Dataset Viewer (First 5GB)
Search is not available for this dataset
text
stringlengths 2.03k
93.4M
| id
stringlengths 22
24
| file_path
stringclasses 44
values |
|---|---|---|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "63595224",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2023-04-08T10:03:23.803314Z",
"iopub.status.busy": "2023-04-08T10:03:23.802611Z",
"iopub.status.idle": "2023-04-08T10:03:26.560047Z",
"shell.execute_reply": "2023-04-08T10:03:26.558738Z"
},
"papermill": {
"duration": 2.764565,
"end_time": "2023-04-08T10:03:26.562557",
"exception": false,
"start_time": "2023-04-08T10:03:23.797992",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"%%capture\n",
"!git clone https://github.com/ultralytics/yolov5.git"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0e07f928",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:26.570163Z",
"iopub.status.busy": "2023-04-08T10:03:26.569189Z",
"iopub.status.idle": "2023-04-08T10:03:47.482550Z",
"shell.execute_reply": "2023-04-08T10:03:47.481156Z"
},
"papermill": {
"duration": 20.919718,
"end_time": "2023-04-08T10:03:47.485279",
"exception": false,
"start_time": "2023-04-08T10:03:26.565561",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install -r yolov5/requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "afb72c72",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:47.491830Z",
"iopub.status.busy": "2023-04-08T10:03:47.491506Z",
"iopub.status.idle": "2023-04-08T10:03:58.683913Z",
"shell.execute_reply": "2023-04-08T10:03:58.682874Z"
},
"papermill": {
"duration": 11.198707,
"end_time": "2023-04-08T10:03:58.686565",
"exception": false,
"start_time": "2023-04-08T10:03:47.487858",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"# Define the path to your dataset\n",
"dataset_path = '/kaggle/input/glasses-detection-yolo-format/data'\n",
"\n",
"# Create a directory for the images and labels in the working directory\n",
"output_path = '/kaggle/working'\n",
"image_path = os.path.join(output_path, 'images')\n",
"os.makedirs(image_path, exist_ok=True)\n",
"label_path = os.path.join(output_path, 'labels')\n",
"os.makedirs(label_path, exist_ok=True)\n",
"\n",
"for filename in os.listdir(dataset_path):\n",
" if filename.endswith('.jpg') or filename.endswith('.jpeg'):\n",
" # If the file is an image, copy it to the images directory\n",
" src_path = os.path.join(dataset_path, filename)\n",
" dst_path = os.path.join(image_path, filename)\n",
" shutil.copy(src_path, dst_path)\n",
" elif filename.endswith('.txt'):\n",
" # If the file is a label, copy it to the labels directory\n",
" src_path = os.path.join(dataset_path, filename)\n",
" dst_path = os.path.join(label_path, filename)\n",
" shutil.copy(src_path, dst_path)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d10916ef",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:58.692804Z",
"iopub.status.busy": "2023-04-08T10:03:58.692485Z",
"iopub.status.idle": "2023-04-08T10:03:58.719261Z",
"shell.execute_reply": "2023-04-08T10:03:58.718333Z"
},
"papermill": {
"duration": 0.03231,
"end_time": "2023-04-08T10:03:58.721465",
"exception": false,
"start_time": "2023-04-08T10:03:58.689155",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import yaml\n",
"\n",
"# Define the path to the YAML file\n",
"yaml_path = '/kaggle/working/yolov5/data/my_dataset.yaml'\n",
"\n",
"# Define the contents of the YAML file\n",
"data = dict(\n",
" train='../yolov5/images',\n",
" val='../yolov5/images',\n",
" nc=2,\n",
" names=['no_glasses', 'with_glasses']\n",
")\n",
"\n",
"# Write the YAML file\n",
"with open(yaml_path, 'w') as f:\n",
" yaml.dump(data, f)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bc800699",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:58.727186Z",
"iopub.status.busy": "2023-04-08T10:03:58.726918Z",
"iopub.status.idle": "2023-04-08T10:03:58.736995Z",
"shell.execute_reply": "2023-04-08T10:03:58.736068Z"
},
"papermill": {
"duration": 0.01545,
"end_time": "2023-04-08T10:03:58.739146",
"exception": false,
"start_time": "2023-04-08T10:03:58.723696",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'/kaggle/working/yolov5/labels'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yolov5_path = '/kaggle/working/yolov5'\n",
"shutil.move('/kaggle/working/images', yolov5_path)\n",
"shutil.move('/kaggle/working/labels', yolov5_path)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e23fb071",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:58.744951Z",
"iopub.status.busy": "2023-04-08T10:03:58.744680Z",
"iopub.status.idle": "2023-04-08T10:03:58.751664Z",
"shell.execute_reply": "2023-04-08T10:03:58.750453Z"
},
"papermill": {
"duration": 0.012349,
"end_time": "2023-04-08T10:03:58.753916",
"exception": false,
"start_time": "2023-04-08T10:03:58.741567",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/working/yolov5\n"
]
}
],
"source": [
"cd '/kaggle/working/yolov5'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4225af62",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:03:58.759903Z",
"iopub.status.busy": "2023-04-08T10:03:58.759638Z",
"iopub.status.idle": "2023-04-08T15:39:09.798704Z",
"shell.execute_reply": "2023-04-08T15:39:09.797429Z"
},
"papermill": {
"duration": 20111.045705,
"end_time": "2023-04-08T15:39:09.801958",
"exception": false,
"start_time": "2023-04-08T10:03:58.756253",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: WARNING ⚠️ wandb is deprecated and will be removed in a future release. See supported integrations at https://github.com/ultralytics/yolov5#integrations.\r\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\r\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\r\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\r\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice: (30 second timeout) \r\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: W&B disabled due to login timeout.\r\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=yolov5s.yaml, data=my_dataset.yaml, hyp=hyp.scratch-low.yaml, epochs=100, batch_size=32, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=24, project=runs/train, name=yolo_glasses_det, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\r\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\r\n",
"\u001b[31m\u001b[1mrequirements:\u001b[0m YOLOv5 requirements \"tqdm>=4.64.0\" \"tensorboard>=2.4.1\" not found, attempting AutoUpdate...\r\n",
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
