openMF · deepthi1107 · Mar 4, 2023 · Mar 5, 2023 · Mar 6, 2023 · Mar 18, 2023
diff --git a/DataCollection/ImageAugmentation.ipynb b/DataCollection/ImageAugmentation.ipynb
diff --git a/Object Detection/objectDetection_train.ipynb b/Object Detection/objectDetection_train.ipynb
@@ -0,0 +1,169 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wbvMlHd_QwMG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fd0147bf-9e26-41de-eb98-fd0c58c361cd"
+      },
+      "source": [
+        "!git clone https://github.com/ultralytics/yolov5  \n",
+        "%cd yolov5\n",
+        "%pip install -qr requirements.txt  \n",
+        "\n",
+        "import torch\n",
+        "import utils\n",
+        "display = utils.notebook_init()  "
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 CPU\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 25.5/107.7 GB disk)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# traing the YOLOV5 model\n",
+        "\n",
+        "!python train.py --img 640 --batch 16 --epochs 3 --data /content/yolov5/data/data.yaml --weights yolov5s.pt "
+      ],
+      "metadata": {
+        "id": "JIvXqot4f8d1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "86e52852-2746-4466-ea66-e1f3e46c2f45"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/yolov5/data/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, 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=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[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
+            "YOLOv5 🚀 v7.0-120-g3e55763 Python-3.9.16 torch-1.13.1+cu116 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[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\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",
+            "2023-03-15 11:20:33.924337: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+            "2023-03-15 11:20:35.565625: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
+            "2023-03-15 11:20:35.565860: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/nvidia/lib:/usr/local/nvidia/lib64\n",
+            "2023-03-15 11:20:35.565890: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
+            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
+            "100% 14.1M/14.1M [00:00<00:00, 80.7MB/s]\n",
+            "\n",
+            "Overriding model.yaml nc=80 with nc=52\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    153729  models.yolo.Detect                      [52, [[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, 7159873 parameters, 7159873 gradients, 16.4 GFLOPs\n",
+            "\n",
+            "Transferred 343/349 items from yolov5s.pt\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[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))\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/train/labels.cache... 3619 images, 117 backgrounds, 0 corrupt: 100% 3736/3736 [00:00<?, ?it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/data_imageClassification/object_detection/valid/images/labels.cache... 4000 images, 33 backgrounds, 0 corrupt: 100% 4033/4033 [00:00<?, ?it/s]\n",
+            "\n",
+            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
+            "Plotting labels to runs/train/exp/labels.jpg... \n",
+            "Image sizes 640 train, 640 val\n",
+            "Using 2 dataloader workers\n",
+            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
+            "Starting training for 3 epochs...\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
+            "        0/2         0G    0.08917    0.04332    0.09752         43        640: 100% 234/234 [1:32:34<00:00, 23.74s/it]\n",
+            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 127/127 [36:36<00:00, 17.30s/it]\n",
+            "                   all       4033      15159    0.00765      0.571    0.00917    0.00362\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
+            "        1/2         0G    0.04627    0.03401     0.0956         42        640: 100% 234/234 [1:32:10<00:00, 23.64s/it]\n",
+            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 127/127 [33:53<00:00, 16.01s/it]\n",
+            "                   all       4033      15159     0.0113      0.895     0.0142     0.0071\n",
+            "\n",
+            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
+            "        2/2         0G    0.03428    0.03075    0.09459         33        640: 100% 234/234 [1:32:29<00:00, 23.72s/it]\n",
+            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 127/127 [35:07<00:00, 16.59s/it]\n",
+            "                   all       4033      15159     0.0116      0.919     0.0149    0.00975\n",
+            "\n",
+            "3 epochs completed in 6.383 hours.\n",
+            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.7MB\n",
+            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.7MB\n",
+            "\n",
+            "Validating runs/train/exp/weights/best.pt...\n",
+            "Fusing layers... \n",
+            "Model summary: 157 layers, 7150369 parameters, 0 gradients, 16.2 GFLOPs\n",
+            "                 Class     Images  Instances          P          R      mAP50   mAP50-95:  61% 78/127 [22:11<14:06, 17.28s/it]"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "-GdpCP0TItC5"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
diff --git a/README.md b/README.md
@@ -1,12 +1,15 @@
-<img height='175' src="https://user-images.githubusercontent.com/37406965/51083189-d5dc3a80-173b-11e9-8ca0-28015e0893ac.png" align="left" hspace="1" vspace="1">
+<img height='90' src="https://user-images.githubusercontent.com/37406965/51083189-d5dc3a80-173b-11e9-8ca0-28015e0893ac.png" align="left" hspace="1" vspace="1">
 
 # PPI-Vision :iphone:
-The **Poverty Probability Index (PPI)** is a poverty measurement tool for organizations and businesses with a mission to serve the poor. Taking this into account Vision PPI is designed to facilitate this measurement process much accurate and efficient. 
-Vision PPI is a computer vision and machine learning based android app designed to help in filling out the PPI Survey. There are two broad aspects to this project - The android app which provides the interface to the field officer for conducting the survey and the machine learning models which are used in the backend of app to analyze the images captured. 
+The **Poverty Probability Index (PPI)** is a poverty measurement tool for organizations and businesses with a mission to serve the poor. Taking this into account Vision PPI is designed to facilitate this measurement process much accurate and efficient. Vision PPI is a computer vision and machine learning based android app designed to help in filling out PPI Survey. 
+
+*There are two broad aspects to this project:-*
+- The android app which provides the interface to the field officer for conducting the survey.
+- The machine learning models which are used in the backend of app to analyze the images captured. 
 
 
 ## Wiki 📙
-For more information about the usecases of the project and details about PPI, the API used and demo credentials, please take a look at the [project wiki](https://github.com/openMF/ppi-vision/wiki). 
+For more information about the usecases of the project and details about PPI, the API used and demo credentials, please take a look at the **[project wiki](https://github.com/openMF/ppi-vision/wiki)**. 
 
 ## Screenshots :camera:
 ![Untitled design (5)](https://user-images.githubusercontent.com/75531664/189319200-f7f3b143-2757-46c8-9342-0021fada2ca2.png)

diff --git a/requirements.txt b/requirements.txt
@@ -1,12 +1,13 @@
-numpy==1.19.2
-matplotlib==3.3.2
+numpy==1.22.0
+matplotlib==3.6.3
 sklearn==0.0
-torch==1.6.0
-torchvision==0.7.0
-opencv-contrib-python==4.3.0.36
-opencv-python==4.3.0.36
-scikit-image==0.17.2
-scikit-learn==0.23.2
-scipy==1.5.2
-Pillow==7.2.0
-pyflakes==2.2.0
+torch==1.13.1
+torchvision==0.14.1
+opencv-contrib-python==4.7.0.68
+opencv-python==4.7.0.68
+scikit-image==0.19.3
+scikit-learn==0.24.2
+scipy==1.10.0
+Pillow==9.0.1
+pyflakes==2.5.0
+
diff --git a/score_calculation.zip b/score_calculation.zip