yolov5-model-training
Science Score: 44.0%
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○Scientific vocabulary similarity
Low similarity (10.9%) to scientific vocabulary
Last synced: 6 months ago
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Basic Info
- Host: GitHub
- Owner: zash13
- License: agpl-3.0
- Language: Python
- Default Branch: master
- Size: 87.1 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 4
- Releases: 0
Created over 1 year ago
· Last pushed over 1 year ago
Metadata Files
Readme
Contributing
License
Citation
README.md
```python
This Python 3 environment comes with many helpful analytics libraries installed
It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
For example, here's several helpful packages to load
import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Input data files are available in the read-only "../input/" directory
For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename))
You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
```
!python train.py --resume runs/train/exp1/weights/best.pt --epochs 50
```python %pip install torch torchvision torchaudio %pip install pyyaml matplotlib tqdm !git clone https://github.com/ultralytics/yolov5.git # clone %cd yolov5 %pip install -qr requirements.txt # install
import torch import utils display = utils.notebook_init() # checks ```
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
Setup complete ✅ (4 CPUs, 31.4 GB RAM, 5689.4/8062.4 GB disk)
python
!ls
```python visdrone_yaml = """ path : /kaggle/input/visdrone2019 train : /kaggle/input/visdrone2019/images/train val : /kaggle/input/visdrone2019/images/val names:
0: pedes #pedestrian
1: people
2: bicycle
3: car
4: van
5: truck
6: tricycle
7: awntric #awning-tricycle
8: bus
9: motor download: | from utils.general import download, Path
# Download labels segments = False # segment or box labels dir = Path(yaml['path']) # dataset root dir url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels download(urls, dir=dir.parent)
# Download data urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) download(urls, dir=dir / 'images', threads=3)
"""
Save the changes to a new YAML file
with open('/kaggle/working/yolov5/data/cocoCastom.yaml', 'w') as file: file.write(visdrone_yaml)
```
```python !ls !python train.py --img 640 --batch 16 --epochs 50 --data cocoCastom.yaml --weights yolov5m.pt --cache --name exp1_50ep
!cp runs/train/exp150ep/weights/best.pt runs/train/exp150ep/weights/exp1ep50best.pt.backup
```
CITATION.cff README.zh-CN.md detect.py pyproject.toml tutorial.ipynb
CONTRIBUTING.md benchmarks.py export.py requirements.txt utils
LICENSE classify hubconf.py segment val.py
README.md data models train.py
2024-06-09 23:49:37.071244: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-06-09 23:49:37.071388: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-06-09 23:49:37.198390: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[34m[1mtrain: [0mweights=yolov5m.pt, cfg=, data=cocoCastom.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_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=exp1_50ep, 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, ndjson_console=False, ndjson_file=False
[34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
[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[1mComet: [0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt to yolov5m.pt...
100%|███████████████████████████████████████| 40.8M/40.8M [00:00<00:00, 160MB/s]
Overriding model.yaml nc=80 with nc=10
from n params module arguments
0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 2 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 4 444672 models.common.C3 [192, 192, 4]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 6 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 2 4134912 models.common.C3 [768, 768, 2]
9 -1 1 1476864 models.common.SPPF [768, 768, 5]
10 -1 1 295680 models.common.Conv [768, 384, 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 2 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 2 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 60615 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Model summary: 291 layers, 20907687 parameters, 20907687 gradients, 48.3 GFLOPs
Transferred 475/481 items from yolov5m.pt
[34m[1mAMP: [0mchecks passed ✅
[34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
[34m[1malbumentations: [0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
[34m[1mtrain: [0mScanning /kaggle/input/visdrone2019/labels/train... 6471 images, 0 backgr[0m
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/train.cache.npy'
[34m[1mtrain: [0mCaching images (4.9GB ram): 100%|██████████| 6471/6471 [00:29<00:00, 220.[0m
[34m[1mval: [0mScanning /kaggle/input/visdrone2019/labels/val... 548 images, 0 backgrounds[0m
[34m[1mval: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/val.cache.npy'
[34m[1mval: [0mCaching images (0.4GB ram): 100%|██████████| 548/548 [00:03<00:00, 163.37it[0m
[34m[1mAutoAnchor: [0m2.95 anchors/target, 0.933 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...
[34m[1mAutoAnchor: [0mWARNING ⚠️ Extremely small objects found: 29644 of 343201 labels are <3 pixels in size
[34m[1mAutoAnchor: [0mRunning kmeans for 9 anchors on 342304 points...
[34m[1mAutoAnchor: [0mEvolving anchors with Genetic Algorithm: fitness = 0.7501: 100%|████[0m
[34m[1mAutoAnchor: [0mthr=0.25: 0.9995 best possible recall, 5.74 anchors past thr
[34m[1mAutoAnchor: [0mn=9, img_size=640, metric_all=0.364/0.749-mean/best, past_thr=0.486-mean: 3,5, 4,9, 8,7, 8,14, 16,9, 14,21, 29,16, 34,34, 63,60
[34m[1mAutoAnchor: [0mDone ✅ (optional: update model *.yaml to use these anchors in the future)
Plotting labels to runs/train/exp1_50ep/labels.jpg...