"\u001b[0mRequirement already satisfied: tqdm>=4.64.0 in /opt/conda/lib/python3.7/site-packages (4.64.1)\r\n",
"Requirement already satisfied: tensorboard>=2.4.1 in /opt/conda/lib/python3.7/site-packages (2.11.2)\r\n",
"Requirement already satisfied: grpcio>=1.24.3 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (1.51.1)\r\n",
"Requirement already satisfied: numpy>=1.12.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (1.21.6)\r\n",
"Requirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (0.38.4)\r\n",
"Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (0.6.1)\r\n",
"Requirement already satisfied: absl-py>=0.4 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (1.4.0)\r\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (2.28.2)\r\n",
"Requirement already satisfied: protobuf<4,>=3.9.2 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (3.20.3)\r\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (1.8.1)\r\n",
"Requirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (67.6.1)\r\n",
"Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (3.4.1)\r\n",
"Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (1.35.0)\r\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (0.4.6)\r\n",
"Requirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from tensorboard>=2.4.1) (2.2.3)\r\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1) (0.2.8)\r\n",
"Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1) (4.9)\r\n",
"Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1) (4.2.4)\r\n",
"Requirement already satisfied: six>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from google-auth<3,>=1.6.3->tensorboard>=2.4.1) (1.16.0)\r\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.7/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1) (1.3.1)\r\n",
"Requirement already satisfied: importlib-metadata>=4.4 in /opt/conda/lib/python3.7/site-packages (from markdown>=2.6.8->tensorboard>=2.4.1) (4.11.4)\r\n",
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1) (1.26.14)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1) (2.1.1)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1) (3.4)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1) (2022.12.7)\r\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.7/site-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1) (2.1.1)\r\n",
"Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1) (3.11.0)\r\n",
"Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1) (4.4.0)\r\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.7/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1) (0.4.8)\r\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.7/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1) (3.2.2)\r\n",
"\r\n",
"\u001b[31m\u001b[1mrequirements:\u001b[0m 2 packages updated per /kaggle/working/yolov5/requirements.txt\r\n",
"\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\r\n",
"\r\n",
"YOLOv5 🚀 v7.0-134-g23c4923 Python-3.7.12 torch-1.13.0 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\r\n",
"\r\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\r\n",
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\r\n",
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\r\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\r\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\r\n",
"100%|████████████████████████████████████████| 755k/755k [00:00<00:00, 24.7MB/s]\r\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\r\n",
"100%|██████████████████████████████████████| 14.1M/14.1M [00:00<00:00, 33.5MB/s]\r\n",
"\r\n",
"Overriding model.yaml nc=80 with nc=2\r\n",
"\r\n",
" from n params module arguments \r\n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \r\n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \r\n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \r\n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \r\n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \r\n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \r\n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \r\n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \r\n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \r\n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \r\n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \r\n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \r\n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \r\n",
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \r\n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \r\n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \r\n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \r\n",
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \r\n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \r\n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \r\n",
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \r\n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \r\n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \r\n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \r\n",
" 24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\r\n",
"YOLOv5s summary: 214 layers, 7025023 parameters, 7025023 gradients, 16.0 GFLOPs\r\n",
"\r\n",
"Transferred 342/349 items from yolov5s.pt\r\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\r\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\r\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\r\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/yolov5/labels... 133 images, 0 backgrounds, 0 co\u001b[0m\r\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/yolov5/labels.cache\r\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/yolov5/labels.cache... 133 images, 0 backgrounds, \u001b[0m\r\n",
"\r\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.02 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\r\n",
"Plotting labels to runs/train/yolo_glasses_det/labels.jpg... \r\n",
"Image sizes 320 train, 320 val\r\n",
"Using 2 dataloader workers\r\n",
"Logging results to \u001b[1mruns/train/yolo_glasses_det\u001b[0m\r\n",
