From 104a2f5da80272b4d3ffbc4d1f73c53dc7cb3cfd Mon Sep 17 00:00:00 2001
From: Ivan Chekanov <ischekanov@miem.hse.ru>
Date: Tue, 22 Oct 2024 23:37:26 +0300
Subject: [PATCH] Update solution
---
lab-2/result.ipynb | 3014 ++++++++++++++++++++++++++++++++++++--------
1 file changed, 2459 insertions(+), 555 deletions(-)
diff --git a/lab-2/result.ipynb b/lab-2/result.ipynb
index c68c16e..bee3490 100644
--- a/lab-2/result.ipynb
+++ b/lab-2/result.ipynb
@@ -2,23 +2,610 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Requirement already satisfied: opencv-python in /Users/ischknv/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages (4.10.0.84)\n",
- "Requirement already satisfied: numpy>=1.21.2 in /Users/ischknv/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages (from opencv-python) (2.1.1)\n",
- "\u001b[31mERROR: Could not find a version that satisfies the requirement PIL (from versions: none)\u001b[0m\u001b[31m\n",
- "\u001b[0m\u001b[31mERROR: No matching distribution found for PIL\u001b[0m\u001b[31m\n",
- "\u001b[0m"
+ "Collecting opencv-python\n",
+ " Using cached opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl.metadata (20 kB)\n",
+ "Collecting numpy>=1.21.2 (from opencv-python)\n",
+ " Using cached numpy-2.1.2-cp310-cp310-win_amd64.whl.metadata (59 kB)\n",
+ "Using cached opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl (38.8 MB)\n",
+ "Using cached numpy-2.1.2-cp310-cp310-win_amd64.whl (12.9 MB)\n",
+ "Installing collected packages: numpy, opencv-python\n",
+ "Successfully installed numpy-2.1.2 opencv-python-4.10.0.84\n",
+ "Looking in indexes: https://download.pytorch.org/whl/cu124\n",
+ "Collecting torch\n",
+ " Downloading https://download.pytorch.org/whl/cu124/torch-2.5.0%2Bcu124-cp310-cp310-win_amd64.whl (2510.8 MB)\n",
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+ " ----------------- ---------------------- 1.1/2.5 GB 22.9 MB/s eta 0:01:01\n",
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+ " ------------------ --------------------- 1.1/2.5 GB 23.0 MB/s eta 0:01:00\n",
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+ " ------------------ --------------------- 1.2/2.5 GB 23.2 MB/s eta 0:00:58\n",
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+ " ------------------ --------------------- 1.2/2.5 GB 23.1 MB/s eta 0:00:58\n",
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+ " ------------------- -------------------- 1.2/2.5 GB 23.5 MB/s eta 0:00:56\n",
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+ " ------------------- -------------------- 1.2/2.5 GB 23.4 MB/s eta 0:00:56\n",
+ " ------------------- -------------------- 1.2/2.5 GB 23.3 MB/s eta 0:00:56\n",
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+ " ------------------- -------------------- 1.2/2.5 GB 23.3 MB/s eta 0:00:56\n",
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+ " ------------------- -------------------- 1.2/2.5 GB 23.2 MB/s eta 0:00:55\n",
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+ " ------------------- -------------------- 1.2/2.5 GB 22.7 MB/s eta 0:00:56\n",
+ " ------------------- -------------------- 1.2/2.5 GB 22.8 MB/s eta 0:00:56\n",
+ " ------------------- -------------------- 1.3/2.5 GB 22.8 MB/s eta 0:00:56\n",
+ " -------------------- ------------------- 1.3/2.5 GB 22.7 MB/s eta 0:00:56\n",
+ " -------------------- ------------------- 1.3/2.5 GB 22.7 MB/s eta 0:00:55\n",
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+ " -------------------- ------------------- 1.3/2.5 GB 22.8 MB/s eta 0:00:55\n",
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+ " -------------------- ------------------- 1.3/2.5 GB 22.7 MB/s eta 0:00:54\n",
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+ " -------------------- ------------------- 1.3/2.5 GB 22.6 MB/s eta 0:00:54\n",
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+ " -------------------- ------------------- 1.3/2.5 GB 22.7 MB/s eta 0:00:53\n",
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+ "Collecting torchvision\n",
+ " Downloading https://download.pytorch.org/whl/cu124/torchvision-0.20.0%2Bcu124-cp310-cp310-win_amd64.whl (6.1 MB)\n",
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+ "Collecting torchaudio\n",
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+ "Collecting filelock (from torch)\n",
+ " Downloading https://download.pytorch.org/whl/filelock-3.13.1-py3-none-any.whl (11 kB)\n",
+ "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\maxch\\desktop\\aimm\\.venv\\lib\\site-packages (from torch) (4.12.2)\n",
+ "Collecting networkx (from torch)\n",
+ " Downloading https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
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+ "Collecting sympy==1.13.1 (from torch)\n",
+ " Downloading https://download.pytorch.org/whl/sympy-1.13.1-py3-none-any.whl (6.2 MB)\n",
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+ "Collecting mpmath<1.4,>=1.1.0 (from sympy==1.13.1->torch)\n",
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+ "Collecting MarkupSafe>=2.0 (from jinja2->torch)\n",
+ " Downloading https://download.pytorch.org/whl/MarkupSafe-2.1.5-cp310-cp310-win_amd64.whl (17 kB)\n",
+ "Installing collected packages: mpmath, sympy, pillow, networkx, MarkupSafe, fsspec, filelock, jinja2, torch, torchvision, torchaudio\n",
+ "Successfully installed MarkupSafe-2.1.5 filelock-3.13.1 fsspec-2024.2.0 jinja2-3.1.3 mpmath-1.3.0 networkx-3.2.1 pillow-10.2.0 sympy-1.13.1 torch-2.5.0+cu124 torchaudio-2.5.0+cu124 torchvision-0.20.0+cu124\n"
]
}
],
"source": [
- "!python -m pip install opencv-python"
+ "!python -m pip install opencv-python\n",
+ "!python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124"
]
},
{
@@ -65,7 +652,16 @@
"metadata": {
"id": "BAyQTQKZ9IIN"
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mНе удалось запустить ядро, так как среда Python \".venv (Python 3.10.11)\" больше не доступна. Рассмотрите возможность выбрать другое ядро или обновить список сред Python."
+ ]
+ }
+ ],
"source": [
"import torch\n",
