Karem BenChikha
Published © MIT

FaceMask Detection

Neural Network Algorithm to detect people wearing face masks and take actions accordingly

AdvancedFull instructions provided10 hours1,246
FaceMask Detection

Things used in this project

Hardware components

Arduino Nano R3
Arduino Nano R3
×1
Jumper wires (generic)
Jumper wires (generic)
×1
LED (generic)
LED (generic)
×1

Software apps and online services

Arduino IDE
Arduino IDE
TensorFlow
TensorFlow
OpenCV
OpenCV

Story

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Schematics

fnjai3riuhmt29g_2nxpWHmjk7.jpg

Code

TrainModel.ipynb

Python
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import AveragePooling2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from imutils import paths\n",
    "import matplotlib.pyplot as plot \n",
    "import numpy as np \n",
    "import os "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialization with 20 Epochs and 32 Batches and set images' path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "initLayer = 1e-4\n",
    "epochs = 5\n",
    "batch = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SettingUp Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = \"/home/karem/Documents/Python/FaceMaskDetection/dataset\"\n",
    "categories = [\"withMask\", \"withoutMask\"]\n",
    "data = []\n",
    "labels = []\n",
    "for category in categories:\n",
    "    path = os.path.join(directory, category)\n",
    "    for img in os.listdir(path):\n",
    "    \timg_path = os.path.join(path, img)\n",
    "    \timage = load_img(img_path, target_size=(224, 224))\n",
    "    \timage = img_to_array(image)\n",
    "    \timage = preprocess_input(image)\n",
    "    \tdata.append(image)\n",
    "    \tlabels.append(category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encode Data and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "labels = lb.fit_transform(labels)\n",
    "labels = to_categorical(labels)\n",
    "data = np.array(data,dtype=\"float32\")\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set 80% of Images for training and 20% for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "(trainX,testX,trainY,testY) = train_test_split(data,labels,test_size=0.20,stratify=labels,random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "aug = ImageDataGenerator(\n",
    "\trotation_range=20,\n",
    "\tzoom_range=0.15,\n",
    "\twidth_shift_range=0.2,\n",
    "\theight_shift_range=0.2,\n",
    "\tshear_range=0.15,\n",
    "\thorizontal_flip=True,\n",
    "\tfill_mode=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load MibileNetV2 network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
    }
   ],
   "source": [
    "baseModel = MobileNetV2(weights=\"imagenet\", include_top=False,\n",
    "\tinput_tensor=Input(shape=(224, 224, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Construct the head module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "headModel = baseModel.output\n",
    "headModel = AveragePooling2D(pool_size=(7, 7))(headModel)\n",
    "headModel = Flatten(name=\"flatten\")(headModel)\n",
    "headModel = Dense(128, activation=\"relu\")(headModel)\n",
    "headModel = Dropout(0.5)(headModel)\n",
    "headModel = Dense(2, activation=\"softmax\")(headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pace the head FC model on top of the base model (this will become the actual model we will train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=baseModel.input, outputs=headModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop over all layers in the base model and freeze them so they will not be updated during the first training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in baseModel.layers:\n",
    "\tlayer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compile the neural network Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=initLayer, decay=initLayer / epochs)\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=opt,\n",
    "\tmetrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train the head of the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Epoch 1/5\n95/95 [==============================] - 98s 1s/step - loss: 0.2674 - accuracy: 0.8813 - val_loss: 0.0824 - val_accuracy: 0.9752\nEpoch 2/5\n95/95 [==============================] - 98s 1s/step - loss: 0.1011 - accuracy: 0.9667 - val_loss: 0.0577 - val_accuracy: 0.9831\nEpoch 3/5\n95/95 [==============================] - 96s 1s/step - loss: 0.0831 - accuracy: 0.9726 - val_loss: 0.0477 - val_accuracy: 0.9844\nEpoch 4/5\n95/95 [==============================] - 99s 1s/step - loss: 0.0648 - accuracy: 0.9763 - val_loss: 0.0381 - val_accuracy: 0.9844\nEpoch 5/5\n95/95 [==============================] - 97s 1s/step - loss: 0.0554 - accuracy: 0.9806 - val_loss: 0.0310 - val_accuracy: 0.9870\n"
    }
   ],
   "source": [
    "H = model.fit(aug.flow(trainX, trainY, batch_size=batch),\n",
    "\tsteps_per_epoch=len(trainX) // batch,\n",
    "\tvalidation_data=(testX, testY),\n",
    "\tvalidation_steps=len(testX) // batch,\n",
    "\tepochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Serialize the model to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"mask_detector.model\", save_format=\"h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the training loss and accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "N = epochs\n",
    "plot.style.use(\"ggplot\")\n",
    "plot.figure()\n",
    "plot.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "plot.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "plot.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_acc\")\n",
    "plot.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "plot.title(\"Training Loss and Accuracy\")\n",
    "plot.xlabel(\"Epoch #\")\n",
    "plot.ylabel(\"Loss/Accuracy\")\n",
    "plot.legend(loc=\"lower left\")\n",
    "plot.savefig(\"plot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}