"Starting training for 100 epochs...\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 0/99 1.82G 0.1311 0.00906 0.02928 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 7.75e-05 0.0208 4.19e-05 4.19e-06\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 1/99 2.08G 0.1254 0.01001 0.02872 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000126 0.0354 7.67e-05 1.72e-05\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 2/99 2.08G 0.1189 0.01154 0.02885 13 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000225 0.0625 0.000125 3.07e-05\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 3/99 2.08G 0.1092 0.01208 0.0265 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000319 0.0187 0.00103 0.000383\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 4/99 2.08G 0.1048 0.01247 0.0266 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000491 0.14 0.000292 8.06e-05\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 5/99 2.08G 0.1015 0.01379 0.02544 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000667 0.192 0.000553 0.000117\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 6/99 2.08G 0.09823 0.01261 0.02404 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.000757 0.217 0.000706 0.000175\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 7/99 2.08G 0.09394 0.01398 0.02255 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.00105 0.3 0.0011 0.000233\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 8/99 2.08G 0.09305 0.01437 0.02302 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.00158 0.45 0.00151 0.000389\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 9/99 2.08G 0.08783 0.01455 0.02247 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.00233 0.623 0.00808 0.00216\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 10/99 2.08G 0.08106 0.01428 0.0211 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.00252 0.673 0.0285 0.0056\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 11/99 2.08G 0.08352 0.01335 0.02247 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.00303 0.798 0.0498 0.0143\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 12/99 2.08G 0.07804 0.01245 0.0209 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.13 0.215 0.0942 0.0205\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 13/99 2.08G 0.07874 0.01346 0.02099 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.138 0.0312 0.0596 0.0126\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 14/99 2.08G 0.07916 0.01305 0.02065 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.0215 0.567 0.0456 0.0109\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 15/99 2.08G 0.07626 0.01276 0.01855 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.306 0.252 0.179 0.0481\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 16/99 2.08G 0.07468 0.01209 0.01657 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.109 0.198 0.0836 0.0258\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 17/99 2.08G 0.07586 0.01255 0.0146 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.212 0.24 0.131 0.0288\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 18/99 2.08G 0.07551 0.01158 0.01438 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.287 0.322 0.222 0.0546\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 19/99 2.08G 0.07118 0.01102 0.01212 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.0869 0.306 0.0787 0.0207\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 20/99 2.08G 0.07194 0.01151 0.01114 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.276 0.25 0.214 0.0514\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 21/99 2.08G 0.06878 0.01176 0.01004 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.349 0.373 0.296 0.0714\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 22/99 2.08G 0.06765 0.01152 0.009298 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.296 0.387 0.275 0.0602\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 23/99 2.08G 0.06959 0.01091 0.01061 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.678 0.383 0.525 0.16\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 24/99 2.08G 0.06294 0.01032 0.01053 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.325 0.327 0.284 0.0781\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 25/99 2.08G 0.06653 0.0114 0.008399 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.635 0.533 0.568 0.21\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 26/99 2.08G 0.06234 0.0102 0.01365 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.335 0.531 0.309 0.0935\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 27/99 2.08G 0.06255 0.01035 0.006395 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.545 0.54 0.558 0.162\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 28/99 2.08G 0.06099 0.01005 0.006439 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.589 0.565 0.594 0.187\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 29/99 2.08G 0.06185 0.009531 0.005472 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.723 0.607 0.694 0.255\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 30/99 2.08G 0.05822 0.01011 0.005959 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.718 0.569 0.666 0.227\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 31/99 2.08G 0.05904 0.009549 0.005451 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.637 0.638 0.664 0.241\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 32/99 2.08G 0.05735 0.01051 0.00588 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.747 0.643 0.712 0.254\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 33/99 2.08G 0.05428 0.008754 0.005026 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.562 0.742 0.657 0.197\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 34/99 2.08G 0.05328 0.009273 0.005935 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.694 0.742 0.726 0.232\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 35/99 2.08G 0.05443 0.009382 0.004687 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.587 0.718 0.654 0.245\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 36/99 2.08G 0.05379 0.008759 0.003353 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.674 0.664 0.668 0.203\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 37/99 2.08G 0.05206 0.008254 0.003142 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.787 0.794 0.829 0.311\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 38/99 2.08G 0.05829 0.008697 0.004519 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.788 0.803 0.851 0.352\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 39/99 2.08G 0.05261 0.007873 0.003513 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.648 0.783 0.724 0.286\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 40/99 2.08G 0.04827 0.009399 0.003718 13 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.831 