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to [1mruns/train/exp1_50ep[0m
Starting training for 50 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/49 7.11G 0.1244 0.1405 0.04769 293 640: 1
Class Images Instances P R mAP50
all 548 38759 0.33 0.191 0.114 0.0505
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/49 7.11G 0.1069 0.1675 0.03595 641 640: 1
Class Images Instances P R mAP50
all 548 38759 0.376 0.217 0.16 0.0726
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/49 7.12G 0.105 0.1681 0.03304 483 640: 1
Class Images Instances P R mAP50
all 548 38759 0.428 0.212 0.184 0.0866
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/49 7.12G 0.1027 0.1681 0.0316 820 640: 1
Class Images Instances P R mAP50
all 548 38759 0.542 0.225 0.201 0.0947
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/49 7.12G 0.1008 0.1663 0.03034 675 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.251 0.233 0.114
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/49 7.12G 0.09999 0.1663 0.02959 402 640: 1
Class Images Instances P R mAP50
all 548 38759 0.445 0.262 0.247 0.125
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/49 7.12G 0.0991 0.1635 0.02871 481 640: 1
Class Images Instances P R mAP50
all 548 38759 0.443 0.29 0.264 0.137
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
7/49 7.12G 0.09848 0.1643 0.02837 579 640: 1
Class Images Instances P R mAP50
all 548 38759 0.371 0.307 0.278 0.147
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
8/49 7.12G 0.09743 0.1627 0.02783 775 640: 1
Class Images Instances P R mAP50
all 548 38759 0.387 0.308 0.286 0.152
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
9/49 7.12G 0.09717 0.1623 0.02736 449 640: 1
Class Images Instances P R mAP50
all 548 38759 0.398 0.325 0.302 0.161
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
10/49 7.12G 0.09644 0.1608 0.02688 326 640: 1
Class Images Instances P R mAP50
all 548 38759 0.401 0.314 0.294 0.158
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
11/49 7.12G 0.09623 0.1597 0.02658 260 640: 1
Class Images Instances P R mAP50
all 548 38759 0.426 0.323 0.31 0.168
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
12/49 7.12G 0.09583 0.1599 0.02613 643 640: 1
Class Images Instances P R mAP50
all 548 38759 0.407 0.337 0.317 0.172
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
13/49 7.12G 0.09555 0.1604 0.02602 612 640: 1
Class Images Instances P R mAP50
all 548 38759 0.42 0.331 0.319 0.173
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
14/49 7.12G 0.09523 0.1577 0.02564 670 640: 1
Class Images Instances P R mAP50
all 548 38759 0.42 0.338 0.321 0.173
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
15/49 7.12G 0.09505 0.159 0.02543 375 640: 1
Class Images Instances P R mAP50
all 548 38759 0.433 0.337 0.329 0.181
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
16/49 7.12G 0.09479 0.1573 0.02516 555 640: 1
Class Images Instances P R mAP50
all 548 38759 0.433 0.337 0.329 0.18
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
17/49 7.12G 0.09434 0.1559 0.0251 575 640: 1
Class Images Instances P R mAP50
all 548 38759 0.434 0.338 0.334 0.183
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
18/49 7.12G 0.09461 0.1569 0.02486 710 640: 1
Class Images Instances P R mAP50
all 548 38759 0.447 0.338 0.335 0.183
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
19/49 7.12G 0.09399 0.1586 0.02466 770 640: 1
Class Images Instances P R mAP50
all 548 38759 0.448 0.349 0.339 0.187
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
20/49 7.12G 0.09391 0.1556 0.02435 572 640: 1
Class Images Instances P R mAP50
all 548 38759 0.451 0.356 0.346 0.191
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
21/49 7.12G 0.09373 0.1554 0.02439 621 640: 1
Class Images Instances P R mAP50
all 548 38759 0.456 0.354 0.346 0.192
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
22/49 7.12G 0.09347 0.1545 0.02417 717 640: 1
Class Images Instances P R mAP50
all 548 38759 0.45 0.351 0.346 0.195
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
23/49 7.12G 0.09307 0.1533 0.02418 661 640: 1
Class Images Instances P R mAP50
all 548 38759 0.449 0.355 0.347 0.194
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
24/49 7.12G 0.09307 0.154 0.02389 316 640: 1
Class Images Instances P R mAP50
all 548 38759 0.464 0.341 0.345 0.192
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
25/49 7.12G 0.09306 0.154 0.02388 715 640: 1
Class Images Instances P R mAP50
all 548 38759 0.455 0.358 0.352 0.196
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
26/49 7.12G 0.09271 0.154 0.0235 684 640: 1
Class Images Instances P R mAP50
all 548 38759 0.454 0.363 0.356 0.199
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
27/49 7.12G 0.09263 0.1514 0.02334 622 640: 1
Class Images Instances P R mAP50
all 548 38759 0.451 0.359 0.352 0.198
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
28/49 7.12G 0.09219 0.1527 0.02332 436 640: 1
Class Images Instances P R mAP50
all 548 38759 0.456 0.367 0.359 0.201
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
29/49 7.12G 0.09214 0.1522 0.02332 624 640: 1
Class Images Instances P R mAP50
all 548 38759 0.47 0.354 0.358 0.201
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
30/49 7.12G 0.09223 0.1515 0.02309 425 640: 1
Class Images Instances P R mAP50
all 548 38759 0.46 0.372 0.366 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
31/49 7.12G 0.09191 0.1503 0.02298 471 640: 1
Class Images Instances P R mAP50
all 548 38759 0.456 0.366 0.358 0.2
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
32/49 7.12G 0.09187 0.1513 0.02274 752 640: 1
Class Images Instances P R mAP50
all 548 38759 0.467 0.364 0.363 0.204
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
33/49 7.12G 0.09181 0.1493 0.0229 379 640: 1
Class Images Instances P R mAP50
all 548 38759 0.484 0.358 0.364 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
34/49 7.12G 0.09177 0.1507 0.02275 617 640: 1
Class Images Instances P R mAP50
all 548 38759 0.467 0.372 0.363 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
35/49 7.12G 0.09123 0.1493 0.02257 536 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.366 0.364 0.206
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
36/49 7.12G 0.09186 0.1484 0.02248 401 640: 1
Class Images Instances P R mAP50
all 548 38759 0.474 0.363 0.364 0.206
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
37/49 7.12G 0.09103 0.1502 0.02235 691 640: 1
Class Images Instances P R mAP50
all 548 38759 0.462 0.378 0.366 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
38/49 7.12G 0.09116 0.1479 0.02228 574 640: 1
Class Images Instances P R mAP50
all 548 38759 0.466 0.371 0.365 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
39/49 7.12G 0.09081 0.1469 0.02222 750 640: 1
Class Images Instances P R mAP50
all 548 38759 0.477 0.369 0.368 0.209
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
40/49 7.12G 0.09136 0.1465 0.02227 841 640: 1
Class Images Instances P R mAP50
all 548 38759 0.473 0.375 0.373 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
41/49 7.12G 0.0905 0.1456 0.02197 576 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.373 0.37 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
42/49 7.12G 0.09095 0.1485 0.02188 589 640: 1
Class Images Instances P R mAP50
all 548 38759 0.482 0.375 0.374 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
43/49 7.12G 0.09071 0.1431 0.02169 315 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.38 0.372 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
44/49 7.12G 0.09037 0.1453 0.02166 792 640: 1
Class Images Instances P R mAP50
all 548 38759 0.487 0.374 0.375 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
45/49 7.12G 0.09013 0.1449 0.02159 651 640: 1
Class Images Instances P R mAP50
all 548 38759 0.486 0.373 0.375 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
46/49 7.12G 0.09064 0.1444 0.02161 336 640: 1
Class Images Instances P R mAP50
all 548 38759 0.483 0.375 0.375 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
47/49 7.12G 0.09007 0.1456 0.02148 384 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.376 0.373 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
48/49 7.12G 0.0899 0.1443 0.0214 500 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.377 0.375 0.215
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
49/49 7.12G 0.08956 0.141 0.02127 628 640: 1
Class Images Instances P R mAP50
all 548 38759 0.475 0.379 0.375 0.215
50 epochs completed in 2.816 hours.