"from sam2.sam2_image_predictor import SAM2ImagePredictor\n",
@@ -167,7 +763,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -601,62 +1197,96 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
- "To disable this warning, you can either:\n",
- "\t- Avoid using `tokenizers` before the fork if possible\n",
- "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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+ "Installing collected packages: pytz, py-cpuinfo, urllib3, tzdata, tqdm, scipy, pyyaml, pyparsing, kiwisolver, idna, fonttools, cycler, contourpy, charset-normalizer, certifi, requests, pandas, matplotlib, ultralytics-thop, seaborn, ultralytics\n",
+ "Successfully installed certifi-2024.8.30 charset-normalizer-3.4.0 contourpy-1.3.0 cycler-0.12.1 fonttools-4.54.1 idna-3.10 kiwisolver-1.4.7 matplotlib-3.9.2 pandas-2.2.3 py-cpuinfo-9.0.0 pyparsing-3.2.0 pytz-2024.2 pyyaml-6.0.2 requests-2.32.3 scipy-1.14.1 seaborn-0.13.2 tqdm-4.66.5 tzdata-2024.2 ultralytics-8.3.20 ultralytics-thop-2.0.9 urllib3-2.2.3\n"
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}
],
@@ -666,7 +1296,7 @@
},
{
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- "execution_count": 96,
+ "execution_count": 4,
"metadata": {},
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"source": [
@@ -676,32 +1306,15 @@
},
{
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- "execution_count": 99,
+ "execution_count": 7,
"metadata": {},
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{
"name": "stdout",
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"text": [
- "New https://pypi.org/project/ultralytics/8.3.20 available рџѓ Update with 'pip install -U ultralytics'\n",
- "Ultralytics 8.3.19 рџљЂ Python-3.12.7 torch-2.5.0 MPS (Apple M3)\n",
- "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.pt, data=/Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=mps, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, 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=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=/Users/ischknv/Documents/GitHub/miem/aimm/runs/detect/train\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
- "To disable this warning, you can either:\n",
- "\t- Avoid using `tokenizers` before the fork if possible\n",
- "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "Ultralytics 8.3.20 Python-3.10.11 torch-2.5.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2060, 6144MiB)\n",
+ "\u001b[34m\u001b[1mengine\\trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.pt, data=c:\\Users\\maxch\\Desktop\\aimm\\lab-2/dataset/data.yaml, epochs=50, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=cuda, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, 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=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=c:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\n",
"Overriding model.yaml nc=80 with nc=2\n",
"\n",
" from n params module arguments \n",
@@ -731,481 +1344,43 @@
"Model summary: 225 layers, 11,136,374 parameters, 11,136,358 gradients, 28.6 GFLOPs\n",
"\n",
"Transferred 349/355 items from pretrained weights\n",
- "Freezing layer 'model.22.dfl.conv.weight'\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
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- "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/train... 565 images, 0 backgrounds, 426 corrupt: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 565/565 [00:00<00:00, 5707.18it/s]"
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- "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING вљ пёЏ /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/train/0564.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [469.84412 279.38965 285.4933 548.4157 ]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING вљ пёЏ /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/train/0565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [330.82208 305.52722 374.75528 473.72696]\n",
- "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/train.cache\n"
+ "Freezing layer 'model.22.dfl.conv.weight'\n",
+ "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
+ "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
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- "\u001b[34m\u001b[1mval: \u001b[0mScanning /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/val... 139 images, 0 backgrounds, 0 corrupt: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 139/139 [00:00<00:00, 5359.32it/s]"
+ "100.0%\n"
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- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /Users/ischknv/Documents/GitHub/miem/aimm/lab-2/dataset/val.cache\n"
+ "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed \n"
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+ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning C:\\Users\\maxch\\Desktop\\aimm\\lab-2\\dataset\\train.cache... 565 images, 0 backgrounds, 0 corrupt: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 565/565 [00:00<?, ?it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning C:\\Users\\maxch\\Desktop\\aimm\\lab-2\\dataset\\val.cache... 139 images, 0 backgrounds, 0 corrupt: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 139/139 [00:00<?, ?it/s]\n"
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"text": [
- "Plotting labels to /Users/ischknv/Documents/GitHub/miem/aimm/runs/detect/train/labels.jpg... \n",
+ "Plotting labels to c:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\\labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
"Image sizes 640 train, 640 val\n",
- "Using 0 dataloader workers\n",
- "Logging results to \u001b[1m/Users/ischknv/Documents/GitHub/miem/aimm/runs/detect/train\u001b[0m\n",
+ "Using 6 dataloader workers\n",
+ "Logging results to \u001b[1mc:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\u001b[0m\n",
"Starting training for 50 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
@@ -1215,34 +1390,1762 @@
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- " 0%| | 0/9 [00:10<?, ?it/s]\n"