0.772 0.835 0.352\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 41/99 2.08G 0.04786 0.009583 0.004975 14 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.861 0.801 0.897 0.414\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 42/99 2.08G 0.05037 0.007538 0.003178 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.89 0.752 0.887 0.377\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 43/99 2.08G 0.04798 0.00858 0.003711 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.832 0.769 0.857 0.384\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 44/99 2.08G 0.04664 0.009331 0.002942 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.687 0.8 0.728 0.315\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 45/99 2.08G 0.04832 0.008557 0.003522 14 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.833 0.753 0.854 0.339\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 46/99 2.08G 0.04498 0.008594 0.002365 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.876 0.796 0.874 0.331\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 47/99 2.08G 0.04477 0.007792 0.003881 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.83 0.867 0.907 0.418\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 48/99 2.08G 0.04848 0.007692 0.002047 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.846 0.879 0.916 0.387\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 49/99 2.08G 0.04399 0.00756 0.002722 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.783 0.866 0.854 0.385\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 50/99 2.08G 0.04445 0.00848 0.002784 19 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.886 0.891 0.933 0.469\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 51/99 2.08G 0.04444 0.007793 0.001998 13 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.909 0.885 0.933 0.487\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 52/99 2.08G 0.04425 0.007719 0.003184 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.825 0.891 0.927 0.491\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 53/99 2.08G 0.04217 0.007922 0.003092 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.94 0.841 0.948 0.509\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 54/99 2.08G 0.03956 0.006778 0.00208 3 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.825 0.825 0.849 0.405\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 55/99 2.08G 0.03961 0.006825 0.00205 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.794 0.921 0.92 0.437\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 56/99 2.08G 0.04057 0.007253 0.002727 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.901 0.869 0.938 0.504\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 57/99 2.08G 0.03977 0.007841 0.002813 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.913 0.932 0.958 0.499\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 58/99 2.08G 0.04072 0.007157 0.002212 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.907 0.902 0.947 0.505\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 59/99 2.08G 0.03957 0.007615 0.00231 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.944 0.943 0.968 0.528\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 60/99 2.08G 0.03862 0.007188 0.001953 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.944 0.943 0.969 0.538\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 61/99 2.08G 0.03859 0.007565 0.002956 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.949 0.937 0.974 0.518\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 62/99 2.08G 0.03875 0.007161 0.003702 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.934 0.919 0.966 0.385\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 63/99 2.08G 0.03722 0.006528 0.001811 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.967 0.937 0.981 0.596\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 64/99 2.08G 0.03685 0.007454 0.002233 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.947 0.949 0.977 0.586\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 65/99 2.08G 0.03498 0.006493 0.002524 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.859 0.938 0.943 0.508\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 66/99 2.08G 0.03687 0.006779 0.002774 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.957 0.951 0.982 0.572\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 67/99 2.08G 0.035 0.006943 0.001782 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.949 0.958 0.978 0.563\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 68/99 2.08G 0.03599 0.006265 0.002749 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.929 0.941 0.973 0.492\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 69/99 2.08G 0.03785 0.006824 0.001213 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.916 0.942 0.975 0.51\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 70/99 2.08G 0.03698 0.006897 0.001501 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.969 0.955 0.983 0.573\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 71/99 2.08G 0.03685 0.006234 0.00163 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.954 0.928 0.977 0.58\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 72/99 2.08G 0.03347 0.007197 0.002253 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.944 0.948 0.977 0.582\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 73/99 2.08G 0.03425 0.006328 0.001422 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.935 0.95 0.972 0.575\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 74/99 2.08G 0.03445 0.006145 0.001638 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.952 0.963 0.985 0.598\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 75/99 2.08G 0.03561 0.006504 0.001263 7 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.95 0.937 0.972 0.573\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 76/99 2.08G 0.03406 0.007234 0.001578 14 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.954 0.944 0.981 0.608\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 77/99 2.08G 0.03299 0.007192 0.001044 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.967 0.963 0.983 0.618\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 78/99 2.08G 0.03213 0.007016 0.001574 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.964 0.975 0.988 0.601\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 79/99 2.08G 0.03057 0.005542 