test_phone_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3946e60-8882-4800-b8ce-bdb30722a95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Setup complete. Using torch 2.0.1 (CPU)\n"
     ]
    }
   ],
   "source": [
    "%pip install -qr requirements.txt # install dependencies\n",
    "%pip install -q roboflow\n",
    "\n",
    "import torch\n",
    "import os\n",
    "from IPython.display import Image, clear_output  # to display images\n",
    "from roboflow import Roboflow\n",
    "\n",
    "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b279cdf-5241-4559-bd8f-030cb8eb4ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading Roboflow workspace...\n",
      "loading Roboflow project...\n",
      "Downloading Dataset Version Zip in Photo-Detector-V3-3 to yolov5pytorch: 100% [23270137 / 23270137] bytes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting Dataset Version Zip to Photo-Detector-V3-3 in yolov5pytorch:: 100%|█|\n"
     ]
    }
   ],
   "source": [
    "rf = Roboflow(api_key=\"NBUvQ56DQdrvX3vNmz90\")\n",
    "project = rf.workspace(\"aditya-kakarla-wfuyi\").project(\"photo-detector-v3\")\n",
    "dataset = project.version(3).download(\"yolov5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6266aa1-2dd5-4c03-923b-daa0cf833db6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/Users/adityakakarla/yolov5/Photo-Detector-V3-3/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=150, batch_size=16, imgsz=782, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 3 commits. Use 'git pull' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 v7.0-211-g94e943e Python-3.11.4 torch-2.0.1 CPU\n",
      "\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\n",
      "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "Overriding model.yaml nc=80 with nc=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs\n",
      "\n",
      "Transferred 343/349 items from yolov5s.pt\n",
      "WARNING ⚠️ --img-size 782 must be multiple of max stride 32, updating to 800\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\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /Users/adityakakarla/yolov5/Photo-Detector-V3-3/train/labels.cac\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (1.0GB ram): 100%|██████████| 549/549 [00:00<00:00, 2420.2\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /Users/adityakakarla/yolov5/Photo-Detector-V3-3/valid/labels.cache\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.3GB ram): 100%|██████████| 157/157 [00:00<00:00, 683.61it\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.61 anchors/target, 0.997 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/exp8/labels.jpg... \n",
      "Image sizes 800 train, 800 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp8\u001b[0m\n",
      "Starting training for 150 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      0/149         0G    0.07842    0.04231          0         17        800:  WARNING ⚠️ TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 50 but got size 49 for tensor number 1 in the list.\n",
      "      0/149         0G    0.08568    0.03427          0         14        800:  ^C\n",
      "      0/149         0G    0.08568    0.03427          0         14        800:  \n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/adityakakarla/yolov5/train.py\", line 647, in <module>\n",
      "    main(opt)\n",
      "  File \"/Users/adityakakarla/yolov5/train.py\", line 536, in main\n",
      "    train(opt.hyp, opt, device, callbacks)\n",
      "  File \"/Users/adityakakarla/yolov5/train.py\", line 317, in train\n",
      "    pred = model(imgs)  # forward\n",
      "           ^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/adityakakarla/yolov5/models/yolo.py\", line 209, in forward\n",
      "    return self._forward_once(x, profile, visualize)  # single-scale inference, train\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/adityakakarla/yolov5/models/yolo.py\", line 121, in _forward_once\n",
      "    x = m(x)  # run\n",
      "        ^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/adityakakarla/yolov5/models/common.py\", line 179, in forward\n",
      "    return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))\n",
      "                               ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/container.py\", line 217, in forward\n",
      "    input = module(input)\n",
      "            ^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/adityakakarla/yolov5/models/common.py\", line 132, in forward\n",
      "    return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))\n",
      "               ^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/Users/adityakakarla/yolov5/models/common.py\", line 68, in forward\n",
      "    return self.act(self.bn(self.conv(x)))\n",
      "                            ^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/conv.py\", line 463, in forward\n",
      "    return self._conv_forward(input, self.weight, self.bias)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"/opt/homebrew/lib/python3.11/site-packages/torch/nn/modules/conv.py\", line 459, in _conv_forward\n",
      "    return F.conv2d(input, weight, bias, self.stride,\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "!python train.py --img 782 --batch 16 --epochs 150 --data {dataset.location}/data.yaml --weights yolov5s.pt --cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c31ff3c2-d373-4a1b-a06f-6fb7036d9f49",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python detect.py --weights runs/train/exp/weights/best.pt --img 416 --conf 0.1 --source 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d7ca2be-615e-4b55-bf0c-7acc5605409c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "from IPython.display import Image, display\n",
    "\n",
    "for imageName in glob.glob('/content/yolov5/runs/detect/exp/*.jpg'): #assuming JPG\n",
    "    display(Image(filename=imageName))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5c00b3b-d611-410d-931f-edab4c13b1de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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