Optimizer stripped from runs/train/exp1_50ep/weights/last.pt, 42.2MB
Optimizer stripped from runs/train/exp1_50ep/weights/best.pt, 42.2MB
Validating runs/train/exp1_50ep/weights/best.pt...
Fusing layers...
Model summary: 212 layers, 20889303 parameters, 0 gradients, 48.0 GFLOPs
Class Images Instances P R mAP50 WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50
all 548 38759 0.469 0.364 0.362 0.209
pedes 548 8844 0.513 0.424 0.444 0.2
people 548 5125 0.463 0.321 0.319 0.12
bicycle 548 1287 0.278 0.178 0.143 0.0558
car 548 14064 0.661 0.739 0.755 0.522
van 548 1975 0.463 0.404 0.395 0.276
truck 548 750 0.535 0.353 0.361 0.234
tricycle 548 1045 0.471 0.211 0.223 0.121
awntric 548 532 0.247 0.128 0.107 0.068
bus 548 251 0.547 0.47 0.481 0.328
motor 548 4886 0.511 0.411 0.393 0.168
Results saved to [1mruns/train/exp1_50ep[0m
python
!ls
%cd ./yolov5
!python train.py --resume
!pkill jupyter
```python
! cp /kaggle/working/yolov5/runs/train/exp250ep2/weights/best.pt ./saves/bestexp2_50ep.py ```
python
!python train.py --data cocoCastom.yaml --weights /kaggle/working/yolov5/runs/train/exp1_50ep/weights/last.pt --epochs 50 --cache --name exp2_50ep
[Errno 2] No such file or directory: './yolov5'
/kaggle/working/yolov5
[34m[1mwandb[0m: WARNING ⚠️ wandb is deprecated and will be removed in a future release. See supported integrations at https://github.com/ultralytics/yolov5#integrations.
2024-06-10 05:22:38.393931: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-06-10 05:22:38.393991: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-06-10 05:22:38.395448: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[34m[1mwandb[0m: (1) Create a W&B account
[34m[1mwandb[0m: (2) Use an existing W&B account
[34m[1mwandb[0m: (3) Don't visualize my results
[34m[1mwandb[0m: Enter your choice: (30 second timeout)
[34m[1mwandb[0m: W&B disabled due to login timeout.
[34m[1mtrain: [0mweights=/kaggle/working/yolov5/runs/train/exp1_50ep/weights/last.pt, cfg=, data=cocoCastom.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_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=exp2_50ep, 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, ndjson_console=False, ndjson_file=False
[34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
[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[1mComet: [0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
from n params module arguments
0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 2 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 4 444672 models.common.C3 [192, 192, 4]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 6 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 2 4134912 models.common.C3 [768, 768, 2]
9 -1 1 1476864 models.common.SPPF [768, 768, 5]
10 -1 1 295680 models.common.Conv [768, 384, 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 2 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 2 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 60615 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Model summary: 291 layers, 20907687 parameters, 20907687 gradients, 48.3 GFLOPs
Transferred 481/481 items from /kaggle/working/yolov5/runs/train/exp1_50ep/weights/last.pt
[34m[1mAMP: [0mchecks passed ✅
[34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
[34m[1malbumentations: [0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
[34m[1mtrain: [0mScanning /kaggle/input/visdrone2019/labels/train... 6471 images, 0 backgr[0m
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/train.cache.npy'
[34m[1mtrain: [0mCaching images (4.9GB ram): 100%|██████████| 6471/6471 [00:28<00:00, 226.[0m
[34m[1mval: [0mScanning /kaggle/input/visdrone2019/labels/val... 548 images, 0 backgrounds[0m
[34m[1mval: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/val.cache.npy'
[34m[1mval: [0mCaching images (0.4GB ram): 100%|██████████| 548/548 [00:03<00:00, 171.76it[0m
[34m[1mAutoAnchor: [0m5.73 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp2_50ep2/labels.jpg...
