Internal change

PiperOrigin-RevId: 460830316

Internal change
PiperOrigin-RevId: 460830316
481742c5 · Fan Yang · A. Unique TensorFlower · 675ba741 · 481742c5 · 481742c5
Commit 481742c5 authored Jul 13, 2022 by Fan Yang Committed by A. Unique TensorFlower Jul 13, 2022
8 changed files
--- a/official/projects/waste_identification_ml/README.md
+++ b/official/projects/waste_identification_ml/README.md
+# Waste Identification ML - ( Mask RCNN with TF Lite )
+
+This projects aims to develop a TensorFlow Lite model based Mask RCNN instance
+segmentation model for on-device inference.
+
+## Background
+
+The sustainability team at Google wants to build a computer vision based ML
+model for waste identification. An ML model which detects the trash objects in
+the images and can identify their material type and packaging type. This
+projects aims to accelerate innovation in the waste management industry by
+providing no-cist open sourced ML models. This would help reduce barriers for
+technology adoption, and provide much needed efficientcy, traceability &
+transparency, which inturn can help increase recycling rates.
+
+## Code Structure
+This is an implementation of Mask RCNN based on Python 3 and Tensorflow 2.x. The
+model generates bounding boxes and segmentation masks for each instance of an
+object in the image. The repository includes :
+
+* Source code for training a Mask RCNN model.
+* Inference code
+* Pre-trained weights for inferencing
+* Docker to deploy the model in any operating system and run.
+* Jupyter notebook to visualize the detection pipeline at every step.
+* Evaluation metric of the validation dataset.
+* Example of training on your own custom dataset.
+
+The code is designed in such a way so that it can be extend. If you use in your
+research or industrial solutions then please consider citing this repository.
+
+## Pre-requisites
+
+## Prepare dataset
+
+## Setup virtual systems for training
+
+### ***Start a TPU v3-32 instance***
+
+-   [x] Set up a Google cloud account on GCP
+-   [x] Go to the cloud console and create a new project.
+-   [x] While setting up your project, you will be asked to set up a billing
+    account. You will only be charged after you start using it.
+-   [x] Create a cloud TPU project
+-   [x] Link for the above 4 steps can be
+    [found here](https://cloud.google.com/tpu/docs/setup-gcp-account)
+-   [x] Once the project is created, select the project from the cloud console.
+-   [x] On the top right, click cloud shell to open the terminal. See
+    [TPU Quickstart](https://cloud.google.com/tpu/docs/quick-starts) for
+    instructions.
+    An example command would look like:
+    ```bash
+    ctpu up --name
+    <tpu-name> --zone <zone> --tpu-size=v3-32 --tf-version nightly --project
+    <project ID>
+    ```
+    **Example** -
+
+-   This model requires TF version >= 2.5. Currently, that is only available via
+    a nightly build on Cloud.
+
+-   You can check TPU types with their cores and memory
+    [here](https://cloud.google.com/tpu/docs/types-zones#tpu-vm) and select
+    accordingly.
+
+-   CAREFULLY choose a TPU type which can be turned ON and OFF after usage.
+    Preferred one is below - `bash ctpu up --name waste-identification --zone
+    us-central1-a --tpu-size=v3-8 --tf-version nightly --project
+    waste-identification-ml` After the execution of the above command you will
+    see 2 virtual devices with name "waste-identification" each in TPU and
+    COMPUTE ENGINE section.
+
+### ***Get into the virtual machine***
+
+The virtual machine which is a TPU host can be seen in the COMPUTE ENGINE
+section of GCP. We will use this virtual machine to start the training process.
+This machine will use another virtual instance of TPU that is found in the TPU
+section of the GCP. To get inside the TPU host virtual machine :
+
+-   Go the COMPUTE ENGINE section in the GCP
+-   Find your instance there
+-   Under the "Connect" tab of your instance you will see "SSH"
+-   click on SSH and it will open another window which will take you inside the
+    virtual machine.
+-   Use the following commands inside the virtual machine window :
+
+```bash
+$ git clone https://github.com/tensorflow/models.git
+$ cd models
+$ pip3 install -r official/requirements.txt
+```
+
+## Roadmap
+
+-   Provide ML model pre-trained weights with Docker to run for detection of
+    Material type.
+
+-   Deploy a model tp detect the packaging type of the objects.
+
+-   Deploy a model to detect the brands of the object.
--- a/official/projects/waste_identification_ml/pre_processing/coco_to_tfrecord.ipynb
+++ b/official/projects/waste_identification_ml/pre_processing/coco_to_tfrecord.ipynb
+{"cells":[{"cell_type":"markdown","source":["# Conversion of COCO annotation JSON file to TFRecords"],"metadata":{"id":"SsIv6LYT84gm"}},{"cell_type":"markdown","source":["Given a COCO annotated JSON file, your goal is to convert it into a TFRecords  file necessary to train with the Mask RCNN model.\n","\n","To accomplish this task, you will clone the TensorFlow Model Garden repo. The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users.\n","\n","This notebook is an end to end example. When you run the notebook, it will take COCO annotated JSON train and test files as an input and will convert them into TFRecord files. You can also output sharded TFRecord files in case your training and validation data is huge. It makes it easier for the algorithm to read and access the data."],"metadata":{"id":"zl7o2xEW9IbX"}},{"cell_type":"markdown","source":["**Note** - In this example, we assume that all our data is saved on Google drive and we will also write our outputs to Google drive. We also assume that the script will be used as a Google Colab notebook. But this can be changed according to the needs of users. They can modify this in case they are working on their local workstation, remote server or any other database. This colab notebook can be changed to a regular jupyter notebook running on a local machine according to the need of the users."],"metadata":{"id":"g3OHfWQBpYVB"}},{"cell_type":"markdown","metadata":{"id":"CRwVTTPuED_1"},"source":["## Run the below command to connect to your google drive"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4439,"status":"ok","timestamp":1650585781180,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"hdRAEurMA3zi","outputId":"7212e558-af5d-4cb2-dd1f-6e634f5fca0a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting tensorflow-addons\n","  Downloading tensorflow_addons-0.16.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n","\u001b[?25l\r\u001b[K     |▎                               | 10 kB 22.4 MB/s eta 0:00:01\r\u001b[K     |▋                               | 20 kB 8.9 MB/s eta 0:00:01\r\u001b[K   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|███████████████████████████████▏| 1.1 MB 5.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 1.1 MB 5.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 1.1 MB 5.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 1.1 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n","Installing collected packages: tensorflow-addons\n","Successfully installed tensorflow-addons-0.16.1\n"]}],"source":["!pip install tensorflow-addons"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bBN0CZWlD7zl"},"outputs":[],"source":["# import libraries\n","from google.colab import drive\n","import sys\n","from configparser import ConfigParser"]},{"cell_type":"code","source":["# \"opencv-python-headless\" version should be same of \"opencv-python\"\n","import pkg_resources\n","version_number = pkg_resources.get_distribution(\"opencv-python\").version\n","\n","!pip install opencv-python-headless==$version_number"],"metadata":{"id":"leap_jk5fq_v","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650585791722,"user_tz":420,"elapsed":5939,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"b5608bb5-24df-4fb1-9885-649ceca98a26"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting opencv-python-headless==4.1.2.30\n","  Downloading opencv_python_headless-4.1.2.30-cp37-cp37m-manylinux1_x86_64.whl (21.8 MB)\n","\u001b[K     |████████████████████████████████| 21.8 MB 62.9 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python-headless==4.1.2.30) (1.21.6)\n","Installing collected packages: opencv-python-headless\n","Successfully installed opencv-python-headless-4.1.2.30\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i80tEP0pEJif","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650585818263,"user_tz":420,"elapsed":22456,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"cb0d8dde-8852-49eb-e6d7-33653722eee0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","Successful\n"]}],"source":["# connect to google drive\n","drive.mount('/content/gdrive')\n","\n","# making an alias for the root path\n","try:\n","  !ln -s /content/gdrive/My\\ Drive/ /mydrive\n","  print('Successful')\n","except Exception as e:\n","  print(e)\n","  print('Not successful')"]},{"cell_type":"markdown","metadata":{"id":"w40-VpWXU-Hu"},"source":["## Clone TensorFlow Model Garden repository"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qjhCOR3ZYB0T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650585823614,"user_tz":420,"elapsed":167,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"7dc2762f-bae2-4361-8e3f-d7f919ebfa1d"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/gdrive/My Drive/TFVision\n"]}],"source":["# move to the specified folder where you want to clone\n","%cd $tensorflow_model_folder"]},{"cell_type":"code","source":["# clone the Model Garden directory for Tensorflow where all the config files and scripts are located for this project. \n","# project folder name is - 'waste_identification_ml'\n","!git clone https://github.com/tensorflow/models.git "],"metadata":{"id":"Vh42KtozpqeT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Go to the model folder\n","%cd models"],"metadata":{"id":"wm-k6-S4pr_B"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cbPxppeNWsD8"},"source":["## **MUST CHANGE** - Import the path and parameters"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6sMnfRIeWvKG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650585820740,"user_tz":420,"elapsed":1207,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"f463824e-61a8-4512-b02a-a054f721e907"},"outputs":[{"output_type":"stream","name":"stdout","text":["/mydrive/TFVision/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/train/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/val/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/Total_images/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_train.json\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/Total_images/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_val.json\n"]}],"source":["config = ConfigParser()\n","\n","# path to the config file defining parameters\n","# config.ini file is an important file where all the parameter variables are located\n","# config.ini file resides in the 'config' folder which is in the 'pre_processing' folder\n","config.read('official/projects/waste_identification_ml/pre_processing/config/config.ini')\n","\n","# path