+ " 1/50 4.12G 0.8632 2.393 1.392 6 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:10<00:00, 3.53it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 3.79it/s]"
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- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[99], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mHOME\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/dataset/data.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmps\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/engine/model.py:802\u001b[0m, in \u001b[0;36mModel.train\u001b[0;34m(self, trainer, **kwargs)\u001b[0m\n\u001b[1;32m 799\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mmodel\n\u001b[1;32m 801\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mhub_session \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msession \u001b[38;5;66;03m# attach optional HUB session\u001b[39;00m\n\u001b[0;32m--> 802\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 803\u001b[0m \u001b[38;5;66;03m# Update model and cfg after training\u001b[39;00m\n\u001b[1;32m 804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m}:\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py:207\u001b[0m, in \u001b[0;36mBaseTrainer.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 204\u001b[0m ddp_cleanup(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28mstr\u001b[39m(file))\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 207\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mworld_size\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/engine/trainer.py:385\u001b[0m, in \u001b[0;36mBaseTrainer._do_train\u001b[0;34m(self, world_size)\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m autocast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mamp):\n\u001b[1;32m 384\u001b[0m batch \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreprocess_batch(batch)\n\u001b[0;32m--> 385\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss_items \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m RANK \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m world_size\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/nn/tasks.py:111\u001b[0m, in \u001b[0;36mBaseModel.forward\u001b[0;34m(self, x, *args, **kwargs)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124;03mPerform forward pass of the model for either training or inference.\u001b[39;00m\n\u001b[1;32m 99\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;124;03m (torch.Tensor): Loss if x is a dict (training), or network predictions (inference).\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m): \u001b[38;5;66;03m# for cases of training and validating while training.\u001b[39;00m\n\u001b[0;32m--> 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(x, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/nn/tasks.py:293\u001b[0m, in \u001b[0;36mBaseModel.loss\u001b[0;34m(self, batch, preds)\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcriterion \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit_criterion()\n\u001b[1;32m 292\u001b[0m preds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mforward(batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mimg\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;28;01mif\u001b[39;00m preds \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m preds\n\u001b[0;32m--> 293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/utils/loss.py:253\u001b[0m, in \u001b[0;36mv8DetectionLoss.__call__\u001b[0;34m(self, preds, batch)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fg_mask\u001b[38;5;241m.\u001b[39msum():\n\u001b[1;32m 252\u001b[0m target_bboxes \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m stride_tensor\n\u001b[0;32m--> 253\u001b[0m loss[\u001b[38;5;241m0\u001b[39m], loss[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbbox_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[43mpred_distri\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43manchor_points\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_bboxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_scores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_scores_sum\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfg_mask\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 257\u001b[0m loss[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhyp\u001b[38;5;241m.\u001b[39mbox \u001b[38;5;66;03m# box gain\u001b[39;00m\n\u001b[1;32m 258\u001b[0m loss[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhyp\u001b[38;5;241m.\u001b[39mcls \u001b[38;5;66;03m# cls gain\u001b[39;00m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
- "File \u001b[0;32m~/Documents/GitHub/miem/aimm/.venv/lib/python3.12/site-packages/ultralytics/utils/loss.py:103\u001b[0m, in \u001b[0;36mBboxLoss.forward\u001b[0;34m(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask)\u001b[0m\n\u001b[1;32m 101\u001b[0m weight \u001b[38;5;241m=\u001b[39m target_scores\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)[fg_mask]\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 102\u001b[0m iou \u001b[38;5;241m=\u001b[39m bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, CIoU\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 103\u001b[0m loss_iou \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43miou\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m/\u001b[39m target_scores_sum\n\u001b[1;32m 105\u001b[0m \u001b[38;5;66;03m# DFL loss\u001b[39;00m\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdfl_loss:\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.415 0.56 0.408 0.246\n"
]
- }
- ],
- "source": [
- "model.train(data=f\"{HOME}/dataset/data.yaml\", device=\"mps\", batch=16, epochs=50)"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 2/50 4.04G 0.9583 1.467 1.405 12 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.01it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.02it/s]"
+ ]
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+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.187 0.587 0.28 0.13\n"