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to [1mruns/train/exp2_50ep2[0m
Starting training for 50 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/49 7.11G 0.08999 0.1439 0.02135 293 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.377 0.374 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/49 7.11G 0.0913 0.1464 0.02171 641 640: 1
Class Images Instances P R mAP50
all 548 38759 0.474 0.362 0.365 0.204
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/49 7.12G 0.09275 0.1515 0.02251 483 640: 1
Class Images Instances P R mAP50
all 548 38759 0.451 0.345 0.336 0.182
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/49 7.12G 0.09372 0.1554 0.02344 820 640: 1
Class Images Instances P R mAP50
all 548 38759 0.458 0.343 0.344 0.189
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/49 7.12G 0.09372 0.1545 0.02357 675 640: 1
Class Images Instances P R mAP50
all 548 38759 0.452 0.343 0.341 0.185
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/49 7.12G 0.09383 0.1558 0.02354 402 640: 1
Class Images Instances P R mAP50
all 548 38759 0.442 0.355 0.348 0.189
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/49 7.12G 0.0937 0.153 0.02337 481 640: 1
Class Images Instances P R mAP50
all 548 38759 0.459 0.351 0.35 0.192
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
7/49 7.12G 0.09354 0.1543 0.02349 579 640: 1
Class Images Instances P R mAP50
all 548 38759 0.451 0.355 0.349 0.188
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
8/49 7.12G 0.09296 0.1532 0.02334 775 640: 1
Class Images Instances P R mAP50
all 548 38759 0.452 0.353 0.348 0.19
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
9/49 7.12G 0.09299 0.1529 0.02313 449 640: 1
Class Images Instances P R mAP50
all 548 38759 0.459 0.357 0.352 0.196
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
10/49 7.12G 0.09258 0.1517 0.02294 326 640: 1
Class Images Instances P R mAP50
all 548 38759 0.462 0.351 0.345 0.189
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
11/49 7.12G 0.09251 0.151 0.02294 260 640: 1
Class Images Instances P R mAP50
all 548 38759 0.462 0.347 0.351 0.194
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
12/49 7.12G 0.09225 0.1511 0.02261 643 640: 1
Class Images Instances P R mAP50
all 548 38759 0.469 0.358 0.355 0.198
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
13/49 7.12G 0.09221 0.1518 0.02267 612 640: 1
Class Images Instances P R mAP50
all 548 38759 0.458 0.352 0.351 0.197
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
14/49 7.12G 0.09201 0.1495 0.0225 670 640: 1
Class Images Instances P R mAP50
all 548 38759 0.471 0.354 0.356 0.197
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
15/49 7.12G 0.092 0.1507 0.02237 375 640: 1
Class Images Instances P R mAP50
all 548 38759 0.454 0.36 0.354 0.197
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
16/49 7.12G 0.09183 0.1494 0.02227 555 640: 1
Class Images Instances P R mAP50
all 548 38759 0.463 0.36 0.363 0.203
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
17/49 7.12G 0.09145 0.148 0.02217 575 640: 1
Class Images Instances P R mAP50
all 548 38759 0.457 0.366 0.362 0.203
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
18/49 7.12G 0.09188 0.1491 0.02211 710 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.353 0.359 0.201
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
19/49 7.12G 0.09134 0.1507 0.022 770 640: 1
Class Images Instances P R mAP50
all 548 38759 0.463 0.364 0.36 0.201
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
20/49 7.12G 0.09134 0.1481 0.02178 572 640: 1
Class Images Instances P R mAP50
all 548 38759 0.477 0.362 0.362 0.203
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
21/49 7.12G 0.09116 0.1478 0.02187 621 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.362 0.364 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
22/49 7.12G 0.09102 0.1471 0.02172 717 640: 1
Class Images Instances P R mAP50
all 548 38759 0.465 0.366 0.361 0.204
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
23/49 7.12G 0.0907 0.1459 0.02171 661 640: 1
Class Images Instances P R mAP50
all 548 38759 0.476 0.362 0.365 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
24/49 7.12G 0.09077 0.1468 0.02154 316 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.371 0.366 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
25/49 7.12G 0.09079 0.1467 0.02154 715 640: 1
Class Images Instances P R mAP50
all 548 38759 0.479 0.369 0.367 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
26/49 7.12G 0.0905 0.147 0.02125 684 640: 1
Class Images Instances P R mAP50
all 548 38759 0.471 0.37 0.366 0.208
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
27/49 7.12G 0.09043 0.1443 0.02111 622 640: 1
Class Images Instances P R mAP50
all 548 38759 0.486 0.369 0.373 0.211
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
28/49 7.12G 0.09003 0.1456 0.0211 436 640: 1
Class Images Instances P R mAP50
all 548 38759 0.473 0.372 0.366 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
29/49 7.12G 0.09007 0.145 0.02118 624 640: 1
Class Images Instances P R mAP50
all 548 38759 0.486 0.366 0.368 0.209
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
30/49 7.12G 0.0902 0.1446 0.02095 425 640: 1
Class Images Instances P R mAP50
all 548 38759 0.479 0.374 0.371 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
31/49 7.12G 0.08985 0.1436 0.02085 471 640: 1
Class Images Instances P R mAP50
all 548 38759 0.467 0.373 0.368 0.209
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
32/49 7.12G 0.08987 0.1446 0.02067 752 640: 1
Class Images Instances P R mAP50
all 548 38759 0.476 0.376 0.373 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
33/49 7.12G 0.08985 0.1426 0.02085 379 640: 1
Class Images Instances P R mAP50
all 548 38759 0.483 0.371 0.373 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
34/49 7.12G 0.08985 0.1442 0.02069 617 640: 1
Class Images Instances P R mAP50
all 548 38759 0.475 0.379 0.373 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
35/49 7.12G 0.08931 0.1428 0.02058 536 640: 1
Class Images Instances P R mAP50
all 548 38759 0.488 0.37 0.377 0.215
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
36/49 7.12G 0.09002 0.142 0.02049 401 640: 1
Class Images Instances P R mAP50
all 548 38759 0.482 0.376 0.374 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
37/49 7.12G 0.08917 0.1438 0.02038 691 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.38 0.374 0.215
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
38/49 7.12G 0.08933 0.1417 0.02034 574 640: 1
Class Images Instances P R mAP50
all 548 38759 0.479 0.371 0.372 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
39/49 7.12G 0.08901 0.1409 0.02027 750 640: 1
Class Images Instances P R mAP50
all 548 38759 0.496 0.371 0.375 0.215
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
40/49 7.12G 0.0896 0.1406 0.02035 841 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.368 0.373 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
41/49 7.12G 0.08874 0.1397 0.02009 576 640: 1
Class Images Instances P R mAP50
all 548 38759 0.483 0.374 0.376 0.216
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
42/49 7.12G 0.08924 0.1426 0.02002 589 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.374 0.377 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
43/49 7.12G 0.089 0.1376 0.01981 315 640: 1
Class Images Instances P R mAP50
all 548 38759 0.485 0.376 0.378 0.218
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
44/49 7.12G 0.08871 0.1397 0.01985 792 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.376 0.38 0.22
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
45/49 7.12G 0.08849 0.1394 0.01976 651 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.375 0.379 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
46/49 7.12G 0.08903 0.1389 0.0198 336 640: 1
Class Images Instances P R mAP50
all 548 38759 0.491 0.377 0.38 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
47/49 7.12G 0.08844 0.14 0.01968 384 640: 1
Class Images Instances P R mAP50
all 548 38759 0.485 0.376 0.38 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
48/49 7.12G 0.0883 0.139 0.01965 500 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.375 0.381 0.22
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
49/49 7.12G 0.08797 0.136 0.01953 628 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.383 0.381 0.22
50 epochs completed in 2.782 hours.