0.0009706 5 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.965 0.963 0.986 0.627\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 80/99 2.08G 0.03095 0.006986 0.001438 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.978 0.957 0.987 0.646\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 81/99 2.08G 0.03296 0.006044 0.001274 4 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.956 0.97 0.99 0.635\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 82/99 2.08G 0.02938 0.006094 0.001583 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.962 0.97 0.99 0.656\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 83/99 2.08G 0.03094 0.006903 0.001132 12 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.971 0.963 0.99 0.636\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 84/99 2.08G 0.03164 0.006661 0.001052 10 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.966 0.963 0.99 0.643\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 85/99 2.08G 0.03397 0.006788 0.00127 8 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.942 0.97 0.989 0.644\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 86/99 2.08G 0.02924 0.006947 0.0008702 18 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.942 0.972 0.989 0.649\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 87/99 2.08G 0.02847 0.006582 0.001598 14 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.977 0.96 0.989 0.657\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 88/99 2.08G 0.03033 0.007059 0.001871 14 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.975 0.976 0.99 0.664\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 89/99 2.08G 0.02811 0.006297 0.00143 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.975 0.971 0.988 0.656\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 90/99 2.08G 0.03078 0.006939 0.001127 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.974 0.972 0.989 0.674\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 91/99 2.08G 0.02822 0.006083 0.0008664 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.969 0.97 0.989 0.666\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 92/99 2.08G 0.02742 0.0058 0.0007475 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.971 0.967 0.989 0.683\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 93/99 2.08G 0.02799 0.006099 0.0009196 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.973 0.974 0.99 0.695\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 94/99 2.08G 0.02885 0.005759 0.001305 6 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.975 0.98 0.992 0.679\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 95/99 2.08G 0.02911 0.006442 0.00188 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.973 0.967 0.992 0.679\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 96/99 2.08G 0.0256 0.005368 0.0006449 9 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.979 0.966 0.99 0.679\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 97/99 2.08G 0.02798 0.006242 0.001544 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.981 0.966 0.99 0.696\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 98/99 2.08G 0.02912 0.00642 0.001193 11 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.981 0.966 0.99 0.697\r\n",
"\r\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\r\n",
" 99/99 2.08G 0.02788 0.006269 0.001092 15 320: 1\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.981 0.972 0.993 0.701\r\n",
"\r\n",
"100 epochs completed in 5.531 hours.\r\n",
"Optimizer stripped from runs/train/yolo_glasses_det/weights/last.pt, 14.3MB\r\n",
"Optimizer stripped from runs/train/yolo_glasses_det/weights/best.pt, 14.3MB\r\n",
"\r\n",
"Validating runs/train/yolo_glasses_det/weights/best.pt...\r\n",
"Fusing layers... \r\n",
"YOLOv5s summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs\r\n",
" Class Images Instances P R mAP50 \r\n",
" all 133 140 0.981 0.972 0.993 0.699\r\n",
" no_glasses 133 60 0.983 0.993 0.994 0.697\r\n",
" with_glasses 133 80 0.979 0.95 0.991 0.702\r\n",
"Results saved to \u001b[1mruns/train/yolo_glasses_det\u001b[0m\r\n"
]
}
],
"source": [
"!python train.py --img 320 --cfg yolov5s.yaml --hyp hyp.scratch-low.yaml --batch 32 --epochs 100 --data my_dataset.yaml --weights yolov5s.pt --workers 24 --name yolo_glasses_det"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e272116a",
"metadata": {
"papermill": {
"duration": 0.058453,
"end_time": "2023-04-08T15:39:09.920777",
"exception": false,
"start_time": "2023-04-08T15:39:09.862324",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 20155.449782,
"end_time": "2023-04-08T15:39:10.539974",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2023-04-08T10:03:15.090192",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0125/018/125018496.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f7ba31cf",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:39.286194Z",
"iopub.status.busy": "2023-04-08T10:05:39.285649Z",
"iopub.status.idle": "2023-04-08T10:05:40.507279Z",
"shell.execute_reply": "2023-04-08T10:05:40.506122Z"
},
"papermill": {
"duration": 1.235197,
"end_time": "2023-04-08T10:05:40.510154",
"exception": false,
"start_time": "2023-04-08T10:05:39.274957",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6cf91b04",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.528150Z",
"iopub.status.busy": "2023-04-08T10:05:40.527724Z",
"iopub.status.idle": "2023-04-08T10:05:40.627023Z",
"shell.execute_reply": "2023-04-08T10:05:40.625885Z"
},
"papermill": {
"duration": 0.110881,
"end_time": "2023-04-08T10:05:40.629757",
"exception": false,
"start_time": "2023-04-08T10:05:40.518876",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from sklearn.datasets import load_diabetes\n",
"x,y = load_diabetes(return_X_y=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fe450e54",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.646872Z",
"iopub.status.busy": "2023-04-08T10:05:40.646010Z",
"iopub.status.idle": "2023-04-08T10:05:40.654910Z",
"shell.execute_reply": "2023-04-08T10:05:40.653676Z"
},
"papermill": {
"duration": 0.020835,
"end_time": "2023-04-08T10:05:40.658154",
"exception": false,
"start_time": "2023-04-08T10:05:40.637319",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n",
" 0.01990842, -0.01764613],\n",
" [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
" -0.06832974, -0.09220405],\n",
" [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n",
