{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import yaml\n", "import shutil\n", "from pathlib import Path\n", "from datetime import datetime\n", "\n", "import torch\n", "from tqdm import tqdm\n", "from ultralytics import YOLO\n", "from sklearn import model_selection" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "image_files = sorted(os.listdir(\"images\"))\n", "label_files = sorted(os.listdir(\"labels\"))\n", "train_images, valid_images, train_labels, valid_labels = (\n", " model_selection.train_test_split(image_files, label_files, test_size=0.1)\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def create_folder(\n", " image_names: list[str],\n", " label_names: list[str],\n", " img_src_dir: str,\n", " label_src_dir: str,\n", " img_dest_dir: str,\n", " label_dest_dir: str,\n", "):\n", " if os.path.exists(img_dest_dir) == True:\n", " shutil.rmtree(img_dest_dir)\n", " os.makedirs(img_dest_dir)\n", "\n", " if os.path.exists(label_dest_dir) == True:\n", " shutil.rmtree(label_dest_dir)\n", " os.makedirs(label_dest_dir)\n", "\n", " for i in tqdm(range(len(image_names))):\n", " img_path = Path(img_src_dir) / image_names[i]\n", " img_dest = Path(img_dest_dir) / image_names[i]\n", " if os.path.exists(img_dest) == False:\n", " shutil.copy(img_path, img_dest)\n", "\n", " label_path = Path(label_src_dir) / label_names[i]\n", " label_dest = Path(label_dest_dir) / label_names[i]\n", " if os.path.exists(label_dest) == False:\n", " shutil.copy(label_path, label_dest)\n", "\n", " assert img_path.stem == label_path.stem" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 690/690 [00:00<00:00, 1627.42it/s]\n", "100%|██████████| 77/77 [00:00<00:00, 1252.01it/s]\n" ] } ], "source": [ "create_folder(\n", " image_names=train_images,\n", " label_names=train_labels,\n", " img_src_dir=\"images\",\n", " label_src_dir=\"labels\",\n", " img_dest_dir=\"train/images\",\n", " label_dest_dir=\"train/labels\",\n", ")\n", "\n", "create_folder(\n", " image_names=valid_images,\n", " label_names=valid_labels,\n", " img_src_dir=\"images\",\n", " label_src_dir=\"labels\",\n", " img_dest_dir=\"valid/images\",\n", " label_dest_dir=\"valid/labels\",\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dict_file = {\n", " \"train\": f\"{Path(\"./train\").resolve()}\",\n", " \"val\": f\"{Path(\"./valid\").resolve()}\",\n", " \"nc\": 3,\n", " \"names\": {0: \"circle\", 1: \"oval\", 2: \"teardrop\"},\n", "}\n", "\n", "with open(\"data.yaml\", \"w\") as f:\n", " yaml.dump(dict_file, f)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model = YOLO(\"yolov8n.pt\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ultralytics YOLOv8.2.36 🚀 Python-3.12.3 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti SUPER, 16376MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=data.yaml, epochs=10, time=None, patience=100, batch=16, imgsz=1280, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=glass-bottle_2024-06-20_18-34-51, 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=False, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/glass-bottle_2024-06-20_18-34-51\n", "Overriding model.yaml nc=80 with nc=3\n", "\n", " from n params module arguments \n", " 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n", " 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n", " 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n", " 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n", " 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", " 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n", " 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", " 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n", " 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n", " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n", " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n", " 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n", " 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n", " 22 [15, 18, 21] 1 751897 ultralytics.nn.modules.head.Detect [3, [64, 128, 256]] \n", "Model summary: 225 layers, 3011433 parameters, 3011417 gradients, 8.2 GFLOPs\n", "\n", "Transferred 319/355 items from pretrained weights\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/glass-bottle_2024-06-20_18-34-51', view at http://localhost:6006/\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33methancao\u001b[0m (\u001b[33methan_cao\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" ] }, { "data": { "text/html": [ "Tracking run with wandb version 0.17.2" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Run data is saved locally in /home/ethan/Python-Table/glass-bottle-mouth-detection/wandb/run-20240620_183454-jwoeicdk" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "Syncing run glass-bottle_2024-06-20_18-34-51 to Weights & Biases (docs)
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View project at https://wandb.ai/ethan_cao/YOLOv8" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ " View run at https://wandb.ai/ethan_cao/YOLOv8/runs/jwoeicdk" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Freezing layer 'model.22.dfl.conv.weight'\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /home/ethan/Python-Table/glass-bottle-mouth-detection/train/labels... 690 images, 0 backgrounds, 0 corrupt: 100%|██████████| 690/690 [00:00<00:00, 1281.04it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /home/ethan/Python-Table/glass-bottle-mouth-detection/train/labels.cache\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning /home/ethan/Python-Table/glass-bottle-mouth-detection/valid/labels... 