where you want to close tensorflow model directory\n","tensorflow_model_folder = config['tfrecord']['tensorflow_model_folder']\n","print(tensorflow_model_folder)\n","\n","# path where you want to put the traiing tfrecord file\n","training_data_folder = config['tfrecord']['training_data_folder']\n","print(training_data_folder)\n","\n","# path where you want to put the validation tfrecord file\n","validation_data_folder = config['tfrecord']['validation_data_folder']\n","print(validation_data_folder)\n","\n","# path where all training images are located\n","training_images_folder = config['tfrecord']['training_images_folder']\n","print(training_images_folder)\n","\n","# path of the training annotation file that needs to be converted\n","training_annotation_file = config['tfrecord']['training_annotation_file']\n","print(training_annotation_file)\n","\n","# path where all validation images are located\n","validation_images_folder = config['tfrecord']['validation_images_folder']\n","print(validation_images_folder)\n","\n","# path of the validation annotation file that needs tobe converted\n","validation_annotation_file = config['tfrecord']['validation_annotation_file']\n","print(validation_annotation_file)"]},{"cell_type":"markdown","metadata":{"id":"xNe2NuqjV4uW"},"source":["## Create TFRecord for training data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J9Nz75g0oJkI"},"outputs":[],"source":["# create a folder for validation data\n","!mkdir -p $validation_data_folder\n","\n","# create a folder for training data\n","!mkdir -p $training_data_folder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mjsai7PDAxgp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650590090694,"user_tz":420,"elapsed":3284882,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"c78c7eaa-36e0-48e0-ba2c-3e674bdc5402"},"outputs":[{"output_type":"stream","name":"stdout","text":["I0422 00:06:23.072771 139705362556800 create_coco_tf_record.py:494] writing to output path: /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/train/\n","I0422 00:06:25.089654 139705362556800 create_coco_tf_record.py:366] Building bounding box index.\n","I0422 00:06:25.115955 139705362556800 create_coco_tf_record.py:377] 0 images are missing bboxes.\n","I0422 00:07:39.273266 139705362556800 tfrecord_lib.py:168] On image 0\n","I0422 00:09:03.214606 139705362556800 tfrecord_lib.py:168] On image 100\n","I0422 00:10:14.332473 139705362556800 tfrecord_lib.py:168] On image 200\n","I0422 00:11:11.556596 139705362556800 tfrecord_lib.py:168] On image 300\n","I0422 00:12:11.437826 139705362556800 tfrecord_lib.py:168] On image 400\n","I0422 00:13:13.166231 139705362556800 tfrecord_lib.py:168] On image 500\n","I0422 00:14:21.695016 139705362556800 tfrecord_lib.py:168] On image 600\n","I0422 00:15:24.191824 139705362556800 tfrecord_lib.py:168] On image 700\n","I0422 00:16:48.620902 139705362556800 tfrecord_lib.py:168] On image 800\n","I0422 00:17:48.565592 139705362556800 tfrecord_lib.py:168] On image 900\n","I0422 00:18:41.091029 139705362556800 tfrecord_lib.py:168] On image 1000\n","I0422 00:19:39.844225 139705362556800 tfrecord_lib.py:168] On image 1100\n","I0422 00:20:45.108587 139705362556800 tfrecord_lib.py:168] On image 1200\n","I0422 00:22:13.738559 139705362556800 tfrecord_lib.py:168] On image 1300\n","I0422 00:23:13.147292 139705362556800 tfrecord_lib.py:168] On image 1400\n","I0422 00:24:06.315325 139705362556800 tfrecord_lib.py:168] On image 1500\n","I0422 00:24:59.421572 139705362556800 tfrecord_lib.py:168] On image 1600\n","I0422 00:25:45.958540 139705362556800 tfrecord_lib.py:168] On image 1700\n","I0422 00:26:35.475085 139705362556800 tfrecord_lib.py:168] On image 1800\n","I0422 00:27:38.255803 139705362556800 tfrecord_lib.py:168] On image 1900\n","I0422 00:28:37.250636 139705362556800 tfrecord_lib.py:168] On image 2000\n","I0422 00:29:38.937792 139705362556800 tfrecord_lib.py:168] On image 2100\n","I0422 00:30:24.683607 139705362556800 tfrecord_lib.py:168] On image 2200\n","I0422 00:31:13.964802 139705362556800 tfrecord_lib.py:168] On image 2300\n","I0422 00:32:06.411041 139705362556800 tfrecord_lib.py:168] On image 2400\n","I0422 00:33:06.038232 139705362556800 tfrecord_lib.py:168] On image 2500\n","I0422 00:34:15.721037 139705362556800 tfrecord_lib.py:168] On image 2600\n","I0422 00:35:19.886712 139705362556800 tfrecord_lib.py:168] On image 2700\n","I0422 00:36:32.834578 139705362556800 tfrecord_lib.py:168] On image 2800\n","I0422 00:38:00.137243 139705362556800 tfrecord_lib.py:168] On image 2900\n","I0422 00:39:24.083769 139705362556800 tfrecord_lib.py:168] On image 3000\n","I0422 00:40:47.815561 139705362556800 tfrecord_lib.py:168] On image 3100\n","I0422 00:42:01.868806 139705362556800 tfrecord_lib.py:168] On image 3200\n","I0422 00:43:10.464518 139705362556800 tfrecord_lib.py:168] On image 3300\n","I0422 00:44:08.492330 139705362556800 tfrecord_lib.py:168] On image 3400\n","I0422 00:45:06.637591 139705362556800 tfrecord_lib.py:168] On image 3500\n","I0422 00:46:17.144057 139705362556800 tfrecord_lib.py:168] On image 3600\n","I0422 00:47:34.219212 139705362556800 tfrecord_lib.py:168] On image 3700\n","I0422 00:48:47.535176 139705362556800 tfrecord_lib.py:168] On image 3800\n","I0422 00:49:44.018001 139705362556800 tfrecord_lib.py:168] On image 3900\n","I0422 00:50:46.843277 139705362556800 tfrecord_lib.py:168] On image 4000\n","I0422 00:51:42.749161 139705362556800 tfrecord_lib.py:168] On image 4100\n","I0422 00:52:29.118489 139705362556800 tfrecord_lib.py:168] On image 4200\n","I0422 00:53:12.499863 139705362556800 tfrecord_lib.py:168] On image 4300\n","I0422 00:54:02.751904 139705362556800 tfrecord_lib.py:168] On image 4400\n","I0422 00:54:54.855237 139705362556800 tfrecord_lib.py:168] On image 4500\n","I0422 00:56:11.432259 139705362556800 tfrecord_lib.py:168] On image 4600\n","I0422 00:57:12.901312 139705362556800 tfrecord_lib.py:168] On image 4700\n","I0422 00:58:15.347571 139705362556800 tfrecord_lib.py:168] On image 4800\n","I0422 00:59:13.046698 139705362556800 tfrecord_lib.py:168] On image 4900\n","I0422 01:00:38.408758 139705362556800 tfrecord_lib.py:168] On image 5000\n","I0422 01:02:03.484946 139705362556800 tfrecord_lib.py:168] On image 5100\n","I0422 01:02:57.290261 139705362556800 tfrecord_lib.py:168] On image 5200\n","I0422 01:03:54.188467 139705362556800 tfrecord_lib.py:168] On image 5300\n","I0422 01:04:49.160263 139705362556800 tfrecord_lib.py:168] On image 5400\n","I0422 01:05:46.782065 139705362556800 tfrecord_lib.py:168] On image 5500\n","I0422 01:07:00.913060 139705362556800 tfrecord_lib.py:168] On image 5600\n","I0422 01:08:05.558512 139705362556800 tfrecord_lib.py:168] On image 5700\n","I0422 01:09:09.658477 139705362556800 tfrecord_lib.py:168] On image 5800\n","I0422 01:10:10.147291 139705362556800 tfrecord_lib.py:168] On image 5900\n","I0422 01:11:11.286698 139705362556800 tfrecord_lib.py:168] On image 6000\n","I0422 01:12:08.696386 139705362556800 tfrecord_lib.py:168] On image 6100\n","I0422 01:13:02.225769 139705362556800 tfrecord_lib.py:168] On image 6200\n","I0422 01:13:55.910152 139705362556800 tfrecord_lib.py:168] On image 6300\n","I0422 01:14:47.861520 139705362556800 tfrecord_lib.py:181] Finished writing, skipped 8 annotations.\n","I0422 01:14:47.862285 139705362556800 create_coco_tf_record.py:529] Finished writing, skipped 8 annotations.\n"]}],"source":["# run the script to convert your json file to TFRecord file\n","# --num_shards (how many TFRecord sharded files you want)\n","!python3 -m official.vision.data.create_coco_tf_record --logtostderr \\\n","      --image_dir=$training_images_folder \\\n","      --object_annotations_file=$training_annotation_file \\\n","      --output_file_prefix=$training_data_folder \\\n","      --num_shards=100 \\\n","      --include_masks=True \\\n","      --num_processes=0"]},{"cell_type":"markdown","metadata":{"id":"zwazp89SojMA"},"source":["## Create TFRecord for validation data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nWbKeLoVwXbi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650576542070,"user_tz":420,"elapsed":1834225,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"}},"outputId":"63f4fc03-43b1-424e-dfb2-200f9bbdf1e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["I0421 20:53:39.071351 140304098097024 create_coco_tf_record.py:494] writing to output path: /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/val/\n","I0421 20:53:40.622877 140304098097024 create_coco_tf_record.py:366] Building bounding box index.\n","I0421 20:53:40.627101 140304098097024 create_coco_tf_record.py:377] 0 images are missing bboxes.\n","I0421 20:54:41.275259 140304098097024 tfrecord_lib.py:168] On image 0\n","I0421 20:56:53.052898 140304098097024 tfrecord_lib.py:168] On image 100\n","I0421 20:59:01.886727 140304098097024 tfrecord_lib.py:168] On image 200\n","I0421 21:01:12.356394 140304098097024 tfrecord_lib.py:168] On image 300\n","I0421 21:03:03.635432 140304098097024 tfrecord_lib.py:168] On image 400\n","I0421 21:05:04.787051 140304098097024 tfrecord_lib.py:168] On image 500\n","I0421 21:06:52.991898 140304098097024 tfrecord_lib.py:168] On image 600\n","I0421 21:09:02.626780 140304098097024 tfrecord_lib.py:168] On image 700\n","I0421 21:11:39.070799 140304098097024 tfrecord_lib.py:168] On image 800\n","I0421 21:13:58.603258 140304098097024 tfrecord_lib.py:168] On image 900\n","I0421 21:16:23.214870 140304098097024 tfrecord_lib.py:168] On image 1000\n","I0421 21:18:25.072518 140304098097024 tfrecord_lib.py:168] On image 1100\n","I0421 21:20:29.223420 140304098097024 tfrecord_lib.py:168] On image 1200\n","I0421 21:22:34.431273 140304098097024 tfrecord_lib.py:168] On image 1300\n","I0421 21:24:29.066092 140304098097024 tfrecord_lib.py:168] On image 1400\n","I0421 21:26:33.851860 140304098097024 tfrecord_lib.py:168] On image 1500\n","I0421 21:28:25.426244 140304098097024 tfrecord_lib.py:168] On image 1600\n","I0421 21:28:59.923923 140304098097024 tfrecord_lib.py:181] Finished writing, skipped 2 annotations.\n","I0421 21:28:59.924295 140304098097024 create_coco_tf_record.py:529] Finished writing, skipped 2 annotations.\n"]}],"source":["# run the script to convert your json file to TFRecord file\n","# --num_shards (how many TFRecord sharded files you want)\n","!python3 -m official.vision.data.create_coco_tf_record --logtostderr \\\n","      --image_dir=$validation_images_folder \\\n","      --object_annotations_file=$validation_annotation_file \\\n","      --output_file_prefix=$validation_data_folder \\\n","      --num_shards=100 \\\n","      --include_masks=True \\\n","      --num_processes=0"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","name":"coco_to_tfrecord.ipynb","provenance":[],"authorship_tag":"ABX9TyOBsOCsWRUa6CQ6GhCAKlN0"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
--- a/official/projects/waste_identification_ml/pre_processing/config/categories_list_of_dictionaries.py
+++ b/official/projects/waste_identification_ml/pre_processing/config/categories_list_of_dictionaries.py
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Create a list of dictionaries for categories according to the taxonomy.