" 0.00286377, -0.02593034],\n",
" ...,\n",
" [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n",
" -0.04687948, 0.01549073],\n",
" [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n",
" 0.04452837, -0.02593034],\n",
" [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
" -0.00421986, 0.00306441]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0b33b8d6",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.675121Z",
"iopub.status.busy": "2023-04-08T10:05:40.674685Z",
"iopub.status.idle": "2023-04-08T10:05:40.686543Z",
"shell.execute_reply": "2023-04-08T10:05:40.685299Z"
},
"papermill": {
"duration": 0.023194,
"end_time": "2023-04-08T10:05:40.688938",
"exception": false,
"start_time": "2023-04-08T10:05:40.665744",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([151., 75., 141., 206., 135., 97., 138., 63., 110., 310., 101.,\n",
" 69., 179., 185., 118., 171., 166., 144., 97., 168., 68., 49.,\n",
" 68., 245., 184., 202., 137., 85., 131., 283., 129., 59., 341.,\n",
" 87., 65., 102., 265., 276., 252., 90., 100., 55., 61., 92.,\n",
" 259., 53., 190., 142., 75., 142., 155., 225., 59., 104., 182.,\n",
" 128., 52., 37., 170., 170., 61., 144., 52., 128., 71., 163.,\n",
" 150., 97., 160., 178., 48., 270., 202., 111., 85., 42., 170.,\n",
" 200., 252., 113., 143., 51., 52., 210., 65., 141., 55., 134.,\n",
" 42., 111., 98., 164., 48., 96., 90., 162., 150., 279., 92.,\n",
" 83., 128., 102., 302., 198., 95., 53., 134., 144., 232., 81.,\n",
" 104., 59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
" 173., 180., 84., 121., 161., 99., 109., 115., 268., 274., 158.,\n",
" 107., 83., 103., 272., 85., 280., 336., 281., 118., 317., 235.,\n",
" 60., 174., 259., 178., 128., 96., 126., 288., 88., 292., 71.,\n",
" 197., 186., 25., 84., 96., 195., 53., 217., 172., 131., 214.,\n",
" 59., 70., 220., 268., 152., 47., 74., 295., 101., 151., 127.,\n",
" 237., 225., 81., 151., 107., 64., 138., 185., 265., 101., 137.,\n",
" 143., 141., 79., 292., 178., 91., 116., 86., 122., 72., 129.,\n",
" 142., 90., 158., 39., 196., 222., 277., 99., 196., 202., 155.,\n",
" 77., 191., 70., 73., 49., 65., 263., 248., 296., 214., 185.,\n",
" 78., 93., 252., 150., 77., 208., 77., 108., 160., 53., 220.,\n",
" 154., 259., 90., 246., 124., 67., 72., 257., 262., 275., 177.,\n",
" 71., 47., 187., 125., 78., 51., 258., 215., 303., 243., 91.,\n",
" 150., 310., 153., 346., 63., 89., 50., 39., 103., 308., 116.,\n",
" 145., 74., 45., 115., 264., 87., 202., 127., 182., 241., 66.,\n",
" 94., 283., 64., 102., 200., 265., 94., 230., 181., 156., 233.,\n",
" 60., 219., 80., 68., 332., 248., 84., 200., 55., 85., 89.,\n",
" 31., 129., 83., 275., 65., 198., 236., 253., 124., 44., 172.,\n",
" 114., 142., 109., 180., 144., 163., 147., 97., 220., 190., 109.,\n",
" 191., 122., 230., 242., 248., 249., 192., 131., 237., 78., 135.,\n",
" 244., 199., 270., 164., 72., 96., 306., 91., 214., 95., 216.,\n",
" 263., 178., 113., 200., 139., 139., 88., 148., 88., 243., 71.,\n",
" 77., 109., 272., 60., 54., 221., 90., 311., 281., 182., 321.,\n",
" 58., 262., 206., 233., 242., 123., 167., 63., 197., 71., 168.,\n",
" 140., 217., 121., 235., 245., 40., 52., 104., 132., 88., 69.,\n",
" 219., 72., 201., 110., 51., 277., 63., 118., 69., 273., 258.,\n",
" 43., 198., 242., 232., 175., 93., 168., 275., 293., 281., 72.,\n",
" 140., 189., 181., 209., 136., 261., 113., 131., 174., 257., 55.,\n",
" 84., 42., 146., 212., 233., 91., 111., 152., 120., 67., 310.,\n",
" 94., 183., 66., 173., 72., 49., 64., 48., 178., 104., 132.,\n",
" 220., 57.])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7ecf4992",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.706335Z",
"iopub.status.busy": "2023-04-08T10:05:40.705911Z",
"iopub.status.idle": "2023-04-08T10:05:40.714413Z",
"shell.execute_reply": "2023-04-08T10:05:40.713373Z"
},
"papermill": {
"duration": 0.020188,
"end_time": "2023-04-08T10:05:40.716885",
"exception": false,
"start_time": "2023-04-08T10:05:40.696697",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2,random_state=2)"
]
},
{
"cell_type": "markdown",
"id": "63db6626",
"metadata": {
"papermill": {
"duration": 0.007206,
"end_time": "2023-04-08T10:05:40.731828",
"exception": false,
"start_time": "2023-04-08T10:05:40.724622",
"status": "completed"
},
"tags": []
},
"source": [
"Let's First Check sciket_learn's LinearRegression class"
]
},
{
"cell_type": "markdown",
"id": "5d921fea",
"metadata": {
"papermill": {
"duration": 0.007158,
"end_time": "2023-04-08T10:05:40.746491",
"exception": false,
"start_time": "2023-04-08T10:05:40.739333",
"status": "completed"
},
"tags": []
},
"source": [
"# Sciket_learn's Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7b188d16",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.765278Z",
"iopub.status.busy": "2023-04-08T10:05:40.764221Z",
"iopub.status.idle": "2023-04-08T10:05:40.771985Z",
"shell.execute_reply": "2023-04-08T10:05:40.770851Z"
},
"papermill": {
"duration": 0.019437,
"end_time": "2023-04-08T10:05:40.774212",
"exception": false,
"start_time": "2023-04-08T10:05:40.754775",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.00188202, -0.04464164, -0.06979687, ..., -0.03949338,\n",
" -0.06291295, 0.04034337],\n",
" [-0.00914709, -0.04464164, 0.01103904, ..., -0.03949338,\n",
" 0.01703713, -0.0052198 ],\n",
" [ 0.02354575, 0.05068012, -0.02021751, ..., -0.03949338,\n",
" -0.09643322, -0.01764613],\n",
" ...,\n",
" [ 0.06350368, 0.05068012, -0.00405033, ..., -0.00259226,\n",
" 0.08449528, -0.01764613],\n",
" [-0.05273755, 0.05068012, -0.01806189, ..., 0.1081111 ,\n",
" 0.03605579, -0.04249877],\n",
" [ 0.00175052, 0.05068012, 0.05954058, ..., 0.1081111 ,\n",
" 0.06898221, 0.12732762]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xtrain"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e3ff6d95",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.791464Z",
"iopub.status.busy": "2023-04-08T10:05:40.791033Z",
"iopub.status.idle": "2023-04-08T10:05:40.812319Z",
"shell.execute_reply": "2023-04-08T10:05:40.811210Z"
},
"papermill": {
"duration": 0.033026,
"end_time": "2023-04-08T10:05:40.814814",
"exception": false,
"start_time": "2023-04-08T10:05:40.781788",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"slr=LinearRegression()\n",
"slr.fit(xtrain,ytrain)\n",
"y_pred=slr.predict(xtest)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1fb8613d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.832590Z",
"iopub.status.busy": "2023-04-08T10:05:40.832150Z",