DetecMask.py

Python
# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
from imutils.video import VideoStream
import numpy as np
import imutils
import time
import cv2
import os
import serial
import time 

#Setting up your arduino
arduino = serial.Serial('/dev/ttyUSB0',9600)

lowConfidence = 0.75

#face detectinon function
def detectAndPredictMask(frame, faceNet, maskNet):

	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
		(104.0, 177.0, 123.0))

	# pass the blob through the network and obtain the face detections
	faceNet.setInput(blob)
	detections = faceNet.forward()

	# initialize our list of faces, their corresponding locations and the list of predictions from our face mask network
	faces = []
	locs = []
	preds = []

	# loop over the detections
	for i in range(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with the detection
		confidence = detections[0, 0, i, 2]

		# filter out weak detections by ensuring the confidence is greater than the minimum confidence
		if confidence > lowConfidence:
			# compute the (x, y)-coordinates of the bounding box for the object
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			# ensure the bounding boxes fall within the dimensions of the frame
			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h - 1, endY))

			# extract the face ROI, convert it from BGR to RGB channel ordering, and preprocess it
			face = frame[startY:endY, startX:endX]
			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
			face = cv2.resize(face, (224, 224))
			face = img_to_array(face)
			face = preprocess_input(face)

			# add the face and bounding boxes to their respective lists
			faces.append(face)
			locs.append((startX, startY, endX, endY))

	# only make a predictions if at least one face was detected
	if len(faces) > 0:
		faces = np.array(faces, dtype="float32")
		preds = maskNet.predict(faces, batch_size=32)

	# return a 2-tuple of the face locations and their corresponding
	return (locs, preds)

# load our serialized face detector model from disk
prototxtPath = r"deploy.prototxt"
weightsPath = r"res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)

# load the face mask detector model from disk
maskNet = load_model("mask_detector.model")

# initialize the video stream
vs = VideoStream(src=0).start()

# loop over the frames from the video stream
while True:
	# grab the frame from the threaded video stream and resize it to have a maximum width of 900 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=900)

	# detect faces in the frame and determine if they are wearing a face mask or not
	(locs, preds) = detectAndPredictMask(frame, faceNet, maskNet)

	# loop over the detected face locations and their corresponding locations
	for (box, pred) in zip(locs, preds):
		# unpack the bounding box and predictions
		(startX, startY, endX, endY) = box
		(mask, withoutMask) = pred

		# determine the class label and color we'll use to draw the bounding box and text
		label = "Mask" if mask > withoutMask else "No Mask"
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
		if label =="Mask":
			print("ACCESS GRANTED")
			arduino.write(b'H')

		else: 
			print("ACCESS DENIED")
			arduino.write(b'L')
		# include the probability in the label
		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)

		# display the label and bounding box rectangle on the output frame
		cv2.putText(frame, label, (startX, startY - 10),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

	# show the output frame
	cv2.imshow("FaceMask Detection by KAREM -- q to quit", frame)
	key = cv2.waitKey(1) & 0xFF

	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

mask_detector.model

Python
No preview (download only).

res10_300x300_ssd_iter_140000.caffemodel

Python
No preview (download only).