Optimizer stripped from runs/train/exp2_50ep2/weights/last.pt, 42.2MB
Optimizer stripped from runs/train/exp2_50ep2/weights/best.pt, 42.2MB
Validating runs/train/exp2_50ep2/weights/best.pt...
Fusing layers...
Model summary: 212 layers, 20889303 parameters, 0 gradients, 48.0 GFLOPs
Class Images Instances P R mAP50 WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50
all 548 38759 0.473 0.376 0.373 0.217
pedes 548 8844 0.517 0.429 0.449 0.203
people 548 5125 0.481 0.339 0.341 0.127
bicycle 548 1287 0.3 0.189 0.154 0.06
car 548 14064 0.679 0.742 0.762 0.529
van 548 1975 0.453 0.415 0.4 0.281
truck 548 750 0.517 0.359 0.371 0.251
tricycle 548 1045 0.408 0.255 0.217 0.116
awntric 548 532 0.25 0.147 0.125 0.0807
bus 548 251 0.601 0.458 0.503 0.345
motor 548 4886 0.521 0.428 0.408 0.174
Results saved to [1mruns/train/exp2_50ep2[0m
python
!rm -rf /kaggle/working/yolov5/runs/train/exp2_50ep
!rm -rf /kaggle/working/yolov5_v1_out1.zip
%cd ..
! zip -r yolov5_v1_out1.zip ./yolov5
python
!python detect.py --weights /kaggle/working/yolov5/runs/train/exp2_50ep2/weights/best.pt --conf 0.25 --source "https://www.youtube.com/watch?v=MNn9qKG2UFI&pp=ygUZdHJhZmZpYyByb2FkIGNhbWVyYSBtb3ZpZQ%3D%3D"
[34m[1mdetect: [0mweights=['/kaggle/working/yolov5/runs/train/exp2_50ep2/weights/best.pt'], source=https://www.youtube.com/watch?v=MNn9qKG2UFI&pp=ygUZdHJhZmZpYyByb2FkIGNhbWVyYSBtb3ZpZQ%3D%3D, 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_csv=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, vid_stride=1
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
Fusing layers...
Model summary: 212 layers, 20889303 parameters, 0 gradients, 48.0 GFLOPs
WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()
1/1: https://www.youtube.com/watch?v=MNn9qKG2UFI&pp=ygUZdHJhZmZpYyByb2FkIGNhbWVyYSBtb3ZpZQ%3D%3D... Success (9184 frames 1280x720 at 30.00 FPS)
Traceback (most recent call last):
File "/kaggle/working/yolov5/detect.py", line 312, in <module>
main(opt)
File "/kaggle/working/yolov5/detect.py", line 307, in main
run(**vars(opt))
File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/kaggle/working/yolov5/detect.py", line 134, in run
for path, im, im0s, vid_cap, s in dataset:
File "/kaggle/working/yolov5/utils/dataloaders.py", line 505, in __next__
if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit
cv2.error: OpenCV(4.10.0) /io/opencv/modules/highgui/src/window.cpp:1367: error: (-2:Unspecified error) The function is not implemented. Rebuild the library with Windows, GTK+ 2.x or Cocoa support. If you are on Ubuntu or Debian, install libgtk2.0-dev and pkg-config, then re-run cmake or configure script in function 'cvWaitKey'
terminate called without an active exception
```python visdrone_yaml = """ path : /kaggle/input/visdrone2019 train : /kaggle/input/visdrone2019/images/train val : /kaggle/input/visdrone2019/images/val names:
0: pedes #pedestrian
1: people
2: bicycle
3: car
4: van
5: truck
6: tricycle
7: awntric #awning-tricycle
8: bus
9: motor download: | from utils.general import download, Path
# Download labels segments = False # segment or box labels dir = Path(yaml['path']) # dataset root dir url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels download(urls, dir=dir.parent)
# Download data urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) download(urls, dir=dir / 'images', threads=3)
"""
Save the changes to a new YAML file
with open('/kaggle/working/yolov5/data/cocoCastom.yaml', 'w') as file: file.write(visdrone_yaml)
```
python
!python val.py --weights /kaggle/working/yolov5/runs/train/exp3_60ep/weights/best.pt --data cocoCastom.yaml --img 640 --half --verbose
[Errno 2] No such file or directory: './yolov5'
/kaggle/working/yolov5
[34m[1mval: [0mdata=/kaggle/working/yolov5/data/cocoCastom.yaml, weights=['/kaggle/working/yolov5/runs/train/exp3_60ep/weights/best.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=True, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
Fusing layers...