+ ]
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+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 3/50 4.18G 1.025 1.395 1.437 11 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.03it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.596 0.0779 0.041 0.0104\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 4/50 4.05G 1.127 1.418 1.515 11 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.14it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.06it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.653 0.287 0.169 0.0754\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 5/50 4.05G 1.12 1.329 1.519 10 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.06it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.111 0.275 0.155 0.0764\n"
+ ]
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 6/50 4.04G 1.043 1.266 1.433 9 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 3.96it/s]"
+ ]
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.667 0.0779 0.0642 0.0348\n"
+ ]
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 7/50 4.05G 1.018 1.245 1.426 11 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.08it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.02it/s]"
+ ]
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.806 0.332 0.386 0.268\n"
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+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 8/50 3.88G 0.961 1.214 1.385 13 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.02it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.425 0.405 0.408 0.239\n"
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+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 9/50 4.05G 0.9582 1.205 1.381 14 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.01it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.488 0.67 0.567 0.407\n"
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+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 10/50 4.05G 0.9233 1.108 1.36 11 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.09it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 3.80it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.273 0.644 0.502 0.353\n"
+ ]
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 11/50 3.89G 0.9202 1.08 1.343 13 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.07it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 4.05it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.52 0.626 0.625 0.46\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 12/50 3.89G 0.9089 1.076 1.342 10 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.10it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 3.87it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.523 0.529 0.522 0.373\n"
+ ]
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+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 13/50 3.88G 0.832 0.9542 1.304 17 640: 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 36/36 [00:08<00:00, 4.01it/s]\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|в–€в–€в–€в–€в–€в–€в–€в–€в–€в–€| 5/5 [00:01<00:00, 3.97it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " all 139 139 0.405 0.636 0.487 0.38\n"
+ ]
+ },
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+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ "text": [
+ "\n",
+ "50 epochs completed in 0.156 hours.\n",
+ "Optimizer stripped from c:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\\weights\\last.pt, 22.5MB\n",
+ "Optimizer stripped from c:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\\weights\\best.pt, 22.5MB\n",
+ "\n",
+ "Validating c:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\\weights\\best.pt...\n",
+ "Ultralytics 8.3.20 Python-3.10.11 torch-2.5.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2060, 6144MiB)\n",
+ "Model summary (fused): 168 layers, 11,126,358 parameters, 0 gradients, 28.4 GFLOPs\n"
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+ "Speed: 0.3ms preprocess, 4.4ms inference, 0.0ms loss, 1.9ms postprocess per image\n",
+ "Results saved to \u001b[1mc:\\Users\\maxch\\Desktop\\aimm\\runs\\detect\\train\u001b[0m\n"
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+ " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n",
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+ " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n",
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+ " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n",
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+ " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n",
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+ " [ 1, 1, 1, ..., 0.11681, 0.058406, 0]]), 'Recall', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n",
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+ " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n",
+ " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n",
+ " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n",
+ " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n",
+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.20482, 0.20482, 0.2705, ..., 0, 0, 0],\n",
+ " [ 0.64533, 0.64533, 0.72089, ..., 0, 0, 0]]), 'Confidence', 'F1'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n",
+ " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n",
+ " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n",
+ " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n",
+ " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n",
+ " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n",
+ " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n",
+ " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n",
+ " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n",
+ " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n",
+ " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n",
+ " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n",
+ " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n",
+ " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n",
+ " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n",
+ " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n",
+ " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n",
+ " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n",
+ " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n",
+ " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n",
+ " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n",
+ " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n",
+ " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n",
+ " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n",
+ " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n",
+ " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n",
+ " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n",
+ " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n",
+ " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n",
+ " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n",
+ " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n",
+ " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n",
+ " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n",
+ " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n",
+ " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n",
+ " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n",
+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.11409, 0.11409, 0.1564, ..., 1, 1, 1],\n",
+ " [ 0.47826, 0.47826, 0.56623, ..., 1, 1, 1]]), 'Confidence', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n",
+ " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n",
+ " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n",
+ " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n",
+ " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n",
+ " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n",
+ " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n",
+ " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n",
+ " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n",
+ " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n",
+ " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n",
+ " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n",
+ " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n",
+ " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n",
+ " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n",
+ " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n",
+ " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n",
+ " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n",
+ " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n",
+ " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n",
+ " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n",
+ " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n",
+ " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n",
+ " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n",
+ " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n",
+ " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n",
+ " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n",
+ " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n",
+ " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n",
+ " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n",
+ " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n",
+ " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n",
+ " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n",
+ " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n",
+ " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n",
+ " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n",
+ " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n",
+ " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n",
+ " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n",
+ " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n",
+ " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n",
+ " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0, 0, 0],\n",
+ " [ 0.9918, 0.9918, 0.9918, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n",
+ "fitness: np.float64(0.8033924789666338)\n",
+ "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
+ "maps: array([ 0.7087, 0.89085])\n",
+ "names: {0: 'kettle', 1: 'teapot'}\n",
+ "plot: True\n",
+ "results_dict: {'metrics/precision(B)': np.float64(0.8055972010864378), 'metrics/recall(B)': np.float64(0.8617328954841008), 'metrics/mAP50(B)': np.float64(0.8359362210577036), 'metrics/mAP50-95(B)': np.float64(0.7997765076231815), 'fitness': np.float64(0.8033924789666338)}\n",
+ "save_dir: WindowsPath('c:/Users/maxch/Desktop/aimm/runs/detect/train')\n",
+ "speed: {'preprocess': 0.3013267791528496, 'inference': 4.441570034987635, 'loss': 0.0, 'postprocess': 1.9155354808560379}\n",
+ "task: 'detect'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.train(data=f\"{HOME}/dataset/data.yaml\", device=\"cuda\", batch=16, epochs=50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "image 1/1 c:\\Users\\maxch\\Desktop\\aimm\\lab-2\\dataset\\train\\0002.jpg: 640x640 1 teapot, 37.9ms\n",
+ "Speed: 4.0ms preprocess, 37.9ms inference, 2.0ms postprocess per image at shape (1, 3, 640, 640)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from ultralytics import YOLO\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2\n",
+ "model_path = f\"{HOME}/../runs/detect/train/weights/best.pt\"\n",
+ "dataset_dir = f\"{HOME}/dataset/\" # путь к папке с вашим датасетом\n",
+ "model = YOLO(model_path)\n",
+ "results = model.predict(dataset_dir+\"train/0002.jpg\")\n",
+ "result = results[0]\n",
+ "res_plotted = result.plot()\n",
+ "plt.imshow(cv2.cvtColor(res_plotted, cv2.COLOR_BGR2RGB))\n",
+ "plt.axis('off')\n",
+ "plt.show()"
]
}
],
@@ -1253,7 +3156,8 @@
"provenance": []
},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": ".venv",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -1266,7 +3170,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.7"
+ "version": "3.10.11"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
--
GitLab