+
+Example usage-
+    build_material(MATERIAL_LIST,'material-types')
+    build_material(MATERIAL_FORM_LIST,'material-form-types')
+    build_material(MATERIAL_SUBCATEGORY_LIST,'material-subcategory-types')
+    build_material(MATERIAL_FORM_SUBCATEGORY_LIST,'material-form-subcategory-types')
+"""
+#! /usr/bin/env python
+
+from typing import List, Dict, Union
+
+MATERIAL_LIST = [
+    'Inorganic-wastes', 'Textiles', 'Rubber-and-Leather', 'Wood', 'Food',
+    'Plastics', 'Yard-trimming', 'Fiber', 'Glass', 'Metals'
+]
+
+MATERIAL_FORM_LIST = [
+    'Flexibles', 'Bottle', 'Jar', 'Carton', 'Sachets-&-Pouch', 'Blister-pack',
+    'Tray', 'Tube', 'Can', 'Tub', 'Cosmetic', 'Box', 'Clothes', 'Bulb',
+    'Cup-&-glass', 'Book-&-magazine', 'Bag', 'Lid', 'Clamshell', 'Mirror',
+    'Tangler', 'Cutlery', 'Cassette-&-tape', 'Electronic-devices', 'Battery',
+    'Pen-&-pencil', 'Paper-products', 'Foot-wear', 'Scissor', 'Toys', 'Brush',
+    'Pipe', 'Foil', 'Hangers'
+]
+
+MATERIAL_SUBCATEGORY_LIST = [
+    'HDPE_Flexible_Color', 'HDPE_Rigid_Color', 'LDPE_Flexible_Color',
+    'LDPE_Rigid_Color', 'PP_Flexible_Color', 'PP_Rigid_Color', 'PETE', 'PS',
+    'PVC', 'Others-MLP', 'Others-Tetrapak', 'Others-HIPC', 'Aluminium',
+    'Ferrous_Iron', 'Ferrous_Steel', 'Non-ferrous_Lead', 'Non-ferrous_Copper',
+    'Non-ferrous_Zinc'
+]
+
+
+def build_material(category_list: List[str],
+                   supercategory: str) -> List[Dict[str, Union[int, str]]]:
+  """Creates a list of dictionaries for the category classes.
+
+  Args:
+    category_list: list of categories from MATERIAL_LIST, MATERIAL_FORM_LIST,
+      MATERIAL_SUBCATEGORY_LIST
+    supercategory: supercategory can be 'material-types', 'material-form-types',
+      'material-subcategory-types', 'material-form-subcategory-types'
+
+  Returns:
+    List of dictionaries returning categories with their IDs
+  """
+  list_of_dictionaries = []
+  for num, m in enumerate(category_list, start=1):
+    list_of_dictionaries.append({
+        'id': num,
+        'name': m,
+        'supercategory': supercategory
+    })
+  return list_of_dictionaries
--- a/official/projects/waste_identification_ml/pre_processing/config/config.ini
+++ b/official/projects/waste_identification_ml/pre_processing/config/config.ini
+[config]
+config_folder_path = /mydrive/TFVision/pre-processing/config/
+
+[paths]
+annotation_path = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/18012022/annotations_18012022_coco.json
+images_folder_path = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/18012022/images
+new_annotation_path = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/18012022/material_annotations_18012022_coco.json
+
+[merge]
+input_files = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/20122021/material_annotations_20122021_coco.json,/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/27122021/material_annotations_27122021_coco.json,/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/03012022/material_annotations_03012022_coco.json,/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/10012022/material_annotations_10012022_coco.json,/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/18012022/material_annotations_18012022_coco.json
+output_file = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/output.json
+
+[split]
+input_file = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/output.json
+output_folder = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/
+
+[tfrecord]
+tensorflow_model_folder = /mydrive/TFVision/
+training_data_folder = /mydrive/TFVision/tfrecords/train/
+validation_data_folder = /mydrive/TFVision/tfrecords/val/
+training_images_folder = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/Total_images/train/
+training_annotation_file = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_train.json
+validation_images_folder = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/Total_images/validation/
+validation_annotation_file = /mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_val.json
--- a/official/projects/waste_identification_ml/pre_processing/config/visualization.py
+++ b/official/projects/waste_identification_ml/pre_processing/config/visualization.py
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""To visualize of the category distribution in an annotated JSON file."""
+
+#! /usr/bin/env python3
+
+import json
+from absl import app
+from absl import flags
+import numpy as np
+import pandas as pd
+
+# Define the flags
+FLAGS = flags.FLAGS
+
+# path to annotated JSON file whose distribution needs to be plotted
+_PATH = flags.DEFINE_string(
+    'path', None, 'path to the annotated JSON file', required=True)
+
+
+def visualize_annotation_file(path: str) -> None:
+  """Plot a bar graph showing the category distribution.
+
+  Args:
+    path: path to the annotated JSON file.
+  """
+  # get annotation file data into a variable
+  with open(path) as json_file:
+    data = json.load(json_file)
+
+    # count the occurance of each category in the annotation file
+    category_names = [i['name'] for i in data['categories']]
+    category_ids = [i['category_id'] for i in data['annotations']]
+    values, counts = np.unique(category_ids, return_counts=True)
+
+    # create a dataframe with all possible values
+    # with their counts and visualize it.
+    df = pd.DataFrame(counts, index=values, columns=['counts'])
+    df = df.reindex(range(1, len(data['categories']) + 1), fill_value=0)
+    df.index = category_names
+    df.plot.bar(
+        figsize=(20, 5),
+        width=0.5,
+        xlabel='Material types',
+        ylabel='count of material types')
+
+
+def main(_):
+  visualize_annotation_file(_PATH.value)
+
+
+if __name__ == '__main__':
+  app.run(main)
--- a/official/projects/waste_identification_ml/pre_processing/merge_coco_files.ipynb
+++ b/official/projects/waste_identification_ml/pre_processing/merge_coco_files.ipynb
+{"cells":[{"cell_type":"markdown","metadata":{"id":"0cTM_BOrBUSU"},"source":["# Merge multiple COCO annotation JSON files into one file. "]},{"cell_type":"markdown","metadata":{"id":"cwJuts2DBaaU"},"source":["Given multiple COCO annotated JSON files, your goal is to merge them into one COCO annotated JSON file. \n","\n","A merged COCO annotated JSON file is required where all the data is in one place and it becomes easy to split it into a training and validation JSON file according to the percentage ratio. In case you already have a validated COCO annotated JSON file, then this notebook can be used to merge multiple files into one training COCO annotated JSON file. \n","\n","This notebook uses a third party library to accomplish this task. Recursion is used to combine multiple JSON files using a third party library. \n","\n","This notebook is an end to end example. When you run the notebook, it will take all the multiple JSON files and will output one JSON file.  "]},{"cell_type":"markdown","source":["**Note** - In this example, we assume that all our data is saved on Google drive and we will also write our outputs to Google drive. We also assume that the script will be used as a Google Colab notebook. But this can be changed according to the needs of users. They can modify this in case they are working on their local workstation, remote server or any other database. This colab notebook can be changed to a regular jupyter notebook running on a local machine according to the need of the users."],"metadata":{"id":"aDE5tjSUm1qu"}},{"cell_type":"markdown","metadata":{"id":"BM-tYHTlWhDQ"},"source":["## **MUST DO** - Install the package and restart runtime"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zegGuOmQGBOr"},"outputs":[],"source":["# install python object detection insights library to merge multiple COCO annotation files\n","!pip install pyodi\n","\n","# RESTART THE RUNTIME in order to use this library"]},{"cell_type":"markdown","metadata":{"id":"l7eLOdQ5F33b"},"source":["## Run the below command to connect to your google drive"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"h5soS-6URktT"},"outputs":[],"source":["# import other libraries\n","from google.colab import drive\n","import pyodi\n","import subprocess\n","import sys\n","from configparser import ConfigParser"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24573,"status":"ok","timestamp":1650578638985,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"mTXqVbFdlqxi","outputId":"9fb60b39-fa49-4968-be59-457826b15ace"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","Successful\n"]}],"source":["# connect to google drive\n","drive.mount('/content/gdrive')\n","\n","# making an alias for the root path\n","try:\n","  !ln -s /content/gdrive/My\\ Drive/ /mydrive\n","  print('Successful')\n","except Exception as e:\n","  print(e)\n","  print('Not successful')"]},{"cell_type":"markdown","source":["## Clone TensorFlow Model Garden repository"],"metadata":{"id":"5a9JH7ENqbo-"}},{"cell_type":"code","source":["# move to the specified folder where you want to clone\n","%cd $tensorflow_model_folder"],"metadata":{"id":"Jpu0EQmIqcjx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# clone the Model Garden directory for Tensorflow where all the config files and scripts are located for this project. \n","# project folder name is - 'waste_identification_ml'\n","!git clone https://github.com/tensorflow/models.git "],"metadata":{"id":"MeRMoweaqd0B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Go to the model folder\n","%cd models"],"metadata":{"id":"ijwcqGvdqfMX"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2J9bBncoUNu9"},"source":["## **MUST CHANGE** - Import the path and parameters"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2156,"status":"ok","timestamp":1650578643603,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"Asb2bYGoVsR_","outputId":"9950b9d3-6e34-4c6f-cb67-7ee0d8ed95a3"},"outputs":[{"output_type":"stream","name":"stdout","text":["/mydrive/TFVision/pre_processing/config/\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/output.json\n","['/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/20122021/material_annotations_20122021_coco.json', '/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/27122021/material_annotations_27122021_coco.json', '/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/03012022/material_annotations_03012022_coco.json', '/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/10012022/material_annotations_10012022_coco.json', '/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/18012022/material_annotations_18012022_coco.json']\n"]}],"source":["config = ConfigParser()\n","\n","# path to the config file defining parameters\n","# config.ini file is an important file where all the parameter variables are located.\n","# config.ini file resides in the 'config' folder which is in the 'pre_processing' folder\n","config.read('official/projects/waste_identification_ml/pre_processing/config/config.ini')\n","\n","config_folder_path = config['config']['config_folder_path']\n","print(config_folder_path)\n","\n","output_file = config['merge']['output_file']\n","print(output_file)\n","\n","input_files = config['merge']['input_files']\n","list_of_json_files = input_files.split(',')\n","print(list_of_json_files)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vJp0gH0uHI8j"},"outputs":[],"source":["# insert 'config' folder path for importing the file\n","sys.path.insert(0, config_folder_path)\n","\n","# import visualization script\n","from visualization import visualize_annotation_file"]},{"cell_type":"markdown","metadata":{"id":"6ot4VWOcSTWO"},"source":["# Merge the files"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hngtw3K6S7Qx"},"outputs":[],"source":["def merge_two_files(file1, file2, output_file):\n","  \"\"\"Function to merge 2 files\n","\n","  Args:\n","    file1: path of the 1st COCO annotation json file\n","    file2: path of the 2nd COCO annotation json file\n","    output_file: path of the output COCO annotation json file after merge\n","\n","  Returns:\n","    Path of the merged COCO annotation json file\n","  \"\"\"\n","  subprocess.run(['pyodi', 'coco', 'merge', file1, file2, output_file])\n","  return output_file\n","\n","def merge_multiple_files(list_of_files,output_file_path):\n","  \"\"\"Recursive function to merge multiple files\n","\n","  Args:\n","    list_of_files: list of all the COCO annotation json files that need to be merged \n","    output_file_path: path of the output COCO annotation json file after merge\n","\n","  Returns:\n","    Path of the merged COCO annotation json file\n","  \"\"\"\n","  if len(list_of_files) == 2:\n","    return merge_two_files(list_of_files[0], list_of_files[1], output_file_path)\n","\n","  else:\n","    return merge_two_files(list_of_files[0], merge_multiple_files(list_of_files[1:], output_file_path), output_file_path)"]},{"cell_type":"markdown","metadata":{"id":"__Wj0rO2D-HT"},"source":["The output of the below code will be a merged COCO annotation file in the same directory."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":38754,"status":"ok","timestamp":1650578691051,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"ZMKFOjchVMlH","outputId":"c0365e48-f0fa-4fb4-bcf4-ebd9484f11a9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Total number of files to merge : 5\n","Merge Done\n"]}],"source":["# call function to merge multiple files\n","print('Total number of files to merge :', len(list_of_json_files))\n","merge_multiple_files(list_of_json_files, output_file)\n","\n","print('Merge Done')"]},{"cell_type":"markdown","metadata":{"id":"vf4ESHiDEQYF"},"source":["## Visualize the results"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":2803,"status":"ok","timestamp":1650578693840,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"T_Tdg1IpETJN","outputId":"538a3f3c-800b-4add-dd45-9a91f27ac3e6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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kSZIkSeqGJc3BdC5wC7A+8PFR7XcDlw4zlCRJkiRJkrpjsQWmqroBuAF45sTFkSRJkiRJUtf0MweTJEmSJEmStFgWmCRJkiRJkjSQoRaYklyf5LIkFyeZ27Stm+SMJFc3z+s07UlydJJrklyaZPtR95ndnH91ktnDzCxJkiRJkqTxWdIqcpcBtahDQFXV1n2+xi5V9cdR+4cCZ1bVh5Mc2uz/O/ACYIvm8QzgGOAZSdYFDgNmNXkuSDKnqu7o8/UlSZIkSZI0REtaRW6vIb3mPsDOzfaJwDn0Ckz7ACdVVQG/TLJ2ksc2555RVfMBkpwB7AF8dUj5JEmSJEmSNA5jrSI3qAL+N0kBn6uqY4ENquqW5vjvgQ2a7Y2AG0ddO69pW1y7JEmSJEmSJoEl9WACIMmOwKeAJwErA9OAv1TVmn3cf6equinJ3wFnJPnN6INVVU3xaWBJDgIOAth0002XxS0lSZIkSZLUh34m+f408ArgamBV4A3A/+3n5lV1U/N8K/AtYAfgD83QN5rnW5vTbwI2GXX5xk3b4toXfq1jq2pWVc2aMWNGP/EkSZIkSZK0DPS1ilxVXQNMq6oFVXUCvTmQlijJaknWGNkGdgcuB+YAIyvBzQZOa7bnAK9pVpPbEbizGUr3Q2D3JOs0K87t3rRJkiRJkiRpEhhziBxwT5KVgYuT/DdwC/0VpjYAvpVk5HW+UlU/SHI+cHKS1wM3APs3538PeCFwDXAPcCBAVc1P8gHg/Oa8w0cm/JYkSZIkSVL7+ikwHUBv3qU3A2+jN1ztpWNdVFXXAtssov12YLdFtBfwpsXc63jg+D6ySpIkSZIkaYKNWWAatZrcX4H3DzeOJEmSJEmSumaxBaYkJ1fV/kkuA/5mpbeq2nqoySRJkiRJktQJS+rBdEjzvNdEBJEkSZIkSVI3LbbAVFW3JJkGfKGqdpnATJIkSZIkSeqQJc7BVFULkjyUZK2qunOiQkmSJGk4Zh56etsRhur6D+/ZdgRJkqakflaR+zNwWZIzgL+MNFbVwUNLJUmSJEmSpM7op8D0zeYhSZIkSZIk/Y0xC0xVdWKSVYFNq+qqCcgkSZIkSZKkDllhrBOSvAi4GPhBs79tkjlDziVJkiRJkqSOGLPABLwP2AH4E0BVXQxsPrREkiRJkiRJ6pR+CkwPLGIFuYeGEUaSJEmSJEnd088k31ckeSUwLckWwMHAucONJUmSJEmSpK7opwfTW4AnA/cBXwHuBA4ZZihJkiRJkiR1Rz89mPasqncD7x5pSLIfcMrQUkmSJEmSJKkz+unB9K4+2yRJkiRJkjQFLbYHU5IXAC8ENkpy9KhDawIPDjuYJEmSJEmSumFJQ+RuBuYCewMXjGq/G3jbMENJkiRJkiSpOxZbYKqqS4BLknylqh6YwEySJEmSJEnqkH4m+Z6Z5EPAVsD0kcaq2nxoqSRJkiRJktQZ/UzyfQJwDL15l3YBTgK+NMxQkiRJkiRJ6o5+CkyrVtWZQKrqhqp6H7DncGNJkiRJkiSpK/oZIndfkhWAq5O8GbgJWH24sSRJkiRJktQV/fRgOgR4DHAw8DTgAGD2MENJkiRJkiSpO8YsMFXV+VX156qaV1UHVtVLquqX/b5AkmlJLkry3WZ/syS/SnJNkq8nWblpX6XZv6Y5PnPUPd7VtF+V5PlL8XVKkiRJkiRpSMYsMCWZleRbSS5McunIYxyvcQjw61H7HwGOqqq/B+4AXt+0vx64o2k/qjmPJFsBLweeDOwBfCbJtHG8viRJkiRJkoaonyFyX6a3ktxLgReNeowpycb0JgT/fLMfYFfg1OaUE4EXN9v7NPs0x3drzt8H+FpV3VdV1wHXADv08/qSJEmSJEkavn4m+b6tquYs5f0/AbwTWKPZXw/4U1U92OzPAzZqtjcCbgSoqgeT3NmcvxEwekje6GskSZIkSZLUsn4KTIcl+TxwJnDfSGNVfXNJFyXZC7i1qi5IsvMgIfuR5CDgIIBNN9102C8nSZIkSZKkRj8FpgOBJwIrAQ81bQUsscAEPBvYO8kLgenAmsAngbWTrNj0YtoYuKk5/yZgE2BekhWBtYDbR7WPGH3Nw6rqWOBYgFmzZlUfX5ckSZIkSZKWgX4KTE+vqi3He+OqehfwLoCmB9O/VdWrkpwC7At8DZgNnNZcMqfZ/0Vz/KyqqiRzgK8kORLYENgCOG+8eSRJkiRJkjQc/UzyfW6zktuy8u/A25NcQ2+OpeOa9uOA9Zr2twOHAlTVFcDJwJXAD4A3VdWCZZhHkiRJkiRJA+inB9OOwMVJrqM3B1OAqqqt+32RqjoHOKfZvpZFrAJXVfcC+y3m+g8CH+z39SRJkiRJkjRx+ikw7TH0FJIkSZIkSeqsMQtMVXXDRASRJEmSJElSN/UzB5MkSZIkSZK0WIstMCVZZSKDSJIkSZIkqZuW1IPpFwBJvjhBWSRJkiRJktRBS5qDaeUkrwSeleQlCx+sqm8OL5YkSZIkSZK6YkkFpn8BXgWsDbxooWMFWGCSJEmSJEnS4gtMVfUz4GdJ5lbVcROYSZIkSZIkSR2ypB5MI76Y5GDgH5r9HwOfraoHhhdLkiRJkiRJXdFPgekzwErNM8ABwDHAG4YVSpIkSZIkSd3RT4Hp6VW1zaj9s5JcMqxAkiRJkiRJ6pYV+jhnQZLHj+wk2RxYMLxIkiRJkiRJ6pJ+ejC9Azg7ybVAgMcBBw41lSRJkiRJkjpjzAJTVZ2ZZAtgy6bpqqq6b7ixJEmSJEmS1BX99GCiKShdOuQskiRJkiRJ6qB+5mCSJEmSJEmSFssCkyRJkiRJkgYyZoEpyZn9tEmSJEmSJGlqWuwcTEmmA48B1k+yDr0V5ADWBDaagGySJEmSJEnqgCVN8v1G4K3AhsAFPFJgugv49HBjSZIkSZIkqSsWW2Cqqk8Cn0zylqr61ARmkiRJkiRJUocsqQcTAFX1qSTPAmaOPr+qThpiLkmSJEmSJHXEmAWmJF8EHg9cDCxomguwwCRJkiRJkqSxC0zALGCrqqrx3LiZJPwnwCrN65xaVYcl2Qz4GrAevbmdDqiq+5OsQq9o9TTgduBlVXV9c693Aa+nV+A6uKp+OJ4skiRJkiRJGp4V+jjncuD/LMW97wN2raptgG2BPZLsCHwEOKqq/h64g17hiOb5jqb9qOY8kmwFvBx4MrAH8Jkk05YijyRJkiRJkoagnwLT+sCVSX6YZM7IY6yLqufPze5KzaOAXYFTm/YTgRc32/s0+zTHd0uSpv1rVXVfVV0HXAPs0EduSZIkSZIkTYB+hsi9b2lv3vQ0ugD4e+D/Ar8F/lRVDzanzAM2arY3Am4EqKoHk9xJbxjdRsAvR9129DWjX+sg4CCATTfddGkjS5IkSZIkaZz6WUXux0t786paAGybZG3gW8ATl/ZefbzWscCxALNmzRrXfFGSJEmSJElaemMOkUtyd5K7mse9SRYkuWs8L1JVfwLOBp4JrJ1kpLC1MXBTs30TsEnzmisCa9Gb7Pvh9kVcI0mSJEmSpJaNWWCqqjWqas2qWhNYFXgp8Jmxrksyo+m5RJJVgecBv6ZXaNq3OW02cFqzPafZpzl+VrNy3Rzg5UlWaVag2wI4r78vT5IkSZIkScPWzyTfD2sm7v428Pw+Tn8scHaSS4HzgTOq6rvAvwNvT3INvTmWjmvOPw5Yr2l/O3Bo85pXACcDVwI/AN7UDL2TJEmSJEnSJDDmHExJXjJqdwVgFnDvWNdV1aXAdotov5ZFrAJXVfcC+y3mXh8EPjjWa0qSJEmSJGni9bOK3ItGbT8IXA/sM5Q0kiRJkiRJ6px+VpE7cCKCSJIkSZIkqZv6WUVu4yTfSnJr8/hGko0nIpwkSZIkSZImv34m+T6B3kpuGzaP7zRtkiRJkiRJUl8FphlVdUJVPdg8vgDMGHIuSZIkSZIkdUQ/Babbk7w6ybTm8Wrg9mEHkyRJkiRJUjf0U2B6HbA/8HvgFmBfwIm/JUmSJEmSBPS3itwNwN4TkEWSJEmSJEkd1M8qcicmWXvU/jpJjh9qKkmSJEmSJHVGP0Pktq6qP43sVNUdwHZDSyRJkiRJkqRO6afAtEKSdUZ2kqxLH0PrJEmSJEmSNDX0Uyj6OPCLJKc0+/sBHxxeJEmSJEmSJHVJP5N8n5RkLrBr0/SSqrpyuLEkSZIkSZLUFX0NdWsKShaVJEmSJEmS9Df6mYNJkiRJkiRJWiwLTJIkSZIkSRqIBSZJkiRJkiQNxAKTJEmSJEmSBmKBSZIkSZIkSQOxwCRJkiRJkqSBWGCSJEmSJEnSQCwwSZIkSZIkaSBDKzAl2STJ2UmuTHJFkkOa9nWTnJHk6uZ5naY9SY5Ock2SS5NsP+pes5vzr04ye1iZJUmSJEmSNH7D7MH0IPD/VtVWwI7Am5JsBRwKnFlVWwBnNvsALwC2aB4HAcdAryAFHAY8A9gBOGykKCVJkiRJkqT2Da3AVFW3VNWFzfbdwK+BjYB9gBOb004EXtxs7wOcVD2/BNZO8ljg+cAZVTW/qu4AzgD2GFZuSZIkSZIkjc+EzMGUZCawHfArYIOquqU59Htgg2Z7I+DGUZfNa9oW1y5JkiRJkqRJYOgFpiSrA98A3lpVd40+VlUF1DJ6nYOSzE0y97bbblsWt5QkSZIkSVIfhlpgSrISveLSl6vqm03zH5qhbzTPtzbtNwGbjLp846Ztce2PUlXHVtWsqpo1Y8aMZfuFSJIkSZIkabGGuYpcgOOAX1fVkaMOzQFGVoKbDZw2qv01zWpyOwJ3NkPpfgjsnmSdZnLv3Zs2SZIkSZIkTQIrDvHezwYOAC5LcnHT9h/Ah4GTk7weuAHYvzn2PeCFwDXAPcCBAFU1P8kHgPOb8w6vqvlDzC1JkiRJkqRxGFqBqap+BmQxh3dbxPkFvGkx9zoeOH7ZpZMkSZIkSdKyMiGryEmSJEmSJGn5ZYFJkiRJkiRJA7HAJEmSJEmSpIFYYJIkSZIkSdJALDBJkiRJkiRpIBaYJEmSJEmSNBALTJIkSZIkSRqIBSZJkiRJkiQNxAKTJEmSJEmSBmKBSZIkSZIkSQOxwCRJkiRJkqSBWGCSJEmSJEnSQCwwSZIkSZIkaSAWmCRJkiRJkjQQC0ySJEmSJEkaiAUmSZIkSZIkDcQCkyRJkiRJkgZigUmSJEmSJEkDscAkSZIkSZKkgVhgkiRJkiRJ0kAsMEmSJEmSJGkgFpgkSZIkSZI0EAtMkiRJkiRJGsjQCkxJjk9ya5LLR7Wtm+SMJFc3z+s07UlydJJrklyaZPtR18xuzr86yexh5ZUkSZIkSdLSGWYPpi8AeyzUdihwZlVtAZzZ7AO8ANiieRwEHAO9ghRwGPAMYAfgsJGilCRJkiRJkiaHoRWYquonwPyFmvcBTmy2TwRePKr9pOr5JbB2kscCzwfOqKr5VXUHcAZ/W7SSJEmSJElSiyZ6DqYNquqWZvv3wAbN9kbAjaPOm9e0La5dkiRJkiRJk0Rrk3xXVQG1rO6X5KAkc5PMve2225bVbSVJkiRJkjSGiS4w/aEZ+kbzfGvTfhOwyajzNm7aFtf+N6rq2KqaVVWzZsyYscyDS5IkSZIkadEmusA0BxhZCW42cNqo9tc0q8ntCNzZDKX7IbB7knWayb13b9okSZIkSZI0Saw4rBsn+SqwM7B+knn0VoP7MHByktcDNwD7N6d/D3ghcA1wD3AgQFXNT/IB4PzmvMOrauGJwyVJkiRJktSioRWYquoVizm02yLOLeBNi7nP8cDxyzCaJEmSJEmSlqHWJvmWJEmSJEnS8sECkyRJkiRJkgZigUmSJEmSJEkDscAkSZIkSZKkgVhgkiRJkiRJ0kAsMEmSJEmSJGkgFpgkSZIkSZI0EAtMkiRJkiRJGogFJkmSJEmSJA3EApMkSZIkSZIGYoFJkiRJkiRJA7HAJEmSJEmSpIFYYJIkSZIkSdJALDBJkiRJkiRpICu2HUBq28xDT287wlBd/+E9244gSZIkSVrO2YNJkiRJkiRJA7HAJEmSJEmSpIFYYJIkSZIkSdJALDBJkiRJkiRpIBaYJEmSJEmSNBALTJIkSZIkSRqIBSZJkiRJkiQNxAKTJEmSJEmSBtKZAlOSPZJcleSaJIe2nUeSJEmSJEk9nSgwJZkG/F/gBcBWwCuSbNVuKkmSJEmSJEFHCkzADsA1VXVtVd0PfA3Yp+VMkiRJkiRJAlZsO0CfNgJuHLU/D3hGS1kkTRIzDz297QhDdf2H92w7giRJkiT1pSsFpjElOQg4qNn9c5Kr2swzZOsDf2w7hJbahL5/+chEvdKU4HvXbX7v7Dbfv+7ye2e3+W+vu/y3123+2+u25fn9e9ziDnSlwHQTsMmo/Y2btodV1bHAsRMZqi1J5lbVrLZzaOn4/nWX7123+f51m+9fd/nedZvvX3f53nWb71+3TdX3rytzMJ0PbJFksyQrAy8H5rScSZIkSZIkSXSkB1NVPZjkzcAPgWnA8VV1RcuxJEmSJEmSREcKTABV9T3ge23nmCSmxFDA5ZjvX3f53nWb71+3+f51l+9dt/n+dZfvXbf5/nXblHz/UlVtZ5AkSZIkSVKHdWUOJkmSJEmSJE1SFpgkSZIkSZI0EAtM0pAleXaS1ZrtVyc5Msnj2s6lsaVnk7ZzSJIkjSXJCkn2bzuHpKnLOZg6oilQ/LWqHkryBOCJwPer6oGWo2kMSS4FtgG2Br4AfB7Yv6qe22Yu9SfJZVX11LZzSFJXJVkH2KSqLm07i8aW5OhFNN8JzK2q0yY6j8YnydyqmtV2Do1fkv2AH1TV3UneA2wPHFFVF7YcTX3yb3YLTJ2R5ALgOcA6wM+B84H7q+pVrQbTmJJcWFXbJ3kvcFNVHTfS1nY2jS3JicCnq+r8trOoP0m+Ayz2h1tV7T2BcbQUklzGkt/DrScwjpZCknOAvemtWHwBcCvw86p6e5u5NLYkx9L7o+iUpumlwHXAesC1VfXWlqKpD0k+DPwR+Drwl5H2qprfWij1JcmlVbV1kp2AI4CPAu+tqme0HE198m/23g99dUOq6p4krwc+U1X/neTitkOpL3cneRdwAPCcJCsAK7WcSf17BvCqJDfQ+0UtQPkH7qT2seb5JcD/Ab7U7L8C+EMriTReezXPb2qev9g8T5lf0JYDa1XVXUneAJxUVYc1PXo1+W0NPLuqFgAkOQb4KbATcFmbwdSXlzXPbxrVVsDmLWTR+CxonvcEjq2q05Mc0WYgjduU/5vdAlN3JMkz6f1y/fqmbVqLedS/lwGvBF5XVb9Psim9TyTUDc9vO4DGp6p+DJDk4wsNE/hOkrktxdI4VNUNAEmeV1XbjTp0aJILgUPbSaZxWDHJY4H9gXe3HUbjsg6wOr1hcQCrAetW1YIk97UXS/2oqs3azqCldlOSzwHPAz6SZBWcM7lrpvzf7P4P2x1vBd4FfKuqrkiyOXB2u5HUj6r6PfANYJWm6Y/At9pLpPFo/tDdBNi12b4Hv3d2xWrN90oAkmxG7w8ldUeSPHvUzrPw319XHA78ELimqs5v/i1e3XIm9ee/gYuTnJDkC8BFwEebuUV+1GoyjSnJY5K8pxnqSJItkuw11nWaFPan933z+VX1J2Bd4B2tJtJ4vZUp/je7czB1TJLHVNU9bedQ/5L8M3AQvU//Hp9kC+CzVbVby9HUhySHAbOALavqCUk2BE6pqmePcalalmQP4FjgWnpDGx8HHFRV/9tqMPUtydOA44G16L2Hd9DrDeqEp9IQNb3Pdmh2z6+qm9vMo/4l+Tq9ec9eU1VPSfIY4Nyq2rbdZBpLkscD86rqviQ70xuuelJTbJI6wQJTRzRd7Y4DVq+qTZNsA7yxqv615WgaQzPudgfgVyNDPVyZrDua92874MJR79+lzsHUDU338ic2u7+pKod3dFCStQCq6s6xztXk0CyQcMjIH0bNSnIfr6rXtRpMfUmyEb2i/MPTaVTVT9pLpH6NrCKX5KJRv7dcUlXbtJ1NS9b8zjkLmAl8DzgNeHJVvbDFWOqDC8w8wjmYuuMT9OaCmQNQVZck+YdWE6lf91XV/UkASLIiS/gGpEnn/qqqJAUPLz+qDkiyEvBGYOR75TlJPjeVlortuqawdBjNe5jkx8DhFpo6YevRn7pX1R1JtlvC+ZokknyE3vyRVwAPNc0FWGDqhvuTrErzu2bTK8YPV7rhoap6MMlLgE9V1aeSXNR2KPXlY2OfMjVYYOqQqrpxpEjRWLC4czWp/DjJfwCrJnke8K/Ad1rOpP6d3Ey4uHYz3PF1wP+0nEn9OYbeio2fafYPaNre0FoijdfxwOX05qWA3nt4Ar0VAjW5rZBknaq6AyDJuvh7Z1e8mN6wcIsS3XQY8ANgkyRfBp4NvLbVROrXA0leAbwGeFHT5srTHTCywIz8Qd8lNzaTm1bzqfwhwK9bzqT+HEpvFYHL6PWm+B7w+VYTqW9V9bGmMHgXsCXw3qo6o+VY6s/TFxoScFaSS1pLo6Xx+Kp66aj990+15X477OPAL5KcQm/+rH2BD7YbSX26lt4ftRaYOqiqzmhW29yR3r+9Q6rqjy3HUn8OBP4F+GBVXdcsTvLFljNpHJq5dj8EbAVMH2mvqs0Xe9FyxjmYOiLJ+sAngX+k98Pif4GDq2p+q8EkaZJqfsHer6p+2+xvDpxaVdu3m0z9SvIL4B1V9bNm/9nAx6rqme0mUz+SbAXs2uyeVVVXtplH/UnyDWAb4ExGFZmq6uDWQmlcmiFWO9EbJvezqnL1YmkCJPkZvV6ER9HrhXYgsEJVvbfVYBPIAlNHJHl2Vf18rDZNHkkuY8mTvTlJdAc0v6R9BPg7esXdAFVVa7YaTGNKshu94VSjV5E7sKqm1HKxXZZkW+BEHllFbj4wu6oubTOXFi/JmlV1VzMk7m/4wdjkl2T2otqr6sSJzqLxS/IZ4O+BrzZNLwN+W1Vvai+V+mHvl+5LckFVPW30gk4jbW1nmygWmDoiyYULf+q+qDZNHkket6TjVXXDRGXR0ktyDfCiqnJIagc1q8ht2exe5Zwi3ZRkTYCquqvtLFqyJN+tqr2SXMejP2QZKc77h5I0REl+Azypmj/ykqwAXFFVT2o3mcZi75fuS3Iuvd6DpwJnATcBH66qLZd44XLEOZgmuSTPBJ4FzEjy9lGH1gSmtZNK/bCAtNz4g8WlbnIVue5zFbnuqaq9mufN2s6i8UlyclXtv7ge2Pa87oxrgE2Bkd9DN2naNPmtWlVnJknzd8T7klwAWGDqjkOAxwAHAx8AdqE3afuUYYFp8lsZWJ3ee7XGqPa76E2YqUkqyc+qaqckd7PoT3EdYjWJNUPjAOYm+TrwbR49F8U328ilcXEVue5zFbmOSnJmVe02VpsmlUOa571aTaGlkuQ79H7fXAP4dZLzmv1nAOe1mU19u6/pcXZ1kjfT6/2yesuZND4zq+p84M/0eqCRZD/gV62mmkAOkeuIJI8b6RHTfONZ3aEC0vAkOWEJh6uqXjdhYbRUklyy0Cpyi2zT5JXk4qradqw2TR5JptP79PZsYGd6H6pAr+f1D6rqiS1F0zg1Q1Mf/jDa+bMmtyTPXdJxl1Gf/JI8nd4q4WvT6/2yFvDfVfXLNnOpf05rYw+mLvlQkn8BFgDnA2sm+WRVfbTlXBpDki9W1QFjtWlyqaqRTx0WOcF+O6k0TguSPH6hVeQWtJxJ4/PXJDsttIrcX1vOpCV7I/BWYEPgAh4pMN0FfLqlTBqHJG8E3g/cyyM9sAtw/qxJzAJS9zU9X2BU7xd1Q5IXAC8ENkpy9KhDawIPtpOqHfZg6oiRT2yTvArYHjgUuMDx8JPfwlXrJCsCl1bVVi3GUp/8JKJ7krwVOJfeJ4D/A1zXHJoJvK6qzmolmMYtyTbASfQ+xQW4A1eR64Qkb6mqT7WdQ+OX5GrgmVX1x7azqH9OzdBdo4Y3LlJV7T2BcbQUmt9XtgUO59FzZt0NnF1Vd7SRqw32YOqOlZoJa18MfLqqHkhidXASS/Iu4D+AVZOMDGcMcD+9P3o1iTnBfqdtDHwCeBJwNb2l7c8GvlFVN7eYS31KsmlV/a6qLgG2cRW5Tvp9kjWq6u4k76H34dgRVXVh28E0pt8C97QdQuP2KoCqWmOsEzXpfGwRbSN/52URxzTJNL+vXJLkK/RqLJtW1VUtx2rFCm0HUN8+B1wPrAb8JMnj6HU31yRVVR9qfsh/tKrWbB5rVNV6VXVo2/k0poUn2B95OMH+JFdV/1ZVzwI2oDdU51x6c8HMTXJli9HUv2+PbCT5RlXdZXGpc/6zKS7tBPwjcBy9SfY1+b0LODfJ55IcPfJoO5TG9K2RjSTfaDOIxm1t4ClV9eNmqONHgROBLwB/12Iujd8ewMXADwCSbJtkTquJJpg9mDqiqo4GRv9wvyHJLm3l0bg8amnYJNOA91TV+1vKoz40P+B/nOQLIxPsq3NWpdfjbK3mcTNwWauJ1K/Rn9g670s3jcx3tidwbFWdnuSINgOpb58DzqL3/fKhlrOof37f7K53Ai8ftb8yMItex4ITgFPaCKWl8j5gB+AcgKq6OMlmbQaaaBaYOiTJnsCTgemjmg9vKY76t1uSlwKvB9aj94PCiRi7454kH2Whf3tVtWt7kbQkSY6l937dTW9Z2HOBI6fS+PflQC1mW91xU5LPAc8DPpJkFew53xUrVdXbxz5Nk4zfN7tr5aq6cdT+z6rqduD2JKu1FUpL5YGqujN51MjGKfXv0QJTRyT5LL1lf3cBPk9viM55rYZSX6rqlUleRu+TwL8Ar1x4VTJNal8Gvg7sBfwLMBu4rdVEGsumwCr05l+6CZgH/KnNQBq3bZq568LfzmPnZLXdsD+9oQIfq6o/JXks8I6WM6k/309yEPAd4L6Rxqqa314k9cHvm921zuidqnrzqN0ZE5xFg7kiySuBaUm2AA6m90HnlOEqch2R5NKq2nrU8+rA96vqOW1n05I131xOpFdgehJwJfD2qnICzQ5IckFVPW3k317Tdn5VPb3tbFq89D46ejK9idqfBTyF3mTfv6iqw9rMJk0lSf6OR/f+/F2LcdSHJNctormqymFX0hAk+TJwTlX9z0LtbwR2rqpXtJNM45XkMcC7gd3pFXd/CHygqu5tNdgEsgdTd/y1eb4nyYbA7cBjW8yj/n0HeHNV/aj5o/ftwPn0/vjV5PdA83xLM0z1ZmDdFvOoD9X79OTyJH8C7mwee9EbF2+BSRqyJHsDHwc2BG6l17PwN/izb9Krqik1X4g0CbwN+HbT82Vkpc2n0euN/eK2Qmn8mg4E724eU5IFpu74bpK16a0qcCG9sZwudd8NO4ysftT80fvxJN9pOZP6d0SStYD/F/gUvUmj39ZuJC1JkoN5pOfSA/S6Jp8LHI+TfEsT5QPAjsCPqmq7ZmGSV7ecSX1oFiPZE5jJqL8VqurItjJJy7OquhV4VpJdeaQIf3pVndViLI3DWCvFVdXeE5WlbQ6R66BmoszpVXVn21k0tiQbAP8FbFRVeyTZCnhmVR3XcjRpuZTkSODnwLlVdUvbeaSpKMncqpqV5BJgu6p6KMklVbVN29m0ZEm+B9zLQqvIufqtJC1aktuAG4Gv0ltg5tGzfPdWp54SLDB1RJKf0Vt57KfAz6vq7pYjqU9Jvk9v5bh3V9U2SVYELqqqp7YcTX1I8gTgGGCDqnpKkq2BvavK5bYlaTGS/Ije0I4PAevTGyb39Kp6Vpu5NLbRcw5KksbW9Px8HvAKYGvgdOCrVXVFq8Fa4HKx3XEAcBXwUuDcJHOTHNVyJi1BU0gCWL+qTqb5FLCqHgQWtBZM4/U/wLto5mKqqkuBl7eaSJImv33ozR/5NuAHwG+BF7WaSP36fpLd2w4hSV1RVQuq6gdVNZve8PBrgHOSvHmMS5c7zsHUEVV1XZJ7gfubxy70ViTT5HUesD3wlyTr0Zs3iyQ70ptwWN3wmKo6rzc/+8MebCuMJHVBVf1l1O6JrQXR0vgl8K0kK9D7cMVl7iVpDM00NnvS68U0Ezga+FabmdpggakjkvwW+CPwFeA44C1V9dCSr1LLRioSbwfmAI9P8nNgBrBva6k0Xn9M8ngeKRDuCzivjyQtQpK76X2/HPkZODIXg0WK7jgSeCZwWTmXhiSNKclJwFOA7wHvr6rLW47UGudg6ogkhwA7AZvQW+b3x8BPquq3rQbTYiWZR++XNOgNR12F3i/Y9wELXI2lG5JsDhxLb0WyO4DrgFdV1Q2tBpMkaQiS/ATY2Q8yJak/SR4CRnruji6wTLkPVywwdUyS1YEDgX8DNq6qaS1H0mIkuYXe5NBZ1HFXY+mWJKsBK1TV3UneWlWfaDuTJE02SaYD/wL8PXApcHwz96A6IskXgM2B79P7UAwAPxiTJI3FAlNHJPk4vR5MqwO/oLea3E+r6tpWg2mxklxYVdu3nUPLXpLfVdWmbeeQpMkmydfpzdvzU+AFwA1VdUi7qTQeSQ5bVLsfjEmSxmKBqSOaeV9+WlV/aDuL+pPkoqraru0cWvaS3FhVm7SdQ5ImmySXVdVTm+0VgfP8sEWSpKnBSb47oqpOHdlO8r6qel+LcdSf3doOoKGxMi9Ji/bAyEZVPbjQCpyaxJJ8oqremuQ7LOLnXFXt3UIsSVKH2IOpgxx6JQ3fqJWQ/uYQsGpVWaCXpIUkWcAjE50GWBW4hyk40WnXJHlaVV2Q5LmLOl5VP57oTJKkbvEPpG7y40BpyKpqjbYzSFLXuPhIdzXFpWnAQVX1qrbzSJK6Z4W2A2ipPK3tANJUlOSgtjNIkjQsVbUAeFySldvOIknqHofIdUSSE4FDqupPzf46wMer6nWtBpOmEIenSpKWd0lOAp4EzOGR4Y5U1ZGthZIkdYJD5Lpj65HiEkBV3ZHEFcqkieXwVEnS8u63zWMFYGS4uJ9IS5LGZIGpO1ZIsk5V3QGQZF18/6SJ9qK2A0iSNGRXVtUpoxuS7NdWGElSdzhEriOSvAb4D+AUer0o9gU+WFVfbDWYtJxK8vYlHXeogCRpebSo4eAOEZck9cMeMB1RVSclmQvs2jS9pKqubDOTtJwbGRawJfB0enNRQK8X03mtJJIkaUiSvAB4IbBRkqNHHVoTeLCdVJKkLrEH0ySXZM2quqsZEvc3qmr+RGeSppIkPwH2rKq7m/01gNOr6h/aTSZJ0rKTZBtgW+Bw4L2jDt0NnD0yTYMkSYtjgWmSS/LdqtoryXU8eoLFAFVVm7cUTZoSklxFb5L9+5r9VYBLq2rLdpNJkrTsJVmpqh5otrevqgvbziRJ6gaHyE1yVbVX87xZ21mkKeok4Lwk32r2Xwyc2F4cSZKGZ6S41Pg84NxLkqS+2IOpQ5JsBDyOUYXBqvpJe4mkqSHJ04Cdmt2fVNVFbeaRJGkiJLmoqrZrO4ckqRssMHVEko8ALwOuBBY0zVVVe7eXSpoakkwDNuDRxd3ftZdIkqThS/Liqvp22zkkSd1ggakjFp4HRtLESPIW4DDgD/SKuyPzn23dajBJkpahJEscCudcTJKksTgHU3dcC6wEWGCSJtYhwJZVdXvbQSRJGqKPN8/TgVnAJfQ+VNkamAs8s6VckqSOsMDUHfcAFyc5k1FFpqo6uL1I0pRwI3Bn2yEkSRqmqtoFIMk3ge2r6rJm/ynA+1qMJknqCAtM3TGneUiaWNcC5yQ5nUcXd49sL5IkSUOz5UhxCaCqLk/ypDYDSZK6wQJTR1SVy6JL7fhd81i5eUiStDy7LMnngS81+68CLm0xjySpI5zkuyOSbAF8CNiK3th4AKpq89ZCSZIkabmSZDrw/wD/0DT9BDimqu5tL5UkqQssMHVEkp/RW8nqKOBFwIHAClX13laDScu5JDOAdwJP5tHF3V1bCyVJ0hAkmQb8aGQ+JkmSxmOFtgOob6tW1Zn0ioI3VNX7gD1bziRNBV8GfgNsBrwfuB44v81AkiQNQ1UtAB5KslbbWSRJ3eMcTN1xX5IVgKuTvBm4CVi95UzSVLBeVR2X5JCq+jHw4yQWmCRJy6s/05uH6QzgLyONrlwsSRqLBabuOAR4DHAw8AFgV2B2q4mkqeGB5vmWJHsCNwPrtphHkqRh+mbzkCRpXJyDSZKWIMlewE+BTYBPAWsC76+qOa0GkyRJkqRJxAJTRyT5DrDwm3UnMBf4nCt7SJIkaVCuXCxJWlpO8t0d19IbE/8/zeMu4G7gCc2+pCFLcmHbGSRJGrITgGOAB4FdgJOAL7WaSJLUCfZg6ogk51fV0xfVluSKqnpyW9mkqSLJRVW1Xds5JEkaliQXVNXTklxWVU8d3dZ2NknS5GYPpu5YPcmmIzvN9sgqcve3E0laviWZluRto5pOby2MJElDlOR7STZjoZWLk/wTrlwsSeqDBabueDvwsyRnJzmH3qTD/5ZkNeDEVpNJy6mqWgC8YtT+e1qMI0nSMJ0A/BD4PrAavZWLnwa8GnhNi7kkSR3hELkOaD5F2hc4DXhi03yVE3tLw5fkKGAl4OvAX0baq8r5mCRJy5UkqwP/CewBfJFHFpipqjqytWCSpE5Yse0AGltVPZTknVV1MnBJ23mkKWbb5vnwUW0F7DrxUSRJGqr76X2YsgqwBvBQu3EkSV1iD6aOSPJh4I/8bS+K+a2FkiRJ0nIhyR7AkcAc4PCquqflSJKkjrHA1BFJrltEc1XV5hMeRppCkmwA/BewYVW9IMlWwDOr6riWo0mStMwk+SnwL1V1RdtZJEndZIFJkpYgyffpTXz67qraJsmKwEUjSzdLkiRJklxFrjOSrJTk4CSnNo83J1mp7VzSFLB+M//ZQwBV9SCwoN1IkiRJkjS5OMl3dxxDbyWrzzT7BzRtb2gtkTQ1/CXJejQr6STZEbiz3UiSJEmSNLk4RK4jklxSVduM1SZp2UqyPfAp4CnA5cAMYN+qurTVYJIkSZI0idiDqTsWJHl8Vf0WIMnmOExHGrqqujDJc4EtgQBXVdUDLceSJEmSpEnFAlN3vAM4O8m19P7IfRxwYLuRpOVfkunAvwI70Rsm99Mkn62qe9tNJkmSJEmTh0PkOiTJKvR6UUCvF8V9beaRpoIkJwN3A19qml4JrF1V+7WXSpIkSZImFwtMHZLkWcBMRvU8q6qTWgskTQFJrqyqrcZqkyRJkqSpzCFyHZHki8DjgYt5ZO6lAiwwScN1YZIdq+qXAEmeAcxtOZMkSZIkTSoWmLpjFrBV2eVMmhBJLqNXxF0JODfJ75pDmwK/aS2YJEmSJE1CFpi643Lg/wC3tB1EmiL2ajuAJEmSJHWFBabuWB+4Msl5wMOTe1fV3u1FkpZfVXXDyHaS7XlkFbmfV9WFrQWTJEmSpEnISb47IslzF9VeVT+e6CzSVJLkvcB+wDebphcDp1TVEa2FkiRJkqRJxgKTJC1BkquAbarq3mZ/VeDiqtqy3WSSJEmSNHk4RG6SS3I3vWE5f3MIqKpac4IjSVPNzcB04N5mfxXgpvbiSJIkSdLkY4FpkquqNdrOIE1FST5Fr7h7J3BFkjOa/ecB57WZTZIkSZImG4fISdIiJJm9pONVdeJEZZEkSZKkyc4CkyRJkiRJkgbiEDlJWoIk17GIedCqavMW4kiSJEnSpGSBSZKWbNao7enAfsC