Model summary: 212 layers, 20889303 parameters, 0 gradients, 48.0 GFLOPs
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100%|████████████████████████████████████████| 755k/755k [00:00<00:00, 68.7MB/s]
[34m[1mval: [0mScanning /kaggle/input/visdrone2019/labels/val... 548 images, 0 backgrounds[0m
[34m[1mval: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/val.cache.npy'
Class Images Instances P R mAP50 WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50
all 548 38759 0.504 0.36 0.369 0.216
pedes 548 8844 0.559 0.414 0.447 0.204
people 548 5125 0.508 0.309 0.328 0.124
bicycle 548 1287 0.326 0.176 0.16 0.0622
car 548 14064 0.702 0.73 0.759 0.531
van 548 1975 0.477 0.4 0.392 0.279
truck 548 750 0.559 0.35 0.363 0.241
tricycle 548 1045 0.419 0.227 0.221 0.118
awntric 548 532 0.272 0.139 0.124 0.0797
bus 548 251 0.669 0.458 0.506 0.349
motor 548 4886 0.549 0.393 0.394 0.17
Speed: 0.1ms pre-process, 4.6ms inference, 14.2ms NMS per image at shape (32, 3, 640, 640)
Results saved to [1mruns/val/exp4[0m
python
!python detect.py --weights /kaggle/working/yolov5/runs/train/exp2_50ep2/weights/best.pt --conf 0.25 --source /kaggle/input/testvideo/4K\ Road\ traffic\ video\ for\ object\ detection\ and\ tracking\ -\ free\ download\ now.mp4
python
!python detect.py --weights /kaggle/working/yolov5/runs/train/exp3_60ep/weights/best.pt --conf 0.25 --source /kaggle/input/testvideo/4K\ Road\ traffic\ video\ for\ object\ detection\ and\ tracking\ -\ free\ download\ now.mp4
python
!pkill jupyter
^C
python
!ls
%cd ./yolov5
state.db yolov5 yolov5_v1_out1.zip
/kaggle/working/yolov5
python
!mv /kaggle/input/visdrone-dataset/VisDrone2019-DET-val/VisDrone2019-DET-val/* /kaggle/input/visdrone-dataset/VisDrone2019-DET-val/
mv: cannot move '/kaggle/input/visdrone-dataset/VisDrone2019-DET-val/VisDrone2019-DET-val/annotations' to '/kaggle/input/visdrone-dataset/VisDrone2019-DET-val/annotations': Read-only file system
mv: cannot move '/kaggle/input/visdrone-dataset/VisDrone2019-DET-val/VisDrone2019-DET-val/images' to '/kaggle/input/visdrone-dataset/VisDrone2019-DET-val/images': Read-only file system
```python visdrone_yaml = """
Ultralytics YOLO 🚀, AGPL-3.0 license
VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
Example usage: yolo train data=VisDrone.yaml
parent
├── ultralytics
└── datasets
└── VisDrone ← downloads here (2.3 GB)# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: D:\detection\visdrone # dataset root dir
train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
test: VisDrone2019-DET-test_dev/images # test images (optional) 1610 images# Classes
Classes
names: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8: bus 9: motor
"""
Save the changes to a new YAML file
with open('/kaggle/working/yolov5/data/cocoCastomDS2.yaml', 'w') as file: file.write(visdroneyaml) ```
python
!rm -rf /kaggle/working/yolov5/runs/train/exp3_60ep*
python
!python train.py --data cocoCastom.yaml --weights /kaggle/working/yolov5/runs/train/exp2_50ep2/weights/last.pt --epochs 60 --cache --name exp3_60ep
[Errno 2] No such file or directory: './yolov5'
/kaggle/working/yolov5
[34m[1mwandb[0m: WARNING ⚠️ wandb is deprecated and will be removed in a future release. See supported integrations at https://github.com/ultralytics/yolov5#integrations.
2024-06-11 08:06:05.148417: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-06-11 08:06:05.148489: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-06-11 08:06:05.150059: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
[34m[1mwandb[0m: (1) Create a W&B account
[34m[1mwandb[0m: (2) Use an existing W&B account
[34m[1mwandb[0m: (3) Don't visualize my results
[34m[1mwandb[0m: Enter your choice: (30 second timeout)
[34m[1mwandb[0m: W&B disabled due to login timeout.
[34m[1mtrain: [0mweights=/kaggle/working/yolov5/runs/train/exp2_50ep2/weights/last.pt, cfg=, data=cocoCastom.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=60, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_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=exp3_60ep, 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, ndjson_console=False, ndjson_file=False
[34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-321-g3742ab49 Python-3.10.13 torch-2.1.2 CUDA:0 (Tesla P100-PCIE-16GB, 16276MiB)
[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[1mComet: [0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
from n params module arguments
0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 2 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 4 444672 models.common.C3 [192, 192, 4]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 6 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 2 4134912 models.common.C3 [768, 768, 2]
9 -1 1 1476864 models.common.SPPF [768, 768, 5]
10 -1 1 295680 models.common.Conv [768, 384, 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 2 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 2 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 2 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 2 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 60615 models.yolo.Detect [10, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Model summary: 291 layers, 20907687 parameters, 20907687 gradients, 48.3 GFLOPs
Transferred 481/481 items from /kaggle/working/yolov5/runs/train/exp2_50ep2/weights/last.pt
[34m[1mAMP: [0mchecks passed ✅
[34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias
[34m[1malbumentations: [0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
self.pid = os.fork()
[34m[1mtrain: [0mScanning /kaggle/input/visdrone2019/labels/train... 6471 images, 0 backgr[0m
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000137_02220_d_0000163.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/0000140_00118_d_0000002.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999945_00000_d_0000114.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ /kaggle/input/visdrone2019/images/train/9999987_00000_d_0000049.jpg: 1 duplicate labels removed
[34m[1mtrain: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/train.cache.npy'
[34m[1mtrain: [0mCaching images (4.9GB ram): 100%|██████████| 6471/6471 [00:28<00:00, 227.[0m
[34m[1mval: [0mScanning /kaggle/input/visdrone2019/labels/val... 548 images, 0 backgrounds[0m
[34m[1mval: [0mWARNING ⚠️ Cache directory /kaggle/input/visdrone2019/labels is not writeable: [Errno 30] Read-only file system: '/kaggle/input/visdrone2019/labels/val.cache.npy'
[34m[1mval: [0mCaching images (0.4GB ram): 100%|██████████| 548/548 [00:03<00:00, 176.14it[0m
[34m[1mAutoAnchor: [0m5.73 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp3_60ep/labels.jpg...