"iopub.status.idle": "2023-04-08T10:05:40.839034Z",
"shell.execute_reply": "2023-04-08T10:05:40.837796Z"
},
"papermill": {
"duration": 0.018609,
"end_time": "2023-04-08T10:05:40.841313",
"exception": false,
"start_time": "2023-04-08T10:05:40.822704",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"array([ -9.16088483, -205.46225988, 516.68462383, 340.62734108,\n",
" -895.54360867, 561.21453306, 153.88478595, 126.73431596,\n",
" 861.12139955, 52.41982836])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"slr.coef_"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "912833e8",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.859125Z",
"iopub.status.busy": "2023-04-08T10:05:40.858132Z",
"iopub.status.idle": "2023-04-08T10:05:40.864281Z",
"shell.execute_reply": "2023-04-08T10:05:40.863447Z"
},
"papermill": {
"duration": 0.017083,
"end_time": "2023-04-08T10:05:40.866340",
"exception": false,
"start_time": "2023-04-08T10:05:40.849257",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"151.88334520854633"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"slr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "468df839",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.884397Z",
"iopub.status.busy": "2023-04-08T10:05:40.883694Z",
"iopub.status.idle": "2023-04-08T10:05:40.891068Z",
"shell.execute_reply": "2023-04-08T10:05:40.889834Z"
},
"papermill": {
"duration": 0.019164,
"end_time": "2023-04-08T10:05:40.893419",
"exception": false,
"start_time": "2023-04-08T10:05:40.874255",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0.4399387660024644"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2_score(ytest,y_pred)"
]
},
{
"cell_type": "markdown",
"id": "22b01622",
"metadata": {
"papermill": {
"duration": 0.007804,
"end_time": "2023-04-08T10:05:40.909295",
"exception": false,
"start_time": "2023-04-08T10:05:40.901491",
"status": "completed"
},
"tags": []
},
"source": [
"# Simple Linear Regression "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "12b101f9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.927262Z",
"iopub.status.busy": "2023-04-08T10:05:40.926808Z",
"iopub.status.idle": "2023-04-08T10:05:40.935398Z",
"shell.execute_reply": "2023-04-08T10:05:40.934166Z"
},
"papermill": {
"duration": 0.020504,
"end_time": "2023-04-08T10:05:40.937924",
"exception": false,
"start_time": "2023-04-08T10:05:40.917420",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"class LinearR:\n",
" def __init__(self):\n",
" self.m=None;\n",
" self.b=None;\n",
" def fit(self,X_train,Y_train):\n",
" den=0\n",
" num=0\n",
" for i in range(X_train.shape[0] ):\n",
" num=num+((X_train[i]-X_train.mean())*(Y_train[i]-Y_train.mean()))\n",
" den=den+((X_train[i]-X_train.mean())*(X_train[i]-X_train.mean()))\n",
" self.m=num/den\n",
" self.b=Y_train.mean()-(self.m*X_train.mean())\n",
" def predict(self,X_test):\n",
" return m*X_test+self.b\n",
" "
]
},
{
"cell_type": "markdown",
"id": "81a9f53a",
"metadata": {
"papermill": {
"duration": 0.007809,
"end_time": "2023-04-08T10:05:40.953962",
"exception": false,
"start_time": "2023-04-08T10:05:40.946153",
"status": "completed"
},
"tags": []
},
"source": [
"# Multiple Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7a0e780b",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.972072Z",
"iopub.status.busy": "2023-04-08T10:05:40.971295Z",
"iopub.status.idle": "2023-04-08T10:05:40.978267Z",
"shell.execute_reply": "2023-04-08T10:05:40.977150Z"
},
"papermill": {
"duration": 0.018871,
"end_time": "2023-04-08T10:05:40.980801",
"exception": false,
"start_time": "2023-04-08T10:05:40.961930",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(353, 10)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xtrain.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "705a1db4",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:40.999623Z",
"iopub.status.busy": "2023-04-08T10:05:40.998806Z",
"iopub.status.idle": "2023-04-08T10:05:41.005674Z",
"shell.execute_reply": "2023-04-08T10:05:41.004710Z"
},
"papermill": {
"duration": 0.018721,
"end_time": "2023-04-08T10:05:41.007838",
"exception": false,
"start_time": "2023-04-08T10:05:40.989117",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(353,)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ytrain.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "3ca4f3df",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:41.026345Z",
"iopub.status.busy": "2023-04-08T10:05:41.025937Z",
"iopub.status.idle": "2023-04-08T10:05:41.033332Z",
"shell.execute_reply": "2023-04-08T10:05:41.032486Z"
},
"papermill": {
"duration": 0.019552,
"end_time": "2023-04-08T10:05:41.035544",
"exception": false,
"start_time": "2023-04-08T10:05:41.015992",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"class MLR:\n",
" def __init__(self):\n",
" self.coeff_=None\n",
" self.intercept_=None\n",
" def fit(self,X_train,y_train):\n",
" X_train=np.insert(X_train,0,1,axis=1)\n",
" betas=np.linalg.inv(np.dot(X_train.T,X_train)).dot(X_train.T).dot(y_train)\n",
" self.intercept_=betas[0]\n",
" self.coeff_=betas[1:]\n",
" def predict(self,X_test):\n",
" y_pred=np.dot(X_test,self.coeff_)+self.intercept_\n",
" return y_pred\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a773510a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:41.054117Z",
"iopub.status.busy": "2023-04-08T10:05:41.053466Z",
"iopub.status.idle": "2023-04-08T10:05:41.062523Z",
"shell.execute_reply": "2023-04-08T10:05:41.061161Z"
},
"papermill": {
"duration": 0.022044,
"end_time": "2023-04-08T10:05:41.065876",
"exception": false,
"start_time": "2023-04-08T10:05:41.043832",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"mlr=MLR()\n",
"mlr.fit(xtrain,ytrain)\n",
"ypred=mlr.predict(xtest)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ea53989a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-08T10:05:41.095034Z",
"iopub.status.busy": "2023-04-08T10:05:41.094485Z",
"iopub.status.idle": "2023-04-08T10:05:41.228490Z",
"shell.execute_reply": "2023-04-08T10:05:41.227016Z"
},
"papermill": {
"duration": 0.150773,
"end_time": "2023-04-08T10:05:41.230313",
"exception": true,
"start_time": "2023-04-08T10:05:41.079540",
"status": "failed"
},
"tags": []
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'MLR' object has no attribute 'coef_'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_19/267678862.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'MLR' object has no attribute 'coef_'"
]
}
],
"source": [