77 images, 0 backgrounds, 0 corrupt: 100%|██████████| 77/77 [00:00<00:00, 597.31it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /home/ethan/Python-Table/glass-bottle-mouth-detection/valid/labels.cache\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Plotting labels to runs/detect/glass-bottle_2024-06-20_18-34-51/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=0.001429, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n", "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n", "Image sizes 1280 train, 1280 val\n", "Using 8 dataloader workers\n", "Logging results to \u001b[1mruns/detect/glass-bottle_2024-06-20_18-34-51\u001b[0m\n", "Starting training for 10 epochs...\n", "Closing dataloader mosaic\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 1/10 9.64G 0.9238 7.231 1.177 2 1280: 100%|██████████| 44/44 [00:11<00:00, 3.76it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 3.68it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.00355 1 0.655 0.54\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": [ " 2/10 8.84G 0.7243 4.601 0.9087 2 1280: 100%|██████████| 44/44 [00:08<00:00, 5.00it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 4.90it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.985 1 0.995 0.81\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": [ " 3/10 8.85G 0.7352 3.581 0.9055 2 1280: 100%|██████████| 44/44 [00:06<00:00, 6.40it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 5.18it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.991 0.994 0.995 0.788\n" ] }, { "name": "stderr", "output_type": 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all 77 77 0.996 1 0.995 0.848\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": [ " 6/10 8.85G 0.6313 1.974 0.8829 2 1280: 100%|██████████| 44/44 [00:08<00:00, 5.18it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 6.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.99 1 0.995 0.868\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": [ " 7/10 8.85G 0.6132 1.692 0.8826 2 1280: 100%|██████████| 44/44 [00:06<00:00, 6.63it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 6.19it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.995 1 0.995 0.869\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": [ " 8/10 8.85G 0.5814 1.452 0.864 2 1280: 100%|██████████| 44/44 [00:08<00:00, 5.06it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 5.80it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.996 1 0.995 0.883\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": [ " 9/10 8.85G 0.5619 1.322 0.8536 1 1280: 100%|██████████| 44/44 [00:08<00:00, 5.25it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 6.26it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.997 1 0.995 0.886\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": [ " 10/10 8.85G 0.5471 1.216 0.8529 2 1280: 100%|██████████| 44/44 [00:06<00:00, 6.89it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:00<00:00, 6.13it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.997 1 0.995 0.894\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "10 epochs completed in 0.026 hours.\n", "Optimizer stripped from runs/detect/glass-bottle_2024-06-20_18-34-51/weights/last.pt, 6.4MB\n", "Optimizer stripped from runs/detect/glass-bottle_2024-06-20_18-34-51/weights/best.pt, 6.4MB\n", "\n", "Validating runs/detect/glass-bottle_2024-06-20_18-34-51/weights/best.pt...\n", "Ultralytics YOLOv8.2.36 🚀 Python-3.12.3 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4070 Ti SUPER, 16376MiB)\n", "Model summary (fused): 168 layers, 3006233 parameters, 0 gradients, 8.1 GFLOPs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 3/3 [00:01<00:00, 1.50it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " all 77 77 0.997 1 0.995 0.894\n", " circle 20 20 0.996 1 0.995 0.917\n", " oval 15 15 0.997 1 0.995 0.905\n", " teardrop 42 42 0.999 1 0.995 0.86\n", "Speed: 0.6ms preprocess, 7.0ms inference, 0.0ms loss, 4.8ms postprocess per image\n", "Results saved to \u001b[1mruns/detect/glass-bottle_2024-06-20_18-34-51\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": 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Run summary:


lr/pg00.00016
lr/pg10.00016
lr/pg20.00016
metrics/mAP50(B)0.995
metrics/mAP50-95(B)0.89407
metrics/precision(B)0.99711
metrics/recall(B)1.0
model/GFLOPs8.196
model/parameters3011433
model/speed_PyTorch(ms)3.472
train/box_loss0.54713
train/cls_loss1.21571
train/dfl_loss0.85287
val/box_loss0.51631
val/cls_loss0.35803
val/dfl_loss0.84626

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array([[ 1, 1, 1, ..., 0, 0, 0],\n", " [ 1, 1, 1, ..., 0, 0, 0],\n", " [ 1, 1, 1, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", "fitness: 0.9041617683645472\n", "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", "maps: array([ 0.91707, 0.90526, 0.85987])\n", "names: {0: 'circle', 1: 'oval', 2: 'teardrop'}\n", "plot: True\n", "results_dict: {'metrics/precision(B)': 0.9971088985021154, 'metrics/recall(B)': 1.0, 'metrics/mAP50(B)': 0.995, 'metrics/mAP50-95(B)': 0.8940686315161634, 'fitness': 0.9041617683645472}\n", "save_dir: PosixPath('runs/detect/glass-bottle_2024-06-20_18-34-51')\n", "speed: {'preprocess': 0.6242975012048498, 'inference': 7.010057375028536, 'loss': 0.0002508039598341112, 'postprocess': 4.758927729222682}\n", "task: 'detect'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "epochs = 10\n", "current_time = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n", "\n", "model.train(\n", " data=\"data.yaml\",\n", " name=f\"glass-bottle_{current_time}\",\n", " task=\"detect\",\n", " epochs=epochs,\n", " imgsz=1280,\n", " val=True,\n", " resume=False,\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": "base", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }