BOOT
<F12> / <Fn>+<F12>
Conda
https://repo.anaconda.com/archive/
https://repo.anaconda.com/archive/Anaconda3-2023.07-1-Windows-x86_64.exe
D:\anaconda3\Scripts
D:\anaconda3\Library\bin
yolov5
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
conda install git
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
D:\anaconda3\envs>conda create -n backbone5 python=3.8.20
D:\anaconda3\envs>conda init
no change D:\anaconda3\Scripts\conda.exe
no change D:\anaconda3\Scripts\conda-env.exe
no change D:\anaconda3\Scripts\conda-script.py
no change D:\anaconda3\Scripts\conda-env-script.py
no change D:\anaconda3\condabin\conda.bat
no change D:\anaconda3\Library\bin\conda.bat
no change D:\anaconda3\condabin\_conda_activate.bat
no change D:\anaconda3\condabin\rename_tmp.bat
no change D:\anaconda3\condabin\conda_auto_activate.bat
no change D:\anaconda3\condabin\conda_hook.bat
no change D:\anaconda3\Scripts\activate.bat
no change D:\anaconda3\condabin\activate.bat
no change D:\anaconda3\condabin\deactivate.bat
no change D:\anaconda3\Scripts\activate
no change D:\anaconda3\Scripts\deactivate
no change D:\anaconda3\etc\profile.d\conda.sh
no change D:\anaconda3\etc\fish\conf.d\conda.fish
no change D:\anaconda3\shell\condabin\Conda.psm1
no change D:\anaconda3\shell\condabin\conda-hook.ps1
no change D:\anaconda3\Lib\site-packages\xontrib\conda.xsh
no change D:\anaconda3\etc\profile.d\conda.csh
modified C:\Users\Administrator\Documents\WindowsPowerShell\profile.ps1
no change HKEY_CURRENT_USER\Software\Microsoft\Command Processor\AutoRun
==> For changes to take effect, close and re-open your current shell. <==
D:\anaconda3\envs>conda activate backbone5
(backbone5) D:\anaconda3\envs\backbone5>pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
Writing to C:\Users\Administrator\AppData\Roaming\pip\pip.ini
(backbone5) D:\anaconda3\envs>cd backbone5
(backbone5) D:\anaconda3\envs\backbone5>conda -V
conda 23.5.2
(backbone5) D:\anaconda3\envs\backbone5>
(backbone5) D:\anaconda3\envs\backbone5>python -V
Python 3.8.20
(backbone5) D:\anaconda3\envs\backbone5>git -v
git version 2.45.2.windows.1
(backbone5) D:\anaconda3\envs\backbone5>git clone https://github.com/ultralytics/yolov5
Cloning into 'yolov5'...
remote: Enumerating objects: 17022, done.
remote: Counting objects: 100% (217/217), done.
remote: Compressing objects: 100% (146/146), done.
remote: Total 17022 (delta 122), reused 131 (delta 71), pack-reused 16805 (from 1)
Receiving objects: 100% (17022/17022), 15.74 MiB | 3.18 MiB/s, done.
Resolving deltas: 100% (11660/11660), done.