6LWWRJEmSpEnJIXKSNE5JLqiqp7WdQ5IkSZImC3swSdISJNl+1O4K9Ho0+b1TkiRJkkbxjyRJWrKPj9p+ELie3jA5SZIkSVLDIXKSNA5JpgEvr6ovt51FkiRJkiaLFdoOIEmTUZI1k7wryaeTPC89bwauAfZvO58kSZIkTSb2YJKkRUhyGnAH8AtgN+DvgACHVNXFLUaTJEmSpEnHApMkLUKSy6rqqc32NOAWYNOqurfdZJIkSZI0+ThETpIW7YGRjapaAMyzuCRJkiRJi2YPJklahCQLgL+M7AKrAvc021VVa7aVTZIkSZImGwtMkiRJkiRJGohD5CRJkiRJkjQQC0ySJEmSJEkaiAUmSZI0JSWpJF8atb9iktuSfHeM67ZN8sKleL0Nk5w6xjkzk1y+mPZXjvc1JUmSJooFJkmSNFX9BXhKklWb/ecBN/Vx3bbAuApMSVasqpurat/xRXzYTMACkyRJmrQsMEmSpKnse8CezfYrgK+OHEiyQ5JfJLkoyblJtkyyMnA48LIkFyd5WZLVkhyf5Lzm3H2a61+bZE6Ss4AzR/dOarZ/muTC5vGsMXJ+GHhO85pvS/KTJNuOyvqzJNskeV+SLza5r07yz6POeUeS85NcmuT9TdtqSU5PckmSy5O8bOD/opIkaUpase0AkiRJLfoa8N5mWNzWwPHAc5pjvwGeU1UPJvlH4L+q6qVJ3gvMqqo3AyT5L+CsqnpdkrWB85L8qLnH9sDWVTU/ycxRr3sr8LyqujfJFvQKW7OWkPNQ4N+qaq/mNecDrwXemuQJwPSquiTJPzVfx47AasBFSU4HngJsAewABJiT5B+AGcDNVbVnc9+1xv1fUJIkCQtMkiRpCquqS5vCzyvo9WYabS3gxKYAVMBKi7nN7sDeSf6t2Z8ObNpsn1FV8xdxzUrAp5teSAuAJ4wz+inAfyZ5B/A64Aujjp1WVX8F/prkbHpFpZ2anBc156xOr+D0U+DjST4CfLeqfjrOHJIkSYAFJkmSpDnAx4CdgfVGtX8AOLuq/qkpQp2zmOsDvLSqrnpUY/IMevM8LcrbgD8A29CbsuDe8QSuqnuSnAHsA+wPPG304YVPbzJ+qKo+9zfhk+3pzSl1RJIzq+rw8WSRJEkC52CSJEk6Hnh/VV22UPtaPDLp92tHtd8NrDFq/4fAW5IEIMl2fbzmWsAtVfUQcAAwbYzzF35NgM8DRwPnV9Udo9r3STI9yXr0imbnNxlfl2T1JuNGSf4uyYbAPVX1JeCj9Ib0SZIkjZsFJkmSNKVV1byqOnoRh/4b+FCSi3h0r++zga1GJvmm19NpJeDSJFc0+2P5DDA7ySXAE1l8T6cRlwILmsm439bkvgC4CzhhEeeeDfwS+ECzet3/Al8BfpHkMuBUegWrp9KbM+pi4DDgiD6yS5Ik/Y1ULdyLWpIkSZNd0/voHOCJTU8okrwP+HNVfazFaJIkaQqyB5MkSVLHJHkN8Cvg3SPFJUmSpDbZg0mSJEmSJEkDsQeTJEmSJEmSBmKBSZIkSZIkSQOxwCRJkiRJkqSBWGCSJEmSJEnSQCwwSZIkSZIkaSAWmCRJkiRJkjSQ/x+UQe//INwKgAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{"needs_background":"light"}}],"source":["# visualize all the input COCO annotated JSON files\n","for file in list_of_json_files:\n","  visualize_annotation_file(file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":1279,"status":"ok","timestamp":1650578695113,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"UBwMf-W0EqG1","outputId":"50d64ecd-8e9d-45fa-9ada-e28dce35f22a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["# visualize the merged COCO annotated JSON file\n","visualize_annotation_file(output_file)"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"merge_coco_files.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
--- a/official/projects/waste_identification_ml/pre_processing/split_coco_files.ipynb
+++ b/official/projects/waste_identification_ml/pre_processing/split_coco_files.ipynb
+{"cells":[{"cell_type":"markdown","source":["# Split one COCO annotation JSON file into training and validation JSON files."],"metadata":{"id":"m0qQu-luFmB5"}},{"cell_type":"markdown","source":["Given a single COCO annotated JSON file, your goal is to split them into training and validation COCO annotated JSON files.\n","\n"," A single JSON file needs to be split into training and validation files. The output files will be further converted to TFRecord files using another notebook.\n","\n","This notebook uses a third party library to accomplish this task. The library can split the JSON files according to the ratio. We kept the validation file to contain 20% of the data. \n","\n","This notebook is an end to end example. When you run the notebook, it will take one JSON file and will split into a train and a val JSON file."],"metadata":{"id":"9NGkWKGrF3pc"}},{"cell_type":"markdown","source":["**Note** - In this example, we assume that all our data is saved on Google drive and we will also write our outputs to Google drive. We also assume that the script will be used as a Google Colab notebook. But this can be changed according to the needs of users. They can modify this in case they are working on their local workstation, remote server or any other database. This colab notebook can be changed to a regular jupyter notebook running on a local machine according to the need of the users."],"metadata":{"id":"GIjj-vE-n1e3"}},{"cell_type":"markdown","metadata":{"id":"QElyM7FtWv5E"},"source":["## **MUST DO** - Install and restart runtime"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WMy_xu64FJ1j"},"outputs":[],"source":["# install python object detection insights library to merge multiple COCO annotation files\n","!pip install pyodi\n","\n","# RESTART THE RUNTIME in order to use this library"]},{"cell_type":"markdown","metadata":{"id":"tySpWIuVFPj0"},"source":["## Run the below command to connect to your google drive"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RfJAkMY9FSPz"},"outputs":[],"source":["# import other libraries\n","from google.colab import drive\n","import pyodi\n","import sys\n","from configparser import ConfigParser"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26119,"status":"ok","timestamp":1650578578675,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"AOLmsOOZFVdJ","outputId":"849f4df7-f941-4107-98c6-ca52a9a2c76a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","Successful\n"]}],"source":["# connect to google drive\n","drive.mount('/content/gdrive')\n","\n","# making an alias for the root path\n","try:\n","  !ln -s /content/gdrive/My\\ Drive/ /mydrive\n","  print('Successful')\n","except Exception as e:\n","  print(e)\n","  print('Not successful')"]},{"cell_type":"markdown","source":["## Clone TensorFlow Model Garden repository"],"metadata":{"id":"iRbEsWOzqRQ5"}},{"cell_type":"code","source":["# move to the specified folder where you want to clone\n","%cd $tensorflow_model_folder"],"metadata":{"id":"vZih--Q4qRl8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# clone the Model Garden directory for Tensorflow where all the config files and scripts are located for this project. \n","# project folder name is - 'waste_identification_ml'\n","!git clone https://github.com/tensorflow/models.git "],"metadata":{"id":"JdWRPXoxqUP5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Go to the model folder\n","%cd models"],"metadata":{"id":"nnshJriFqUlq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8IrviGYBUVwf"},"source":["## **MUST CHANGE** - Import the path and parameters"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FUVal503UJ3M"},"outputs":[],"source":["config = ConfigParser()\n","\n","# path to the config file defining parameters\n","# config.ini file is an important file where all the parameter variables are located.\n","# config.ini file resides in the 'config' folder which is in the 'pre_processing' folder\n","config.read('official/projects/waste_identification_ml/pre_processing/config/config.ini')\n","\n","# folder where all python scripts and configuration file are located\n","config_folder_path = config['config']['config_folder_path']\n","\n","# path of the input file to split\n","input_file = config['split']['input_file']\n","\n","# path to store the split files ( train and val )\n","output_folder = config['split']['output_folder']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4sA1EIH7FV-X"},"outputs":[],"source":["# insert 'config' folder path for importing the file\n","sys.path.insert(0, config_folder_path)\n","\n","# import visualization script\n","from visualization import visualize_annotation_file"]},{"cell_type":"markdown","metadata":{"id":"2E7P4_2eFaPB"},"source":["## Split coco annotation file into train and val COCO files"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21467,"status":"ok","timestamp":1650578618609,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"9HLYrO4JGKFm","outputId":"15245675-06d0-47f1-c8f9-6b6a6dd124f2"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[32m2022-04-21 22:03:27.150\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpyodi.apps.coco.coco_split\u001b[0m:\u001b[36mrandom_split\u001b[0m:\u001b[36m183\u001b[0m - \u001b[1mGathering images...