57 KiB
Vendored
57 KiB
Vendored
This is the official YOLOv5 🚀 notebook by Ultralytics, and is freely available for redistribution under the GPL-3.0 license. For more information please visit https://github.com/ultralytics/yolov5 and https://ultralytics.com. Thank you!
In [ ]:
!git clone https://github.com/ultralytics/yolov5 # clone
%cd yolov5
%pip install -qr requirements.txt # install
import torch
import utils
display = utils.notebook_init() # checksYOLOv5 🚀 v6.1-343-g685332e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 38.6/166.8 GB disk)
In [ ]:
!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images
#display.Image(filename='runs/detect/exp/zidane.jpg', width=600)[34m[1mdetect: [0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False YOLOv5 🚀 v6.1-343-g685332e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB) Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt to yolov5s.pt... 100% 14.1M/14.1M [00:02<00:00, 6.87MB/s] Fusing layers... YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.014s) image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.019s) Speed: 0.5ms pre-process, 16.3ms inference, 22.1ms NMS per image at shape (1, 3, 640, 640) Results saved to [1mruns/detect/exp[0m
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# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')
!unzip -q tmp.zip -d ../datasets && rm tmp.zip0%| | 0.00/780M [00:00<?, ?B/s]
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# Run YOLOv5x on COCO val
!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half[34m[1mval: [0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
YOLOv5 🚀 v6.1-343-g685332e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt to yolov5x.pt...
100% 166M/166M [00:16<00:00, 10.3MB/s]
Fusing layers...
YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 14.8MB/s]
[34m[1mval: [0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 11214.34it/s]
[34m[1mval: [0mNew cache created: /content/datasets/coco/val2017.cache
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:05<00:00, 2.39it/s]
all 5000 36335 0.743 0.625 0.683 0.504
Speed: 0.1ms pre-process, 4.7ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)
Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...
loading annotations into memory...
Done (t=0.38s)
creating index...
index created!
Loading and preparing results...
DONE (t=5.39s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=71.33s).
Accumulating evaluation results...
DONE (t=12.45s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.549
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.340
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.631
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.528
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.737
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833
Results saved to [1mruns/val/exp[0m
In [ ]:
# Download COCO test-dev2017
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017labels.zip', 'tmp.zip')
!unzip -q tmp.zip -d ../datasets && rm tmp.zip
!f="test2017.zip" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d ../datasets/coco/imagesIn [ ]:
# Run YOLOv5x on COCO test
!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test3. Train
Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml, starting from pretrained --weights yolov5s.pt, or from randomly initialized --weights '' --cfg yolov5s.yaml.
- Pretrained Models are downloaded automatically from the latest YOLOv5 release
- Datasets available for autodownload include: COCO, COCO128, VOC, Argoverse, VisDrone, GlobalWheat, xView, Objects365, SKU-110K.
- Training Results are saved to
runs/train/with incrementing run directories, i.e.runs/train/exp2,runs/train/exp3etc.
A Mosaic Dataloader is used for training which combines 4 images into 1 mosaic.
Train on Custom Data with Roboflow 🌟 NEW
Roboflow enables you to easily organize, label, and prepare a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the roboflow pip package.
- Custom Training Example: https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/
- Custom Training Notebook:
In [ ]:
# Tensorboard (optional)
%load_ext tensorboard
%tensorboard --logdir runs/trainIn [ ]:
# ClearML (optional)
%pip install -q clearml
!clearml-initIn [ ]:
# Weights & Biases (optional)
%pip install -q wandb
import wandb
wandb.login()In [ ]:
# Train YOLOv5s on COCO128 for 3 epochs
!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache[34m[1mtrain: [0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
[34m[1mgithub: [0mskipping check (Docker image), for updates see https://github.com/ultralytics/yolov5
YOLOv5 🚀 v6.1-343-g685332e Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
[34m[1mhyperparameters: [0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']
Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...