D:\anaconda3\envs\backbone5\yolov5
├─.github
│ ├─ISSUE_TEMPLATE
│ └─workflows
├─classify
├─data
│ ├─hyps
│ ├─images
│ └─scripts
├─models
│ ├─hub
│ └─segment
├─segment
└─utils
├─aws
├─docker
├─flask_rest_api
├─google_app_engine
├─loggers
│ ├─clearml
│ ├─comet
│ └─wandb
└─segment
(backbone5) D:\anaconda3\envs\backbone5>pip install torch torchvision torchaudio
Requirement already satisfied: torch in d:\anaconda3\envs\backbone5\lib\site-packages (2.5.0)
Requirement already satisfied: torchvision in d:\anaconda3\envs\backbone5\lib\site-packages (0.20.0)
Collecting torchaudio
Downloading torchaudio-2.5.0-cp311-cp311-win_amd64.whl.metadata (6.5 kB)
Requirement already satisfied: filelock in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (3.16.1)
Requirement already satisfied: typing-extensions>=4.8.0 in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (4.12.2)
Requirement already satisfied: networkx in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (3.4.2)
Requirement already satisfied: jinja2 in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (3.1.4)
Requirement already satisfied: fsspec in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (2024.10.0)
Requirement already satisfied: sympy==1.13.1 in d:\anaconda3\envs\backbone5\lib\site-packages (from torch) (1.13.1)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in d:\anaconda3\envs\backbone5\lib\site-packages (from sympy==1.13.1->torch) (1.3.0)
Requirement already satisfied: numpy in d:\anaconda3\envs\backbone5\lib\site-packages (from torchvision) (2.1.2)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in d:\anaconda3\envs\backbone5\lib\site-packages (from torchvision) (11.0.0)
Requirement already satisfied: MarkupSafe>=2.0 in d:\anaconda3\envs\backbone5\lib\site-packages (from jinja2->torch) (3.0.2)
Downloading torchaudio-2.5.0-cp311-cp311-win_amd64.whl (2.4 MB)
---------------------------------------- 2.4/2.4 MB 3.5 MB/s eta 0:00:00
Installing collected packages: torchaudio
Successfully installed torchaudio-2.5.0
(backbone5) D:\anaconda3\envs\backbone5\yolov5>pip install -r requirements.txt
Collecting gitpython>=3.1.30 (from -r requirements.txt (line 5))
Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)
Collecting matplotlib>=3.3 (from -r requirements.txt (line 6))
Downloading matplotlib-3.9.2-cp311-cp311-win_amd64.whl.metadata (11 kB)
Collecting numpy>=1.23.5 (from -r requirements.txt (line 7))
Downloading numpy-2.1.2-cp311-cp311-win_amd64.whl.metadata (59 kB)
Collecting opencv-python>=4.1.1 (from -r requirements.txt (line 8))
Downloading opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl.metadata (20 kB)
Collecting pillow>=10.3.0 (from -r requirements.txt (line 9))
Downloading pillow-11.0.0-cp311-cp311-win_amd64.whl.metadata (9.3 kB)
Collecting psutil (from -r requirements.txt (line 10))
Downloading psutil-6.1.0-cp37-abi3-win_amd64.whl.metadata (23 kB)
Collecting PyYAML>=5.3.1 (from -r requirements.txt (line 11))
Downloading PyYAML-6.0.2-cp311-cp311-win_amd64.whl.metadata (2.1 kB)
Collecting requests>=2.32.2 (from -r requirements.txt (line 12))
Downloading requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)
Collecting scipy>=1.4.1 (from -r requirements.txt (line 13))
Downloading scipy-1.14.1-cp311-cp311-win_amd64.whl.metadata (60 kB)
Collecting thop>=0.1.1 (from -r requirements.txt (line 14))
Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)
Collecting torch>=1.8.0 (from -r requirements.txt (line 15))
Downloading torch-2.5.0-cp311-cp311-win_amd64.whl.metadata (28 kB)
Collecting torchvision>=0.9.0 (from -r requirements.txt (line 16))
Downloading torchvision-0.20.0-cp311-cp311-win_amd64.whl.metadata (6.2 kB)
Collecting tqdm>=4.66.3 (from -r requirements.txt (line 17))
Downloading tqdm-4.66.5-py3-none-any.whl.metadata (57 kB)
Collecting ultralytics>=8.2.34 (from -r requirements.txt (line 18))
Downloading ultralytics-8.3.20-py3-none-any.whl.metadata (34 kB)
Collecting pandas>=1.1.4 (from -r requirements.txt (line 27))
