging 🌟 NEW

Weights & Biases (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B pip install wandb, and then train normally (you will be guided through setup on first use).

During training you will see live updates at https://wandb.ai/home, and you can create and share detailed Reports of your results. For more information see the YOLOv5 Weights & Biases Tutorial.

Local Logging

All results are logged by default to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a Mosaic Dataloader is used for training (shown below), a new concept developed by Ultralytics and first featured in YOLOv4.

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Image(filename='runs/train/exp/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels
Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800)  # test batch 0 labels
Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800)  # test batch 0 predictions

train_batch0.jpg shows train batch 0 mosaics and labels

test_batch0_labels.jpg shows test batch 0 labels

test_batch0_pred.jpg shows test batch 0 predictions

Training losses and performance metrics are also logged to Tensorboard and a custom results.txt logfile which is plotted as results.png (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained --weights yolov5s.pt (orange).

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from utils.plots import plot_results 
plot_results(save_dir='runs/train/exp')  # plot all results*.txt as results.png
Image(filename='runs/train/exp/results.png', width=800)

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):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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.

Appendix

Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.

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# Re-clone repo
%cd ..
%rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5
%cd yolov5

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# Reproduce
for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':
  !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45  # speed
  !python test.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP

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# Unit tests
%%shell
export PYTHONPATH="$PWD"  # to run *.py. files in subdirectories

rm -rf runs  # remove runs/
for m in yolov5s; do  # models
  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained
  python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch
  for d in 0 cpu; do  # devices
    python detect.py --weights $m.pt --device $d  # detect official
    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom
    python test.py --weights $m.pt --device $d # test official
    python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom
  done
  python hubconf.py  # hub
  python models/yolo.py --cfg $m.yaml  # inspect
  python models/export.py --weights $m.pt --img 640 --batch 1  # export
done

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# Profile
from utils.torch_utils import profile 

m1 = lambda x: x * torch.sigmoid(x)
m2 = torch.nn.SiLU()
profile(x=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)

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# Evolve
!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov5s.pt --cache --noautoanchor --evolve
!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket  # upload results (optional)

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# VOC
for b, m in zip([64, 48, 32, 16], ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']):  # zip(batch_size, model)
  !python train.py --batch {b} --weights {m}.pt --data voc.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}