100% 6.66M/6.66M [00:00<00:00, 31.8MB/s]
Dataset download success ✅ (1.5s), saved to [1m/content/datasets[0m
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
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 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
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: 270 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs
Transferred 349/349 items from yolov5s.pt
[34m[1mAMP: [0mchecks passed ✅
Scaled weight_decay = 0.0005
[34m[1moptimizer:[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
[34m[1malbumentations: [0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed
[34m[1mtrain: [0mScanning '/content/datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 13378.96it/s]
[34m[1mtrain: [0mNew cache created: /content/datasets/coco128/labels/train2017.cache
[34m[1mtrain: [0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 1053.85it/s]
[34m[1mval: [0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]
[34m[1mval: [0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 296.75it/s]
Plotting labels to runs/train/exp/labels.jpg...
[34m[1mAutoAnchor: [0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to [1mruns/train/exp[0m
Starting training for 3 epochs...
Epoch gpu_mem box obj cls labels img_size
0/2 3.76G 0.04445 0.06016 0.01651 247 640: 100% 8/8 [00:04<00:00, 1.74it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.38it/s]
all 128 929 0.763 0.611 0.716 0.469
Epoch gpu_mem box obj cls labels img_size
1/2 4.79G 0.0443 0.06624 0.01655 188 640: 100% 8/8 [00:00<00:00, 8.46it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.44it/s]
all 128 929 0.787 0.631 0.734 0.488
Epoch gpu_mem box obj cls labels img_size
2/2 4.79G 0.04283 0.0579 0.01571 217 640: 100% 8/8 [00:00<00:00, 9.69it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.70it/s]
all 128 929 0.807 0.632 0.741 0.491
3 epochs completed in 0.003 hours.
Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB
Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB
Validating runs/train/exp/weights/best.pt...
Fusing layers...
Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00, 1.36it/s]
all 128 929 0.808 0.631 0.741 0.491
person 128 254 0.886 0.689 0.812 0.53
bicycle 128 6 1 0.476 0.837 0.458
car 128 46 0.758 0.413 0.575 0.259
motorcycle 128 5 1 0.93 0.995 0.702
airplane 128 6 0.955 1 0.995 0.745
bus 128 7 0.755 0.714 0.832 0.691
train 128 3 1 0.553 0.789 0.493
truck 128 12 0.629 0.417 0.491 0.269
boat 128 6 1 0.332 0.507 0.201
traffic light 128 14 0.859 0.214 0.385 0.228
stop sign 128 2 0.809 1 0.995 0.796
bench 128 9 0.818 0.504 0.64 0.259
bird 128 16 0.921 1 0.995 0.64
cat 128 4 0.915 1 0.995 0.822
dog 128 9 0.869 0.556 0.902 0.6
horse 128 2 0.816 1 0.995 0.672
elephant 128 17 0.973 0.882 0.934 0.731
bear 128 1 0.699 1 0.995 0.895
zebra 128 4 0.877 1 0.995 0.947
giraffe 128 9 0.868 0.889 0.975 0.742
backpack 128 6 1 0.543 0.76 0.346
umbrella 128 18 0.864 0.611 0.898 0.522
handbag 128 19 0.701 0.127 0.335 0.174
tie 128 7 0.929 0.714 0.739 0.47
suitcase 128 4 0.658 0.75 0.795 0.536
frisbee 128 5 0.722 0.8 0.8 0.69
skis 128 1 0.776 1 0.995 0.3
snowboard 128 7 1 0.707 0.848 0.554
sports ball 128 6 0.662 0.667 0.602 0.316
kite 128 10 0.727 0.536 0.647 0.249
baseball bat 128 4 0.985 0.5 0.559 0.181
baseball glove 128 7 0.581 0.429 0.459 0.282
skateboard 128 5 0.739 0.6 0.705 0.501
tennis racket 128 7 0.759 0.429 0.566 0.31
bottle 128 18 0.593 0.405 0.574 0.294
wine glass 128 16 0.742 0.875 0.91 0.497
cup 128 36 0.833 0.694 0.817 0.514
fork 128 6 1 0.32 0.463 0.31
knife 128 16 0.757 0.585 0.73 0.385
spoon 128 22 0.812 0.393 0.632 0.374
bowl 128 28 0.869 0.571 0.718 0.496
banana 128 1 0.894 1 0.995 0.205