Downloading pandas-2.2.3-cp311-cp311-win_amd64.whl.metadata (19 kB)
Collecting seaborn>=0.11.0 (from -r requirements.txt (line 28))
Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
Requirement already satisfied: setuptools>=70.0.0 in d:\anaconda3\envs\backbone5\lib\site-packages (from -r requirements.txt (line 42)) (75.1.0)
Collecting gitdb<5,>=4.0.1 (from gitpython>=3.1.30->-r requirements.txt (line 5))
Downloading gitdb-4.0.11-py3-none-any.whl.metadata (1.2 kB)
Collecting contourpy>=1.0.1 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading contourpy-1.3.0-cp311-cp311-win_amd64.whl.metadata (5.4 kB)
Collecting cycler>=0.10 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)
Collecting fonttools>=4.22.0 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading fonttools-4.54.1-cp311-cp311-win_amd64.whl.metadata (167 kB)
Collecting kiwisolver>=1.3.1 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading kiwisolver-1.4.7-cp311-cp311-win_amd64.whl.metadata (6.4 kB)
Collecting packaging>=20.0 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading packaging-24.1-py3-none-any.whl.metadata (3.2 kB)
Collecting pyparsing>=2.3.1 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading pyparsing-3.2.0-py3-none-any.whl.metadata (5.0 kB)
Collecting python-dateutil>=2.7 (from matplotlib>=3.3->-r requirements.txt (line 6))
Downloading python_dateutil-2.9.0.post0-py2.py3-none-any.whl.metadata (8.4 kB)
Collecting charset-normalizer<4,>=2 (from requests>=2.32.2->-r requirements.txt (line 12))
Downloading charset_normalizer-3.4.0-cp311-cp311-win_amd64.whl.metadata (34 kB)
Collecting idna<4,>=2.5 (from requests>=2.32.2->-r requirements.txt (line 12))
Downloading idna-3.10-py3-none-any.whl.metadata (10 kB)
Collecting urllib3<3,>=1.21.1 (from requests>=2.32.2->-r requirements.txt (line 12))
Downloading urllib3-2.2.3-py3-none-any.whl.metadata (6.5 kB)
Collecting certifi>=2017.4.17 (from requests>=2.32.2->-r requirements.txt (line 12))
Downloading certifi-2024.8.30-py3-none-any.whl.metadata (2.2 kB)
Collecting filelock (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading filelock-3.16.1-py3-none-any.whl.metadata (2.9 kB)
Collecting typing-extensions>=4.8.0 (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB)
Collecting networkx (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB)
Collecting jinja2 (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB)
Collecting fsspec (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB)
Collecting sympy==1.13.1 (from torch>=1.8.0->-r requirements.txt (line 15))
Downloading sympy-1.13.1-py3-none-any.whl.metadata (12 kB)
Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch>=1.8.0->-r requirements.txt (line 15))
Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB)
Collecting colorama (from tqdm>=4.66.3->-r requirements.txt (line 17))
Downloading colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
Collecting py-cpuinfo (from ultralytics>=8.2.34->-r requirements.txt (line 18))
Downloading py_cpuinfo-9.0.0-py3-none-any.whl.metadata (794 bytes)
Collecting ultralytics-thop>=2.0.0 (from ultralytics>=8.2.34->-r requirements.txt (line 18))
Downloading ultralytics_thop-2.0.9-py3-none-any.whl.metadata (9.3 kB)
Collecting pytz>=2020.1 (from pandas>=1.1.4->-r requirements.txt (line 27))
Downloading pytz-2024.2-py2.py3-none-any.whl.metadata (22 kB)
Collecting tzdata>=2022.7 (from pandas>=1.1.4->-r requirements.txt (line 27))
Downloading tzdata-2024.2-py2.py3-none-any.whl.metadata (1.4 kB)
Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython>=3.1.30->-r requirements.txt (line 5))
Downloading smmap-5.0.1-py3-none-any.whl.metadata (4.3 kB)
Collecting six>=1.5 (from python-dateutil>=2.7->matplotlib>=3.3->-r requirements.txt (line 6))
Downloading six-1.16.0-py2.py3-none-any.whl.metadata (1.8 kB)
Collecting MarkupSafe>=2.0 (from jinja2->torch>=1.8.0->-r requirements.txt (line 15))
Downloading MarkupSafe-3.0.2-cp311-cp311-win_amd64.whl.metadata (4.1 kB)