\u001b[0m\n","\u001b[32m2022-04-21 22:03:27.171\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpyodi.apps.coco.coco_split\u001b[0m:\u001b[36mrandom_split\u001b[0m:\u001b[36m194\u001b[0m - \u001b[1mGathering annotations...\u001b[0m\n","\u001b[32m2022-04-21 22:03:29.601\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mpyodi.apps.coco.coco_split\u001b[0m:\u001b[36mrandom_split\u001b[0m:\u001b[36m218\u001b[0m - \u001b[1mSaving splits to file...\u001b[0m\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_train.json\n","/mydrive/gtech/MRFs/Recykal/Latest_sharing_by_sanket/Google_Recykal/Taxonomy_version_2/_val.json\n"]}],"source":["# split a COCO annotation file into train and val files\n","!pyodi coco random-split $input_file $output_folder --val-percentage 0.2\n","\n","# there will be two files with name '_train.json' and '_val.json' in the output_folder"]},{"cell_type":"markdown","metadata":{"id":"wLnDJLIuMf8o"},"source":["## Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":1799,"status":"ok","timestamp":1650578620402,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"2dNcl3XCMLDX","outputId":"9ac010d1-77d9-4ab6-c685-e255d4038d50"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x360 with 1 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\n"},"metadata":{"needs_background":"light"}}],"source":["# visualization of the input COCO annotated JSON file\n","visualize_annotation_file(input_file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":829},"executionInfo":{"elapsed":2202,"status":"ok","timestamp":1650578622601,"user":{"displayName":"Umair Sabir","userId":"06940594206388957365"},"user_tz":420},"id":"GHZZ3aLbMO35","outputId":"46cd6d72-fb4b-40ce-f5eb-5b43e07dc713"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1440x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["<Figure 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visualization of the training COCO annotated JSON file\n","visualize_annotation_file(output_folder + '_train.json')\n","\n","# visualization of the validation COCO annotated JSON file\n","visualize_annotation_file(output_folder + '_val.json')"]}],"metadata":{"colab":{"collapsed_sections":[],"name":"split_coco_files.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
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+<h1 class="code-line" data-line-start=0 data-line-end=1><a id="Instance_Segmentation_Model_Weights_0"></a>Instance Segmentation Model Weights</h1>
+<p class="has-line-data" data-line-start="2" data-line-end="3">Light weight deep learning models for instance image segmentation.</p>
+<h2 class="code-line" data-line-start=3 data-line-end=4><a id="Overview_3"></a>Overview</h2>
+<p class="has-line-data" data-line-start="5" data-line-end="6">Mask RCNN is a state-of-art deep learning model for instance image segmentation, where the goal is to assign instance level labels ( e.g. person1, person2, cat) to every pixel in an input image. Mask RCNN algorithm is available in the TensorFlow Model Garden which is a repository with a number of different implementations of state-of-the-art models and modeling solutions for TensorFlow users.</p>
+<h2 class="code-line" data-line-start=7 data-line-end=8><a id="Model_Categories_7"></a>Model Categories</h2>
+<ul>
+<li class="has-line-data" data-line-start="9" data-line-end="10">Material model - Detects the high level category (e.g. plastic, paper, etc) of an object according to its material type.</li>
+<li class="has-line-data" data-line-start="10" data-line-end="11">Material Form model - Detects the category of the of an object according to its physical product formation (e.g. cup, plate, pen, etc).</li>
+<li class="has-line-data" data-line-start="11" data-line-end="13">Plastic model - Detects the category of a object according to its plastic types (e.g. HDPE, LDPE, etc)</li>
+</ul>
+<blockquote>
+<p class="has-line-data" data-line-start="13" data-line-end="14">The goal to develop these models is to bring transparency &amp; traceability in the world of waste recycling.</p>
+</blockquote>
+<h2 class="code-line" data-line-start=15 data-line-end=16><a id="Model_paths_in_GCP_buckets_15"></a>Model paths in GCP buckets</h2>
+<table class="table table-striped table-bordered">
+<thead>
+<tr>
+<th>Model categories</th>
+<th>Model backbone</th>
+<th>Model type</th>
+<th>GCP bucket path</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Material Model</td>
+<td>Resnet</td>
+<td>saved model</td>
+<td><a href="gs://official_release/version_1/material_model/saved_model/">click here</a></td>
+</tr>
+<tr>
+<td></td>
+<td></td>
+<td>TFLite</td>
+<td><a href="gs://official_release/version_1/material_model/tflite_model/">click here</a></td>
+</tr>
+<tr>
+<td>Material Form model</td>
+<td>Resnet</td>
+<td>saved model</td>
+<td><a href="gs://official_release/version_1/material_form_model/saved_model/">click here</a></td>
+</tr>
+<tr>
+<td></td>
+<td></td>
+<td>TFLite</td>
+<td><a href="gs://official_release/version_1/material_form_model/tflite_model/">click here</a></td>
+</tr>
+<tr>
+<td>Plastic model</td>
+<td>Resnet</td>
+<td>saved model</td>
+<td><a href="gs://official_release/version_1/plastic_types_model/saved_model/">click here</a></td>
+</tr>
+<tr>
+<td></td>
+<td></td>
+<td>TFLite</td>
+<td><a href="gs://official_release/version_1/plastic_types_model/tflite_model/">click here</a></td>
+</tr>
+</tbody>
+</table>
+<h2 class="code-line" data-line-start=26 data-line-end=27><a id="Installation__Download_26"></a>Installation &amp; Download</h2>
+<p class="has-line-data" data-line-start="28" data-line-end="29">You need to use gsutil command line tool to download the deep learning weights from their respective GCP buckets.</p>
+<ul>
+<li class="has-line-data" data-line-start="29" data-line-end="30">Start by logging into the <a href="https://developers.google.com/workspace/guides/create-project">Google Cloud Console and create a project</a>.</li>
+<li class="has-line-data" data-line-start="30" data-line-end="31">Download and install the Google Cloud SDK from the <a href="https://cloud.google.com/sdk/docs/#mac">official website</a>.</li>
+<li class="has-line-data" data-line-start="31" data-line-end="32">You can verity that the install went successfully by opening up a machine terminal and executing the command</li>
+</ul>
+<pre><code class="has-line-data" data-line-start="33" data-line-end="35" class="language-sh">gsutil -v
+</code></pre>
+<ul>
+<li class="has-line-data" data-line-start="35" data-line-end="36">Once your install is successfull you can download the weight from the GCP buckets using gsutil command. For example -</li>
+</ul>
+<pre><code class="has-line-data" data-line-start="37" data-line-end="39" class="language-sh">gsutil -m cp -r gs://official_release/version_1/plastic_types_model/saved_model/* .
+</code></pre>
+<ul>
+<li class="has-line-data" data-line-start="39" data-line-end="40">The above command will download the weights in your local directory.</li>
+</ul>
+</body></html>
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