sandwich 128 2 1 0 0.308 0.263
orange 128 4 0.876 1 0.995 0.703
broccoli 128 11 0.821 0.364 0.442 0.323
carrot 128 24 0.709 0.625 0.72 0.464
hot dog 128 2 0.546 1 0.828 0.745
pizza 128 5 0.812 0.871 0.962 0.699
donut 128 14 0.686 1 0.96 0.825
cake 128 4 0.856 1 0.995 0.822
chair 128 35 0.591 0.577 0.616 0.311
couch 128 6 0.973 0.667 0.857 0.532
potted plant 128 14 0.839 0.786 0.827 0.45
bed 128 3 1 0 0.641 0.403
dining table 128 13 0.631 0.266 0.462 0.375
toilet 128 2 0.878 1 0.995 0.846
tv 128 2 0.707 1 0.995 0.821
laptop 128 3 1 0 0.863 0.498
mouse 128 2 1 0 0.0907 0.0544
remote 128 8 1 0.598 0.63 0.537
cell phone 128 8 0.661 0.5 0.465 0.249
microwave 128 3 0.823 1 0.995 0.767
oven 128 5 0.428 0.4 0.432 0.285
sink 128 6 0.354 0.167 0.268 0.178
refrigerator 128 5 0.649 0.8 0.806 0.551
book 128 29 0.618 0.207 0.333 0.161
clock 128 9 0.792 0.889 0.943 0.735
vase 128 2 0.502 1 0.995 0.895
scissors 128 1 1 0 0.332 0.0663
teddy bear 128 21 0.84 0.619 0.769 0.521
toothbrush 128 5 0.763 0.654 0.898 0.603
Results saved to [1mruns/train/exp[0m
ClearML Logging and Automation 🌟 NEW
ClearML is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):
pip install clearml- run
clearml-initto connect to a ClearML server (deploy your own open-source server, or use our free hosted server)
You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).
You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the ClearML Tutorial for details!
Weights & Biases Logging
Weights & Biases (W&B) is 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
Training results are automatically logged with Tensorboard and CSV loggers to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc.
This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
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 (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
In [ ]:
# Reproduce
for x in 'yolov5n', 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':
!python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed
!python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAPIn [ ]:
# PyTorch Hub
import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
# Images
dir = 'https://ultralytics.com/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images
# Inference
results = model(imgs)
results.print() # or .show(), .save()In [ ]:
# YOLOv5 CI
%%shell
rm -rf runs # remove runs/
m=yolov5n # official weights
b=runs/train/exp/weights/best # best.pt checkpoint
python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0 # train
for d in 0 cpu; do # devices
for w in $m $b; do # weights
python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val
python detect.py --imgsz 64 --weights $w.pt --device $d # detect
done
done
python hubconf.py --model $m # hub
python models/tf.py --weights $m.pt # build TF model
python models/yolo.py --cfg $m.yaml # build PyTorch model
python export.py --weights $m.pt --img 64 --include torchscript # exportIn [ ]:
# Profile
from utils.torch_utils import profile
m1 = lambda x: x * torch.sigmoid(x)
m2 = torch.nn.SiLU()
results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)In [ ]:
# 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)In [ ]:
# VOC
for b, m in zip([64, 64, 64, 32, 16], ['yolov5n', 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']): # batch, model
!python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cacheIn [ ]:
# TensorRT
# https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-pip
!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # install
!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0 # export
!python detect.py --weights yolov5s.engine --imgsz 640 --device 0 # inference