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to [1mruns/train/exp3_60ep[0m
Starting training for 60 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/59 7.11G 0.08841 0.1387 0.01961 293 640: 1
Class Images Instances P R mAP50
all 548 38759 0.489 0.372 0.377 0.216
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/59 7.11G 0.0895 0.1403 0.0198 641 640: 1
Class Images Instances P R mAP50
all 548 38759 0.485 0.369 0.37 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/59 7.12G 0.09083 0.1452 0.02057 483 640: 1
Class Images Instances P R mAP50
all 548 38759 0.464 0.366 0.354 0.194
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/59 7.12G 0.09191 0.1496 0.02164 820 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.361 0.36 0.201
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/59 7.12G 0.09195 0.1492 0.02182 675 640: 1
Class Images Instances P R mAP50
all 548 38759 0.469 0.353 0.352 0.193
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
5/59 7.12G 0.09221 0.1509 0.02192 402 640: 1
Class Images Instances P R mAP50
all 548 38759 0.449 0.355 0.347 0.191
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
6/59 7.12G 0.09217 0.1485 0.02183 481 640: 1
Class Images Instances P R mAP50
all 548 38759 0.459 0.357 0.35 0.193
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
7/59 7.12G 0.09211 0.1499 0.02197 579 640: 1
Class Images Instances P R mAP50
all 548 38759 0.468 0.356 0.356 0.197
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
8/59 7.12G 0.09155 0.149 0.02186 775 640: 1
Class Images Instances P R mAP50
all 548 38759 0.456 0.357 0.351 0.196
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
9/59 7.12G 0.09166 0.1488 0.02172 449 640: 1
Class Images Instances P R mAP50
all 548 38759 0.459 0.367 0.359 0.199
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
10/59 7.12G 0.09127 0.1475 0.02156 326 640: 1
Class Images Instances P R mAP50
all 548 38759 0.464 0.359 0.354 0.195
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
11/59 7.12G 0.09131 0.147 0.02159 260 640: 1
Class Images Instances P R mAP50
all 548 38759 0.475 0.353 0.357 0.2
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
12/59 7.12G 0.09106 0.1474 0.02131 643 640: 1
Class Images Instances P R mAP50
all 548 38759 0.475 0.363 0.363 0.202
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
13/59 7.12G 0.09097 0.148 0.02136 612 640: 1
Class Images Instances P R mAP50
all 548 38759 0.474 0.364 0.362 0.202
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
14/59 7.12G 0.0909 0.1459 0.02125 670 640: 1
Class Images Instances P R mAP50
all 548 38759 0.475 0.359 0.355 0.197
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
15/59 7.12G 0.09092 0.1472 0.02119 375 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.36 0.36 0.203
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
16/59 7.12G 0.09082 0.1458 0.02112 555 640: 1
Class Images Instances P R mAP50
all 548 38759 0.468 0.367 0.363 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
17/59 7.12G 0.09044 0.1445 0.021 575 640: 1
Class Images Instances P R mAP50
all 548 38759 0.471 0.362 0.359 0.203
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
18/59 7.12G 0.09086 0.1457 0.02095 710 640: 1
Class Images Instances P R mAP50
all 548 38759 0.493 0.355 0.36 0.202
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
19/59 7.12G 0.09042 0.1476 0.02086 770 640: 1
Class Images Instances P R mAP50
all 548 38759 0.474 0.361 0.362 0.204
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
20/59 7.12G 0.09042 0.1448 0.02067 572 640: 1
Class Images Instances P R mAP50
all 548 38759 0.481 0.358 0.362 0.204
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
21/59 7.12G 0.0903 0.1448 0.0208 621 640: 1
Class Images Instances P R mAP50
all 548 38759 0.467 0.372 0.367 0.207
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
22/59 7.12G 0.09013 0.1441 0.02062 717 640: 1
Class Images Instances P R mAP50
all 548 38759 0.48 0.359 0.363 0.206
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
23/59 7.12G 0.08984 0.143 0.02063 661 640: 1
Class Images Instances P R mAP50
all 548 38759 0.478 0.362 0.36 0.205
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
24/59 7.12G 0.08993 0.1438 0.02053 316 640: 1
Class Images Instances P R mAP50
all 548 38759 0.472 0.367 0.361 0.206
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
25/59 7.12G 0.09 0.1439 0.02056 715 640: 1
Class Images Instances P R mAP50
all 548 38759 0.48 0.371 0.369 0.209
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
26/59 7.12G 0.08973 0.1443 0.02025 684 640: 1
Class Images Instances P R mAP50
all 548 38759 0.484 0.365 0.367 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
27/59 7.12G 0.08972 0.1419 0.02015 622 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.367 0.373 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
28/59 7.12G 0.08928 0.1433 0.02016 436 640: 1
Class Images Instances P R mAP50
all 548 38759 0.488 0.368 0.369 0.208
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
29/59 7.12G 0.08935 0.1427 0.02023 624 640: 1
Class Images Instances P R mAP50
all 548 38759 0.492 0.364 0.369 0.21
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
30/59 7.12G 0.08953 0.1425 0.02004 425 640: 1
Class Images Instances P R mAP50
all 548 38759 0.489 0.373 0.373 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
31/59 7.12G 0.08921 0.1414 0.01994 471 640: 1
Class Images Instances P R mAP50
all 548 38759 0.488 0.37 0.373 0.212
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
32/59 7.12G 0.08923 0.1425 0.0198 752 640: 1
Class Images Instances P R mAP50
all 548 38759 0.49 0.372 0.374 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
33/59 7.12G 0.08924 0.1407 0.02001 379 640: 1
Class Images Instances P R mAP50
all 548 38759 0.5 0.371 0.378 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
