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untitled38.ipynb
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"text": [
"Collecting ultralytics\n",
" Downloading ultralytics-8.3.173-py3-none-any.whl.metadata (37 kB)\n",
"Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.0.2)\n",
"Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0)\n",
"Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.12.0.88)\n",
"Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.3.0)\n",
"Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2)\n",
"Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.3)\n",
"Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.16.0)\n",
"Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.6.0+cu124)\n",
"Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.21.0+cu124)\n",
"Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.67.1)\n",
"Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (5.9.5)\n",
"Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0)\n",
"Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2)\n",
"Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
" Downloading ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.2)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.59.0)\n",
"Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8)\n",
"Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.3)\n",
"Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2)\n",
"Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10)\n",
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"\u001b[?25hDownloading ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)\n",
"Installing collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, ultralytics-thop, ultralytics\n",
" Attempting uninstall: nvidia-nvjitlink-cu12\n",
" Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
" Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
" Attempting uninstall: nvidia-curand-cu12\n",
" Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
" Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
" Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
" Attempting uninstall: nvidia-cufft-cu12\n",
" Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
" Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
" Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
" Attempting uninstall: nvidia-cuda-runtime-cu12\n",
" Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
" Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
" Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
" Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
" Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
" Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
"Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.173 ultralytics-thop-2.0.14\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 339,
"referenced_widgets": [
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},
"id": "JujzBvGvZr4s",
"outputId": "d149cae4-23b3-4fcb-c302-d778a1180914"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0.00B [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "77f0848b313d43da844da12ac0451eed"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/train-00000-of-00002.parquet: 0%| | 0.00/461M [00:00<?, ?B/s]"
],
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"version_minor": 0,
"model_id": "fb06b2fd48dc42ca9e97f5c1ae4abfac"
}
},
"metadata": {}
},
{
"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1c2c31447c534d6481bab6962aeb56a6"
}
},
"metadata": {}
},
{
"output_type": "display_data",
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"metadata": {}
},
{
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"data": {
"text/plain": [
"Generating train split: 0%| | 0/2342 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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"data": {
"text/plain": [
"Generating test split: 0%| | 0/236 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6754bef2dff548e0948504cf00b84882"
}
},
"metadata": {}
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"agyaatcoder/PlantDoc\")"
]
},
{
"cell_type": "code",
"source": [
"#!/usr/bin/env python\n",
"\"\"\"\n",
"Convert the Hugging Face object-detection dataset `agyaatcoder/PlantDoc`\n",
"to the Ultralytics-YOLO format.\n",
"\n",
"> pip install \"datasets[pandas]\" pillow tqdm pyyaml\n",
"\"\"\"\n",
"\n",
"from pathlib import Path\n",
"import logging, yaml\n",
"from tqdm.auto import tqdm\n",
"from datasets import load_dataset\n",
"from PIL import Image\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Config -----------------------------------------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"HF_DATASET = \"agyaatcoder/PlantDoc\"\n",
"SPLIT_MAP = {\"train\": \"train\", \"val\": \"test\"} # HF → YOLO names\n",
"OUT_ROOT = Path(\"yolo_plantdoc\") # output root\n",
"IMG_DIR = OUT_ROOT / \"images\"\n",
"LBL_DIR = OUT_ROOT / \"labels\"\n",
"IMG_SUFFIX = \".jpg\" # keep it simple: JPEG\n",
