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Glenn Jocher 8bc0027afc Update loss criteria constructor (#1711 )

2020-12-16 08:39:35 -08:00

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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.

In [ ]:

!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 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)

1. Inference

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

In [ ]:

!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='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)

Fusing layers... 
Model Summary: 232 layers, 7459581 parameters, 0 gradients
image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)
image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)
Results saved to runs/detect/exp
Done. (0.113s)

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.

In [ ]:

# 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='')))

In [ ]:

# 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_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)

Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...
100% 170M/170M [00:05<00:00, 32.6MB/s]

Fusing layers... 
Model Summary: 484 layers, 88922205 parameters, 0 gradients
Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 157/157 [01:30<00:00,  1.74it/s]
                 all       5e+03    3.63e+04       0.409       0.754       0.672       0.484
Speed: 5.9/2.1/7.9 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=4.67s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=92.11s).
Accumulating evaluation results...
DONE (t=13.24s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.492
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.676
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.534
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.376
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.617
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.670
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812
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). Results are saved to a *.json file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794.

In [ ]:

# 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 && mv ./coco ../  # move images to /coco and move /coco next to /yolov5

In [ ]:

# 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).

In [ ]:

# 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=22090455.0), 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.

In [ ]:

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

In [ ]:

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

In [ ]:

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

Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)

Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', 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)
Start Tensorboard with "tensorboard --logdir runs/train", view at http://localhost:6006/
2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
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/v3.1/yolov5s.pt to yolov5s.pt...
100% 14.5M/14.5M [00:01<00:00, 14.8MB/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     19904  models.common.BottleneckCSP             [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1    161152  models.common.BottleneckCSP             [128, 128, 3]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  1    641792  models.common.BottleneckCSP             [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   1248768  models.common.BottleneckCSP             [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    378624  models.common.BottleneckCSP             [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     95104  models.common.BottleneckCSP             [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    313088  models.common.BottleneckCSP             [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   1248768  models.common.BottleneckCSP             [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, 7468157 parameters, 7468157 gradients

Transferred 370/370 items from yolov5s.pt
Optimizer groups: 62 .bias, 70 conv.weight, 59 other
Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]
Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]
Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]
Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]
Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]
NumExpr defaulting to 2 threads.

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   targets  img_size
       0/2     5.24G   0.04202   0.06745   0.01503    0.1245       194       640: 100% 8/8 [00:03<00:00,  2.01it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:03<00:00,  2.40it/s]
                 all         128         929       0.404       0.758       0.701        0.45

     Epoch   gpu_mem       box       obj       cls     total   targets  img_size
       1/2     5.12G   0.04461   0.05874    0.0169    0.1202       142       640: 100% 8/8 [00:01<00:00,  4.14it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:01<00:00,  5.75it/s]
                 all         128         929       0.403       0.772       0.703       0.453

     Epoch   gpu_mem       box       obj       cls     total   targets  img_size
       2/2     5.12G   0.04445   0.06545   0.01667    0.1266       149       640: 100% 8/8 [00:01<00:00,  4.15it/s]
               Class      Images     Targets           P           R      mAP@.5  mAP@.5:.95: 100% 8/8 [00:06<00:00,  1.18it/s]
                 all         128         929       0.395       0.767       0.702       0.452
Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB
3 epochs completed in 0.006 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.

In [ ]:

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).

In [ ]:

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 Notebook with free GPU:
Kaggle Notebook with free GPU: https://www.kaggle.com/ultralytics/yolov5
Google Cloud Deep Learning VM. See GCP Quickstart Guide
Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Appendix

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

In [ ]:

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

In [ ]:

# Test all
%%shell
for x in s m l x; do
  python test.py --weights yolov5$x.pt --data coco.yaml --img 640
done

In [ ]:

# 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

In [ ]:

# 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)

In [ ]:

# 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}

382 KiB Vendored Raw Blame History

Setup

1. Inference

2. Test

COCO val2017

COCO test-dev2017

3. Train

4. Visualize

Weights & Biases Logging 🌟 NEW

Local Logging

Environments

Appendix

382 KiB

Vendored

Raw Blame History