"mlr.coef_"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "657dad1a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:20:02.001217Z",
"iopub.status.busy": "2023-04-01T15:20:02.000167Z",
"iopub.status.idle": "2023-04-01T15:20:02.008260Z",
"shell.execute_reply": "2023-04-01T15:20:02.007292Z",
"shell.execute_reply.started": "2023-04-01T15:20:02.001175Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"mlr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f00316d9",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:20:35.078644Z",
"iopub.status.busy": "2023-04-01T15:20:35.078173Z",
"iopub.status.idle": "2023-04-01T15:20:35.087851Z",
"shell.execute_reply": "2023-04-01T15:20:35.086601Z",
"shell.execute_reply.started": "2023-04-01T15:20:35.078604Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"r2_score(ytest,ypred)"
]
},
{
"cell_type": "markdown",
"id": "1cb7f55b",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"source": []
},
{
"cell_type": "markdown",
"id": "057a555a",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"source": [
"# Linear Regression Using Gradient Descent (Batch Gradient Descent)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f05f396d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:23:23.932442Z",
"iopub.status.busy": "2023-04-01T15:23:23.931963Z",
"iopub.status.idle": "2023-04-01T15:23:23.944474Z",
"shell.execute_reply": "2023-04-01T15:23:23.942907Z",
"shell.execute_reply.started": "2023-04-01T15:23:23.932386Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"class GD:\n",
" def __init__(self,):\n",
" \n",
" self.coeff_=None\n",
" self.intercept_=None\n",
" def fit(self,x_train,y_train,lr=0.01,epochs=100):\n",
" self.lr=lr\n",
" self.epochs=epochs\n",
" self.intercept_=0\n",
" self.coeff_=np.ones(x_train.shape[1])\n",
" \n",
" for i in range(self.epochs):\n",
" y_pred=self.intercept_+ np.dot(x_train,self.coeff_)\n",
" der=-2*np.mean(y_train-y_pred)\n",
" self.intercept_=self.intercept_ -(self.lr*der)\n",
" # coeffs\n",
" coeff_der=-2*(np.dot((y_train-y_pred),x_train)/(x_train.shape[1]))\n",
" self.coeff_=self.coeff_-(self.lr*coeff_der)\n",
" \n",
" print(self.intercept_,self.coeff_)\n",
" \n",
" def predict(self,x):\n",
" return np.dot(x,self.coeff_)+self.intercept_\n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6eebf63",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T14:59:05.369421Z",
"iopub.status.busy": "2023-04-01T14:59:05.368373Z",
"iopub.status.idle": "2023-04-01T14:59:05.378254Z",
"shell.execute_reply": "2023-04-01T14:59:05.377173Z",
"shell.execute_reply.started": "2023-04-01T14:59:05.369369Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"ytrain"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14983948",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:23:31.871845Z",
"iopub.status.busy": "2023-04-01T15:23:31.871427Z",
"iopub.status.idle": "2023-04-01T15:23:31.908268Z",
"shell.execute_reply": "2023-04-01T15:23:31.906754Z",
"shell.execute_reply.started": "2023-04-01T15:23:31.871806Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"gdlr=GD()\n",
"gdlr.fit(xtrain,ytrain,lr=0.1,epochs=1000)\n",
"Ypred=gdlr.predict(xtest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e15405ca",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:23:37.504463Z",
"iopub.status.busy": "2023-04-01T15:23:37.503705Z",
"iopub.status.idle": "2023-04-01T15:23:37.512389Z",
"shell.execute_reply": "2023-04-01T15:23:37.511200Z",
"shell.execute_reply.started": "2023-04-01T15:23:37.504390Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"gdlr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c42f5f1d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T15:24:23.840402Z",
"iopub.status.busy": "2023-04-01T15:24:23.839998Z",
"iopub.status.idle": "2023-04-01T15:24:23.850046Z",
"shell.execute_reply": "2023-04-01T15:24:23.848375Z",
"shell.execute_reply.started": "2023-04-01T15:24:23.840368Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"r2_score(ytest,Ypred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09f3903a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T14:52:19.448075Z",
"iopub.status.busy": "2023-04-01T14:52:19.447655Z",
"iopub.status.idle": "2023-04-01T14:52:19.467121Z",
"shell.execute_reply": "2023-04-01T14:52:19.465847Z",
"shell.execute_reply.started": "2023-04-01T14:52:19.448029Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "404b9eef",
"metadata": {
"execution": {
"iopub.execute_input": "2023-04-01T14:52:19.515251Z",
"iopub.status.busy": "2023-04-01T14:52:19.514457Z",
"iopub.status.idle": "2023-04-01T14:52:19.520084Z",
"shell.execute_reply": "2023-04-01T14:52:19.518899Z",
"shell.execute_reply.started": "2023-04-01T14:52:19.515205Z"
},
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": [
"# lr=LinearR()\n",
"# from sklearn.model_selection import train_test_split\n",
"# x=df.iloc[:,0:1]\n",
"# y=df.iloc[:,-1]\n",
"# X_train,X_test,y_train,y_test=train_test_split(x,y,random_state=2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e6ac8dc",
"metadata": {
"papermill": {
"duration": null,
"end_time": null,
"exception": null,
"start_time": null,
"status": "pending"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 13.458292,
"end_time": "2023-04-08T10:05:41.961976",
"environment_variables": {},
"exception": true,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2023-04-08T10:05:28.503684",
"version": "2.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0125/018/125018622.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"3f0146(...TRUNCATED) | 0125/019/125019060.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81f53062\",\n \"metadata\": (...TRUNCATED) | 0125/019/125019064.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"73dea9ca\",\n \"metadata\": (...TRUNCATED) | 0125/019/125019115.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\"metadata\":{\"accelerator\":\"GPU\",\"colab\":{\"provenance\":[],\"toc_visible\":true},\"gpuClas(...TRUNCATED) | 0125/019/125019131.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"e70352(...TRUNCATED) | 0125/019/125019196.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0b635fc9\",\n \"metadata\": (...TRUNCATED) | 0125/019/125019447.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"69ca2f(...TRUNCATED) | 0125/019/125019450.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0125/019/125019482.ipynb | s3://data-agents/kaggle-outputs/sharded/000_00125.jsonl.gz |
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 27