"LOG_LEVEL = \"INFO\"\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Setup ------------------------------------------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"logging.basicConfig(format=\"%(levelname)s: %(message)s\", level=LOG_LEVEL)\n",
"OUT_ROOT.mkdir(parents=True, exist_ok=True)\n",
"(IMG_DIR / \"train\").mkdir(parents=True, exist_ok=True)\n",
"(IMG_DIR / \"val\").mkdir(parents=True, exist_ok=True)\n",
"(LBL_DIR / \"train\").mkdir(parents=True, exist_ok=True)\n",
"(LBL_DIR / \"val\").mkdir(parents=True, exist_ok=True)\n",
"\n",
"logging.info(\"🔄 Loading dataset %s ...\", HF_DATASET)\n",
"ds = load_dataset(HF_DATASET, split=None) # returns a DatasetDict\n",
"logging.info(\"✅ Loaded: %d train, %d test\", len(ds[\"train\"]), len(ds[\"test\"]))\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Identify the category column inside `objects`\n",
"# ------------------------------------------------------------------------------\n",
"obj_feat = ds[\"train\"].features[\"objects\"] # dict nested feature\n",
"POSSIBLE = (\"category\", \"category_id\", \"label\", \"labels\", \"name\")\n",
"cat_column = next((k for k in POSSIBLE if k in obj_feat), None)\n",
"if cat_column is None:\n",
" raise ValueError(f\"No category column found. Keys present: {list(obj_feat)}\")\n",
"logging.info(\"🗂 Using '%s' as the label field.\", cat_column)\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Build class list (stable, sorted order) --------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"labels = sorted(\n",
" {lbl for split in ds.values() for ex in split for lbl in ex[\"objects\"][cat_column]}\n",
")\n",
"name2id = {n: i for i, n in enumerate(labels)}\n",
"logging.info(\"📊 Found %d unique classes.\", len(labels))\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Helper to export one example -------------------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"def export_example(split: str, idx: int, ex: dict):\n",
" pil = ex[\"image\"] # PIL.Image\n",
" if pil.mode not in (\"RGB\", \"L\"): # handle RGBA / CMYK / P\n",
" pil = pil.convert(\"RGB\")\n",
" elif pil.mode == \"L\": # grayscale → 3-channel\n",
" pil = pil.convert(\"RGB\")\n",
"\n",
" w, h = pil.size\n",
" stem = f\"{idx:07d}\"\n",
" img_p = IMG_DIR / split / (stem + IMG_SUFFIX)\n",
" lbl_p = LBL_DIR / split / (stem + \".txt\")\n",
"\n",
" pil.save(img_p, format=\"JPEG\", quality=90)\n",
"\n",
" lines = []\n",
" for bbox, lbl in zip(ex[\"objects\"][\"bbox\"], ex[\"objects\"][cat_column]):\n",
" x0, y0, bw, bh = bbox # HF format: xywh in **pixels**\n",
" xc, yc = x0 + bw / 2, y0 + bh / 2\n",
" lines.append(\n",
" f\"{name2id[lbl]} {xc / w:.6f} {yc / h:.6f} {bw / w:.6f} {bh / h:.6f}\"\n",
" )\n",
"\n",
" if lines: # YOLO spec: no file = no objects\n",
" lbl_p.write_text(\"\\n\".join(lines))\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Export loops with progress bars ----------------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"for split, hf_split in SPLIT_MAP.items():\n",
" logging.info(\"🚚 Exporting %s split ...\", split)\n",
" for i, ex in enumerate(\n",
" tqdm(ds[hf_split], total=len(ds[hf_split]), desc=f\"{split:5}\")\n",
" ):\n",
" export_example(split, i, ex)\n",
"\n",
"# ------------------------------------------------------------------------------\n",
"# Write the dataset YAML -------------------------------------------------------\n",
"# ------------------------------------------------------------------------------\n",
"yaml_dict = {\n",
" \"path\": str(OUT_ROOT),\n",
" \"train\": \"images/train\",\n",
" \"val\": \"images/val\",\n",
" \"names\": {i: n for i, n in enumerate(labels)},\n",
"}\n",
"with open(OUT_ROOT / \"dataset.yaml\", \"w\") as f:\n",
" yaml.safe_dump(yaml_dict, f, sort_keys=False)\n",
"\n",
"logging.info(\"🎉 All done! → %s\", OUT_ROOT / \"dataset.yaml\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"e912bc30cbb5487f845afb749a6e7526",
"90a931ff8fb14293923d4c66e0a67f04",
"a3c2616e75e14e19a2d6245e47623dad",
"199d62e5848643ba887a918890967173",
"c41f203b020b44738df76fc011c3dc39",
"a67f3a63700c4027ab5264b99799476c",
"07e75f5807c1476b8b536e96a4125dc8",
"0ea721843a8f4c049cbd51c7631fc187",
"728b32072c594bbea6e4c042742ce22b",
"c20325e65a174fc6917adc551b2362b7",
"d7a8bca1a52144f3913962bd603dcd04",
"0f2f4e2fe938494da92fc41f38241bf5",
"5b99f2bc71d949c08bd0032fb4a34724",
"8bef7aa658764fed9ffea7d52382408b",
"1503d70525854a9fbf489c58769dce08",
"2384d3f39c6e49699358b5b822bfcbf3",
"d7185da0f2bd4555b4341b71f0e030e1",
"90ce6b20ed284624bfd1a84addea2df7",
"41ea7e4c8be74434b61bed6f2ca52d4d",
"57038dac0b874f62908b68ef666ab7d5",
"b6f84e7823444c279fbd6b16b3eab124",
"a8b8a3c4736145309bc3569d4969c573"
]
},
"id": "iFkiHpVxaUBT",
"outputId": "602db75b-2494-4b10-ccff-fd4c26c7a0a0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"train: 0%| | 0/2342 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e912bc30cbb5487f845afb749a6e7526"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"val : 0%| | 0/236 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0f2f4e2fe938494da92fc41f38241bf5"
}
},
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}