train
(backbone5) D:\anaconda3\envs\backbone5\yolov5>python train.py --device cpu
Creating new Ultralytics Settings v0.0.6 file ✅
View Ultralytics Settings with 'yolo settings' or at 'C:\Users\Administrator\AppData\Roaming\Ultralytics\settings.json'
Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
train: weights=yolov5s.pt, cfg=, data=data\coco128.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, 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=None, image_weights=False, device=cpu, 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, ndjson_console=False, ndjson_file=False
github: up to date with https://github.com/ultralytics/yolov5
YOLOv5 v7.0-378-g2f74455a Python-3.11.3 torch-2.5.0+cpu CPU
hyperparameters: lr0=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
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
Dataset not found , missing paths ['D:\\anaconda3\\envs\\backbone5\\datasets\\coco128\\images\\train2017']
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip to coco128.zip...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.66M/6.66M [01:08<00:00, 102kB/s]
Dataset download success (129.5s), saved to D:\anaconda3\envs\backbone5\datasets
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf to C:\Users\Administrator\AppData\Roaming\Ultralytics\Arial.ttf...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 755k/755k [00:01<00:00, 698kB/s]
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...
95%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████ 96%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████ 96%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14.1M/14.1M [01:45<00:00, 141kB/s]
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: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs
Transferred 349/349 items from yolov5s.pt
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
train: Scanning D:\anaconda3\envs\backbone5\datasets\coco128\labels\train2017... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:10<00:00, 12.32it/s]
train: New cache created: D:\anaconda3\envs\backbone5\datasets\coco128\labels\train2017.cache
val: Scanning D:\anaconda3\envs\backbone5\datasets\coco128\labels\train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<?, ?it/s]
AutoAnchor: 4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp\labels.jpg...
D:\anaconda3\envs\backbone5\yolov5\train.py:355: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
scaler = torch.cuda.amp.GradScaler(enabled=amp)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs\train\exp
Starting training for 100 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04429 0.06439 0.01391 197 640: 12%|█▎ | 1/8 [00:22<02:34, 22.05D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04407 0.05901 0.01749 154 640: 25%|██▌ | 2/8 [00:29<01:19, 13.32D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04451 0.05735 0.01674 150 640: 38%|███▊ | 3/8 [00:36<00:51, 10.39D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04477 0.06103 0.01689 248 640: 50%|█████ | 4/8 [00:43<00:36, 9.19D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04568 0.06436 0.01707 254 640: 62%|██████▎ | 5/8 [00:50<00:25, 8.37D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04574 0.06618 0.01657 261 640: 75%|███████▌ | 6/8 [00:57<00:15, 7.98D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04622 0.06545 0.0164 196 640: 88%|████████▊ | 7/8 [01:04<00:07, 7.59D:\anaconda3\envs\backbone5\yolov5\train.py:412: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(amp):
0/99 0G 0.04545 0.06536 0.01645 195 640: 100%|██████████| 8/8 [01:11<00:00, 8.89
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 4/4 [00:16<00:00, 4.22s/it]
all 128 929 0.698 0.635 0.714 0.481
```c
```c
100 epochs completed in 1.967 hours.
Optimizer stripped from runs\train\exp\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp\weights\best.pt, 14.8MB
Validating runs\train\exp\weights\best.pt...
Fusing layers...
Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 4/4 [00:13<00:00, 3.36s/it]
all 128 929 0.89 0.933 0.957 0.773
person 128 254 0.963 0.898 0.961 0.763
bicycle 128 6 0.991 1 0.995 0.662
car 128 46 0.912 0.676 0.771 0.434
motorcycle 128 5 0.919 1 0.995 0.881
airplane 128 6 0.931 1 0.995 0.867
bus 128 7 0.857 1 0.995 0.858
train 128 3 0.891 1 0.995 0.913
truck 128 12 0.954 0.833 0.972 0.752
boat 128 6 0.898 0.833 0.972 0.597
traffic light 128 14 0.764 0.571 0.744 0.412
stop sign 128 2 0.85 1 0.995 0.846
bench 128 9 1 0.951 0.995 0.76
bird 128 16 0.978 1 0.995 0.846
cat 128 4 1 0.983 0.995 0.872
dog 128 9 0.973 1 0.995 0.862
horse 128 2 0.804 1 0.995 0.895
elephant 128 17 0.969 0.941 0.974 0.823
bear 128 1 0.759 1 0.995 0.995
zebra 128 4 0.899 1 0.995 0.995
giraffe 128 9 1 0.949 0.995 0.883
backpack 128 6 0.882 0.833 0.972 0.71
umbrella 128 18 0.962 0.944 0.98 0.783
handbag 128 19 0.916 0.842 0.879 0.61
tie 128 7 0.929 0.857 0.858 0.724
suitcase 128 4 0.911 1 0.995 0.823
frisbee 128 5 0.951 1 0.995 0.772
skis 128 1 0.72 1 0.995 0.697
snowboard 128 7 0.951 0.857 0.902 0.708
sports ball 128 6 0.955 0.833 0.837 0.477
kite 128 10 0.767 1 0.995 0.586
baseball bat 128 4 1 0.944 0.995 0.655
baseball glove 128 7 0.661 0.571 0.658 0.463
skateboard 128 5 0.917 1 0.995 0.896
tennis racket 128 7 0.791 0.714 0.823 0.669
bottle 128 18 0.889 0.778 0.903 0.651
wine glass 128 16 0.735 0.938 0.888 0.616
cup 128 36 0.929 0.972 0.975 0.758
fork 128 6 1 0.925 0.995 0.821
knife 128 16 0.839 0.875 0.947 0.622
spoon 128 22 0.956 0.988 0.989 0.685
bowl 128 28 0.909 0.857 0.889 0.738
banana 128 1 0.857 1 0.995 0.895
sandwich 128 2 0.631 1 0.995 0.945
orange 128 4 0.769 1 0.995 0.865
broccoli 128 11 0.964 1 0.995 0.788
carrot 128 24 0.959 0.983 0.992 0.788
hot dog 128 2 0.835 1 0.995 0.921
pizza 128 5 0.971 1 0.995 0.903
donut 128 14 0.966 1 0.995 0.931
cake 128 4 0.894 1 0.995 0.946
chair 128 35 0.939 0.943 0.987 0.804
couch 128 6 0.879 1 0.995 0.968
potted plant 128 14 0.97 1 0.995 0.876
bed 128 3 0.776 1 0.995 0.929
dining table 128 13 1 0.972 0.995 0.823
toilet 128 2 0.841 1 0.995 0.895
tv 128 2 0.834 1 0.995 0.946
laptop 128 3 0.676 0.667 0.669 0.468
mouse 128 2 0.837 1 0.995 0.648
remote 128 8 0.71 0.75 0.797 0.63
cell phone 128 8 0.88 0.919 0.982 0.649
microwave 128 3 0.881 1 0.995 0.931
oven 128 5 0.926 1 0.995 0.883
sink 128 6 0.91 0.833 0.872 0.71
refrigerator 128 5 0.919 1 0.995 0.912
book 128 29 0.938 0.828 0.942 0.592
clock 128 9 0.954 1 0.995 0.9
vase 128 2 0.834 1 0.995 0.846
scissors 128 1 0.913 1 0.995 0.378
teddy bear 128 21 0.923 1 0.995 0.862
toothbrush 128 5 0.91 1 0.995 0.868
Results saved to runs\train\exp
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