34/59 7.12G 0.08928 0.1424 0.01986 617 640: 1
Class Images Instances P R mAP50
all 548 38759 0.491 0.372 0.375 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
35/59 7.12G 0.08875 0.141 0.01978 536 640: 1
Class Images Instances P R mAP50
all 548 38759 0.501 0.376 0.379 0.216
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
36/59 7.12G 0.08947 0.1404 0.01971 401 640: 1
Class Images Instances P R mAP50
all 548 38759 0.489 0.366 0.372 0.213
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
37/59 7.12G 0.08862 0.1423 0.0196 691 640: 1
Class Images Instances P R mAP50
all 548 38759 0.496 0.366 0.375 0.216
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
38/59 7.12G 0.08881 0.1404 0.01956 574 640: 1
Class Images Instances P R mAP50
all 548 38759 0.506 0.366 0.374 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
39/59 7.12G 0.08852 0.1397 0.01951 750 640: 1
Class Images Instances P R mAP50
all 548 38759 0.502 0.374 0.379 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
40/59 7.12G 0.08914 0.1392 0.01958 841 640: 1
Class Images Instances P R mAP50
all 548 38759 0.497 0.368 0.375 0.214
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
41/59 7.12G 0.0883 0.1386 0.01936 576 640: 1
Class Images Instances P R mAP50
all 548 38759 0.491 0.37 0.377 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
42/59 7.12G 0.08881 0.1415 0.01933 589 640: 1
Class Images Instances P R mAP50
all 548 38759 0.506 0.368 0.377 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
43/59 7.12G 0.08858 0.1366 0.01914 315 640: 1
Class Images Instances P R mAP50
all 548 38759 0.501 0.37 0.378 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
44/59 7.12G 0.08827 0.1388 0.01914 792 640: 1
Class Images Instances P R mAP50
all 548 38759 0.498 0.375 0.379 0.218
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
45/59 7.12G 0.08809 0.1385 0.01908 651 640: 1
Class Images Instances P R mAP50
all 548 38759 0.493 0.379 0.377 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
46/59 7.12G 0.08864 0.138 0.01912 336 640: 1
Class Images Instances P R mAP50
all 548 38759 0.497 0.38 0.379 0.217
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
47/59 7.12G 0.08806 0.1392 0.01902 384 640: 1
Class Images Instances P R mAP50
all 548 38759 0.503 0.372 0.378 0.218
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
48/59 7.12G 0.08793 0.1383 0.019 500 640: 1
Class Images Instances P R mAP50
all 548 38759 0.497 0.376 0.38 0.218
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
49/59 7.12G 0.08762 0.1353 0.01883 628 640: 1
Class Images Instances P R mAP50
all 548 38759 0.511 0.368 0.379 0.218
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
50/59 7.12G 0.08787 0.1374 0.01892 350 640: 1
Class Images Instances P R mAP50
all 548 38759 0.499 0.374 0.382 0.22
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
51/59 7.12G 0.0879 0.1381 0.01866 465 640: 1
Class Images Instances P R mAP50
all 548 38759 0.498 0.371 0.379 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
52/59 7.12G 0.08789 0.1379 0.01871 542 640: 1
Class Images Instances P R mAP50
all 548 38759 0.497 0.374 0.38 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
53/59 7.12G 0.08842 0.1382 0.01874 480 640: 1
Class Images Instances P R mAP50
all 548 38759 0.509 0.373 0.38 0.219
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
54/59 7.12G 0.08772 0.1359 0.0186 658 640: 1
Class Images Instances P R mAP50
all 548 38759 0.513 0.37 0.38 0.221
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
55/59 7.12G 0.08729 0.1365 0.01855 295 640: 1
Class Images Instances P R mAP50
all 548 38759 0.505 0.371 0.381 0.221
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
56/59 7.12G 0.08781 0.1369 0.01852 455 640: 1
Class Images Instances P R mAP50
all 548 38759 0.511 0.37 0.382 0.22
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
57/59 7.12G 0.0875 0.1369 0.01849 898 640: 1
Class Images Instances P R mAP50
all 548 38759 0.51 0.372 0.382 0.221
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
58/59 7.12G 0.08758 0.1367 0.0184 502 640: 1
Class Images Instances P R mAP50
all 548 38759 0.508 0.373 0.383 0.222
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
59/59 7.12G 0.08756 0.1357 0.01832 656 640: 1
Class Images Instances P R mAP50
all 548 38759 0.507 0.372 0.381 0.221
60 epochs completed in 3.332 hours.
Optimizer stripped from runs/train/exp3_60ep/weights/last.pt, 42.2MB
Optimizer stripped from runs/train/exp3_60ep/weights/best.pt, 42.2MB
Validating runs/train/exp3_60ep/weights/best.pt...
Fusing layers...
Model summary: 212 layers, 20889303 parameters, 0 gradients, 48.0 GFLOPs
Class Images Instances P R mAP50 WARNING ⚠️ NMS time limit 2.100s exceeded
Class Images Instances P R mAP50
all 548 38759 0.504 0.366 0.374 0.218
pedes 548 8844 0.558 0.416 0.449 0.205
people 548 5125 0.507 0.321 0.338 0.127
bicycle 548 1287 0.328 0.179 0.161 0.0628
car 548 14064 0.702 0.734 0.763 0.533
van 548 1975 0.477 0.402 0.394 0.28
truck 548 750 0.556 0.353 0.365 0.243
tricycle 548 1045 0.42 0.237 0.228 0.122
awntric 548 532 0.278 0.147 0.127 0.0823
bus 548 251 0.667 0.462 0.512 0.353
motor 548 4886 0.552 0.407 0.406 0.175
Results saved to [1mruns/train/exp3_60ep[0m
python
! zip -r /kaggle/working/yolov5_v1_out2.zip /kaggle/working/yolov5
Owner
- Name: void
- Login: zash13
- Kind: user
- Repositories: 1
- Profile: https://github.com/zash13
Citation (CITATION.cff)
cff-version: 1.2.0
preferred-citation:
type: software
message: If you use YOLOv5, please cite it as below.
authors:
- family-names: Jocher
given-names: Glenn
orcid: "https://orcid.org/0000-0001-5950-6979"
title: "YOLOv5 by Ultralytics"
version: 7.0
doi: 10.5281/zenodo.3908559
date-released: 2020-5-29
license: AGPL-3.0
url: "https://github.com/ultralytics/yolov5"