]
},
{
"cell_type": "code",
"source": [
"!yolo detect train data=/content/yolo_plantdoc/dataset.yaml model=yolov8n.pt epochs=60"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QGuE1IVBa3US",
"outputId": "7d3ade58-a22f-46e0-d794-7d36b185e87a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.173 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/yolo_plantdoc/dataset.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=60, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train5, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train5, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n",
"Overriding model.yaml nc=80 with nc=29\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 756967 ultralytics.nn.modules.head.Detect [29, [64, 128, 256]] \n",
"Model summary: 129 layers, 3,016,503 parameters, 3,016,487 gradients, 8.2 GFLOPs\n",
"\n",
"Transferred 319/355 items from pretrained weights\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2603.4±1308.8 MB/s, size: 206.7 KB)\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolo_plantdoc/labels/train.cache... 2342 images, 0 backgrounds, 14 corrupt: 100% 2342/2342 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0741]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.9161 1.9124 3.8298 3.8216]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1439]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000477.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4398]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2627]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0000774.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1875]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0001205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3294]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0001213.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.03]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0001361.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.468]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0001593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 3.27 3.0667 6.516 5.9947]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0001799.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1901]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0002016.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0888 1.0527]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0002091.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3167]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0m/content/yolo_plantdoc/images/train/0002265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.155]\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1310.8±523.8 MB/s, size: 68.9 KB)\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolo_plantdoc/labels/val.cache... 236 images, 0 backgrounds, 2 corrupt: 100% 236/236 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0m/content/yolo_plantdoc/images/val/0000019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0854]\n",
"\u001b[34m\u001b[1mval: \u001b[0m/content/yolo_plantdoc/images/val/0000131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2267]\n",
"Plotting labels to runs/detect/train5/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=0.000303, 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 2 dataloader workers\n",
"Logging results to \u001b[1mruns/detect/train5\u001b[0m\n",
"Starting training for 60 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 1/60 2.19G 1.292 4.191 1.459 63 640: 100% 146/146 [00:55<00:00, 2.63it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.55it/s]\n",
" all 234 450 0.366 0.149 0.0952 0.0706\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/60 2.6G 1.289 3.406 1.438 36 640: 100% 146/146 [00:55<00:00, 2.62it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.48it/s]\n",
" all 234 450 0.276 0.325 0.21 0.155\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/60 2.62G 1.29 2.997 1.43 53 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.82it/s]\n",
" all 234 450 0.306 0.392 0.302 0.213\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 4/60 2.62G 1.288 2.777 1.429 40 640: 100% 146/146 [00:54<00:00, 2.68it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.04it/s]\n",
" all 234 450 0.324 0.49 0.344 0.251\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 5/60 2.62G 1.274 2.597 1.405 93 640: 100% 146/146 [00:54<00:00, 2.68it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:04<00:00, 1.86it/s]\n",
" all 234 450 0.324 0.455 0.383 0.292\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 6/60 2.63G 1.241 2.461 1.394 65 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.13it/s]\n",
" all 234 450 0.356 0.434 0.361 0.27\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 7/60 2.65G 1.226 2.377 1.374 71 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.02it/s]\n",
" all 234 450 0.333 0.504 0.408 0.318\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 8/60 2.65G 1.229 2.268 1.362 47 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.98it/s]\n",
" all 234 450 0.38 0.541 0.458 0.358\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 9/60 2.67G 1.213 2.192 1.352 72 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.10it/s]\n",
" all 234 450 0.394 0.474 0.453 0.346\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 10/60 2.67G 1.206 2.128 1.34 70 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.84it/s]\n",
" all 234 450 0.369 0.589 0.517 0.402\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 11/60 2.68G 1.185 2.064 1.33 35 640: 100% 146/146 [00:52<00:00, 2.76it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.00it/s]\n",
" all 234 450 0.47 0.55 0.496 0.384\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 12/60 2.68G 1.187 2.02 1.323 52 640: 100% 146/146 [00:53<00:00, 2.75it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.34it/s]\n",
" all 234 450 0.411 0.574 0.517 0.394\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 13/60 2.68G 1.188 1.967 1.324 52 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.71it/s]\n",
" all 234 450 0.454 0.532 0.517 0.401\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 14/60 2.68G 1.169 1.939 1.322 63 640: 100% 146/146 [00:53<00:00, 2.75it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.45it/s]\n",
" all 234 450 0.472 0.57 0.566 0.438\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 15/60 2.68G 1.177 1.859 1.311 73 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.09it/s]\n",
" all 234 450 0.464 0.547 0.527 0.403\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 16/60 2.68G 1.156 1.839 1.301 62 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.19it/s]\n",
" all 234 450 0.472 0.55 0.548 0.419\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 17/60 2.68G 1.163 1.796 1.297 104 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.22it/s]\n",
" all 234 450 0.556 0.506 0.56 0.433\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 18/60 2.68G 1.126 1.747 1.29 99 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.00it/s]\n",
" all 234 450 0.501 0.556 0.544 0.424\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 19/60 2.68G 1.142 1.736 1.284 43 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.98it/s]\n",
" all 234 450 0.462 0.581 0.551 0.425\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 20/60 2.68G 1.143 1.7 1.289 73 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.81it/s]\n",
" all 234 450 0.446 0.642 0.547 0.436\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 21/60 2.68G 1.124 1.639 1.277 49 640: 100% 146/146 [00:54<00:00, 2.69it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.13it/s]\n",
" all 234 450 0.472 0.633 0.576 0.447\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 22/60 2.68G 1.13 1.629 1.272 51 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.97it/s]\n",
" all 234 450 0.47 0.671 0.58 0.455\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 23/60 2.68G 1.124 1.598 1.269 43 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.94it/s]\n",
" all 234 450 0.549 0.595 0.589 0.456\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 24/60 2.68G 1.101 1.562 1.263 64 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.52it/s]\n",
" all 234 450 0.569 0.593 0.596 0.463\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 25/60 2.68G 1.112 1.552 1.272 84 640: 100% 146/146 [00:52<00:00, 2.79it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.10it/s]\n",
" all 234 450 0.562 0.592 0.619 0.487\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 26/60 2.68G 1.104 1.537 1.259 45 640: 100% 146/146 [00:52<00:00, 2.76it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.55it/s]\n",
" all 234 450 0.604 0.565 0.616 0.485\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 27/60 2.7G 1.098 1.514 1.259 90 640: 100% 146/146 [00:53<00:00, 2.71it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.08it/s]\n",
" all 234 450 0.525 0.57 0.591 0.465\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 28/60 2.7G 1.091 1.47 1.25 33 640: 100% 146/146 [00:52<00:00, 2.81it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.74it/s]\n",
" all 234 450 0.509 0.612 0.611 0.487\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 29/60 2.7G 1.098 1.473 1.254 47 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.01it/s]\n",
" all 234 450 0.562 0.625 0.642 0.51\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 30/60 2.7G 1.081 1.436 1.248 86 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.78it/s]\n",
" all 234 450 0.529 0.644 0.614 0.483\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 31/60 2.7G 1.089 1.432 1.246 46 640: 100% 146/146 [00:52<00:00, 2.79it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.76it/s]\n",
" all 234 450 0.518 0.572 0.577 0.449\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 32/60 2.7G 1.079 1.385 1.239 48 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.47it/s]\n",
" all 234 450 0.535 0.637 0.602 0.473\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 33/60 2.7G 1.068 1.387 1.237 53 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.39it/s]\n",
