TensorFlow Object Detection

Train and Deploy Object detection models using TensorFlow Object Detection API. This Repository uses Object detection API to train and export models to required formats along with documentation. This repository also contains some scripts for collecting and preprocessing dataset to required format.

Requirements

System

There is no specific requirements for inferencing from pretrained models but require GPU for training or finetuning a detection model.

Python 3

Python 3.6 or higher. Tested with Python 3.7, 3.9 in Windows 10 and Linux.

Packages

• tensorflow-gpu>=2.0 or tensorflow>=2.0
• numpy
• absl-py
• opencv-python

This implementation is tested with tensorflow cpu and gpu 2.0, 2.2, 2.7 in Windows 10 and Linux

Installation

Install Python

There are two ways to install python in windows using Python 3 installer or Anaconda. Installing python with anaconda or miniconda is recommended. In linux Python 3 is installed by default but we can also install miniconda or conda into linux.

Creating Virtual Environment

Create a new python virtual environment using conda or venv and activate it. If Anaconda or Miniconda is installed use conda else use venv to create virtual environments.

• Using conda

conda create --name tfod
conda activate tfod
conda install pip

• Using venv in linux

python3 -m venv path/to/create/env/tfod
source path/to/create/env/tfod/bin/activate

• Using venv in windows

python -m venv path\to\create\env\tfod
path\to\create\env\tfod\Scripts\activate

Installing dependencies

Installing required python packages

The command below will install the required dependencies from requirements.txt file.

pip install -r requirements.txt

Installing tensorflow object detection API

• Clone the tensorflow model repository fro github into desired location. Tested with commit has 439d515a059740ba926ec2442299300fe52aa101

git clone https://github.com/tensorflow/models

Linux

• Run the commands below into terminal to install

cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .

Windows

• Download latest protoc release from here
• Add <PATH_TO_Protobuf>\bin to your Path environment variable.
• Open cmd and run

cd models/research/

# in windows cmd
for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
# in windows powershell
Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}

Download Pretrained Object Detection Models

Object Detection model trained using TensorFlow object detection API can be downloaded from TensorFlow Object Detection Models Zoo with coco weights which we can finetune or train from scratch.

Additional guides:

If get stuck in installation part follow these additional resources

• Python Installation
• pip and usage
• Anaconda installation and using conda

Documentation

Detect Objects in Image

python detect_image.py \
    --model PATH/TO/MODEL \
    --image PATH/TO/IMAGE \
    --output PATH/TO/SAVE/OUTPUT/IMAGE

optional argument threshold which neglects all detections with probability score less that that. Defaults to 0.5

Detect Objects in Video Feed

python detect_video.py \
    --model PATH/TO/MODEL \
    --video PATH/TO/VIDEO \
    --output PATH/TO/SAVE/OUTPUT/VIDEO

optional argument threshold which neglects all detections with probability score less that that. Defaults to 0.5

Detect Objects in Camera Feed

python detect_webcam.py \
    --model PATH/TO/MODEL \
    --cam 0 \
    --output PATH/TO/SAVE/OUTPUT/VIDEO

optional argument threshold which neglects all detections with probability score less that that. Defaults to 0.5

Training an object detection model using TfRecords

Training script is taken from model/research/object-detection

python train_model_tfrecords.py \
    --model_dir "PATH/TO/SAVE/CHECKPOINT/AND/EVENTS" \
    --pipeline_config_path "PATH/TO/PIPELINE/CONFIG" \
    --num_train_steps 100 \
    --checkpoint_dir "PATH/TO/CHECKPOINT/DIR" \
    --eval_training_data True \
    --sample_1_of_n_eval_examples 1 \
    --sample_1_of_n_eval_on_train_examples 5 \
    --max_eval_retries 0

• sample_1_of_n_eval_on_train_examples Will sample one of every n train input examples for evaluation, This is only used if eval_training_data is True. Default is 5
• sample_1_of_n_eval_examples Will sample one of every n eval input examples. Default is 1
• max_eval_retries If running continuous eval, the maximum number of retries upon encountering tf.errors.InvalidArgumentError. If negative, will always retry the evaluation.

Training an object detection model using Images and csv annotations

python train_model_images.py \
    --train_annotations_csv "PATH/TO/TRAIN/CSV" \
    --test_annotations_csv "PATH/TO/TEST/CSV"
    --train_images_dir "PATH/TO/TRAIN/IMAGES" \
    --test_images_dir "PATH/TO/TEST/IMAGES" \
    --labelstxt_path "PATH/TO/LABELS/TXT/FILE"  \
    --pipeline_config_path "PATH/TO/PIPELINE/CONFIG" \
    --num_train_steps 100 \
    --checkpoint_path "PATH/TO/CHECKPOINT/.ckpt" \
    --batch_size 32 \
    --early_stopping_patience 5 \
    --learning_rate 0.01 \
    --model_name "MODEL_NAME" \
    --finetune True \
    --image_width 320 \
    --image_height 320

Exporting Trained model for inference

script Taken from model/research/object-detection

python export_model.py \
    --input_type image_tensor \
    --pipeline_config_path "path/to/ssd_model_pipeline.config" \
    --trained_checkpoint_prefix "path/to/model.ckpt" \
    --output_directory "path/to/exported_model_directory"

Exporting Trained model as tflite

script taken from model/research/object-detection

python export_tflite.py \
    --pipeline_config_path "path/to/ssd_model/pipeline.config" \
    --trained_checkpoint_dir "path/to/ssd_model/checkpoint" \
    --output_directory "path/to/exported_model_directory"

Downloading images from Duckduckgo (collecting data)

python download_images.py \
    --config PATH/TO/DOWNLOADER/JSON

• config Path to config file of downloader. Defaults to image_downloader/config.json

Detection Results

Contributions

Contributions are highly welcome that will improve quality of project.