" all 234 450 0.589 0.593 0.609 0.483\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 34/60 2.7G 1.077 1.352 1.241 52 640: 100% 146/146 [00:52<00:00, 2.80it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.08it/s]\n",
" all 234 450 0.51 0.635 0.582 0.462\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 35/60 2.7G 1.066 1.349 1.229 48 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.63it/s]\n",
" all 234 450 0.579 0.536 0.602 0.478\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 36/60 2.7G 1.069 1.341 1.236 105 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.93it/s]\n",
" all 234 450 0.529 0.65 0.614 0.486\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 37/60 2.7G 1.072 1.325 1.233 52 640: 100% 146/146 [00:52<00:00, 2.76it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.69it/s]\n",
" all 234 450 0.601 0.631 0.653 0.515\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 38/60 2.7G 1.036 1.292 1.218 91 640: 100% 146/146 [00:52<00:00, 2.77it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.93it/s]\n",
" all 234 450 0.539 0.659 0.636 0.5\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 39/60 2.7G 1.047 1.279 1.214 69 640: 100% 146/146 [00:52<00:00, 2.76it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.67it/s]\n",
" all 234 450 0.555 0.632 0.609 0.482\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 40/60 2.7G 1.048 1.263 1.215 25 640: 100% 146/146 [00:52<00:00, 2.77it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.71it/s]\n",
" all 234 450 0.557 0.575 0.605 0.479\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 41/60 2.87G 1.048 1.246 1.22 58 640: 100% 146/146 [00:52<00:00, 2.77it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.81it/s]\n",
" all 234 450 0.6 0.6 0.632 0.496\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 42/60 2.87G 1.036 1.246 1.215 54 640: 100% 146/146 [00:54<00:00, 2.70it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.99it/s]\n",
" all 234 450 0.545 0.654 0.626 0.492\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 43/60 2.87G 1.009 1.21 1.205 51 640: 100% 146/146 [00:52<00:00, 2.79it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.03it/s]\n",
" all 234 450 0.637 0.584 0.638 0.501\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 44/60 2.87G 1.035 1.197 1.199 99 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.00it/s]\n",
" all 234 450 0.489 0.673 0.615 0.482\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 45/60 2.87G 1.026 1.192 1.2 55 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.85it/s]\n",
" all 234 450 0.552 0.597 0.61 0.483\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 46/60 2.87G 1.026 1.195 1.202 55 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.57it/s]\n",
" all 234 450 0.537 0.645 0.632 0.496\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 47/60 2.87G 1.021 1.196 1.198 84 640: 100% 146/146 [00:53<00:00, 2.74it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.05it/s]\n",
" all 234 450 0.597 0.573 0.619 0.488\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 48/60 2.87G 1.014 1.165 1.196 47 640: 100% 146/146 [00:52<00:00, 2.77it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.72it/s]\n",
" all 234 450 0.6 0.598 0.62 0.486\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 49/60 2.87G 1.007 1.166 1.198 63 640: 100% 146/146 [00:53<00:00, 2.72it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.82it/s]\n",
" all 234 450 0.538 0.63 0.619 0.485\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 50/60 2.87G 1.026 1.159 1.202 71 640: 100% 146/146 [00:52<00:00, 2.76it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 2.79it/s]\n",
" all 234 450 0.53 0.665 0.649 0.511\n",
"Closing dataloader mosaic\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 51/60 2.87G 0.9805 1.13 1.184 14 640: 100% 146/146 [00:53<00:00, 2.73it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:03<00:00, 2.20it/s]\n",
" all 234 450 0.528 0.67 0.628 0.487\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 52/60 2.87G 0.9556 1.06 1.174 39 640: 100% 146/146 [00:51<00:00, 2.82it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:02<00:00, 3.05it/s]\n",
" all 234 450 0.577 0.648 0.629 0.493\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 53/60 2.87G 0.9378 1.004 1.154 96 640: 79% 116/146 [00:41<00:10, 2.94it/s]"
]
}
]
},
{
"cell_type": "code",
"source": [
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"\n",
"url = \"https://extension.umd.edu/sites/extension.umd.edu/files/styles/optimized/public/2021-05/hgic_veg_bacterial%20leaf%20spot_pepper_800.jpg?itok=0snxoX9B\"\n",
"\n",
"response = requests.get(url)\n",
"img = Image.open(BytesIO(response.content))\n",
"display(img)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 790
},
"id": "AWbmfE-Iek8u",
"outputId": "077e33f0-4563-493a-b67a-016b098fb6e5"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=800x773>"
],
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