deploy.prototxt

Python
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 300
  dim: 300
}

layer {
  name: "data_bn"
  type: "BatchNorm"
  bottom: "data"
  top: "data_bn"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "data_scale"
  type: "Scale"
  bottom: "data_bn"
  top: "data_bn"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_h"
  type: "Convolution"
  bottom: "data_bn"
  top: "conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "msra"
      variance_norm: FAN_OUT
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "conv1_bn_h"
  type: "BatchNorm"
  bottom: "conv1_h"
  top: "conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "conv1_scale_h"
  type: "Scale"
  bottom: "conv1_h"
  top: "conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1_h"
  top: "conv1_h"
}
layer {
  name: "conv1_pool"
  type: "Pooling"
  bottom: "conv1_h"
  top: "conv1_pool"
  pooling_param {
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "layer_64_1_conv1_h"
  type: "Convolution"
  bottom: "conv1_pool"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_bn2_h"
  type: "BatchNorm"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_64_1_scale2_h"
  type: "Scale"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_1_relu2"
  type: "ReLU"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv1_h"
}
layer {
  name: "layer_64_1_conv2_h"
  type: "Convolution"
  bottom: "layer_64_1_conv1_h"
  top: "layer_64_1_conv2_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_sum"
  type: "Eltwise"
  bottom: "layer_64_1_conv2_h"
  bottom: "conv1_pool"
  top: "layer_64_1_sum"
}
layer {
  name: "layer_128_1_bn1_h"
  type: "BatchNorm"
  bottom: "layer_64_1_sum"
  top: "layer_128_1_bn1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_128_1_scale1_h"
  type: "Scale"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_bn1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu1"
  type: "ReLU"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_bn1_h"
}
layer {
  name: "layer_128_1_conv1_h"
  type: "Convolution"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_bn2"
  type: "BatchNorm"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_128_1_scale2"
  type: "Scale"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu2"
  type: "ReLU"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv1_h"
}
layer {
  name: "layer_128_1_conv2"
  type: "Convolution"
  bottom: "layer_128_1_conv1_h"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_conv_expand_h"
  type: "Convolution"
  bottom: "layer_128_1_bn1_h"
  top: "layer_128_1_conv_expand_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_sum"
  type: "Eltwise"
  bottom: "layer_128_1_conv2"
  bottom: "layer_128_1_conv_expand_h"
  top: "layer_128_1_sum"
}
layer {
  name: "layer_256_1_bn1"
  type: "BatchNorm"
  bottom: "layer_128_1_sum"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_256_1_scale1"
  type: "Scale"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu1"
  type: "ReLU"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
}
layer {
  name: "layer_256_1_conv1"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_bn2"
  type: "BatchNorm"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_256_1_scale2"
  type: "Scale"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu2"
  type: "ReLU"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
}
layer {
  name: "layer_256_1_conv2"
  type: "Convolution"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_conv_expand"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_sum"
  type: "Eltwise"
  bottom: "layer_256_1_conv2"
  bottom: "layer_256_1_conv_expand"
  top: "layer_256_1_sum"
}
layer {
  name: "layer_512_1_bn1"
  type: "BatchNorm"
  bottom: "layer_256_1_sum"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_512_1_scale1"
  type: "Scale"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu1"
  type: "ReLU"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
}
layer {
  name: "layer_512_1_conv1_h"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1 # 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_bn2_h"
  type: "BatchNorm"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "layer_512_1_scale2_h"
  type: "Scale"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu2"
  type: "ReLU"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv1_h"
}
layer {
  name: "layer_512_1_conv2_h"
  type: "Convolution"
  bottom: "layer_512_1_conv1_h"
  top: "layer_512_1_conv2_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 2 # 1
    kernel_size: 3
    stride: 1
    dilation: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_conv_expand_h"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv_expand_h"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1 # 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_sum"
  type: "Eltwise"
  bottom: "layer_512_1_conv2_h"
  bottom: "layer_512_1_conv_expand_h"
  top: "layer_512_1_sum"
}
layer {
  name: "last_bn_h"
  type: "BatchNorm"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
}
layer {
  name: "last_scale_h"
  type: "Scale"
  bottom: "layer_512_1_sum"
  top: "layer_512_1_sum"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "last_relu"
  type: "ReLU"
  bottom: "layer_512_1_sum"
  top: "fc7"
}

layer {
  name: "conv6_1_h"
  type: "Convolution"
  bottom: "fc7"
  top: "conv6_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_1_relu"
  type: "ReLU"
  bottom: "conv6_1_h"
  top: "conv6_1_h"
}
layer {
  name: "conv6_2_h"
  type: "Convolution"
  bottom: "conv6_1_h"
  top: "conv6_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_2_relu"
  type: "ReLU"
  bottom: "conv6_2_h"
  top: "conv6_2_h"
}
layer {
  name: "conv7_1_h"
  type: "Convolution"
  bottom: "conv6_2_h"
  top: "conv7_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_1_relu"
  type: "ReLU"
  bottom: "conv7_1_h"
  top: "conv7_1_h"
}
layer {
  name: "conv7_2_h"
  type: "Convolution"
  bottom: "conv7_1_h"
  top: "conv7_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv7_2_relu"
  type: "ReLU"
  bottom: "conv7_2_h"
  top: "conv7_2_h"
}
layer {
  name: "conv8_1_h"
  type: "Convolution"
  bottom: "conv7_2_h"
  top: "conv8_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_1_relu"
  type: "ReLU"
  bottom: "conv8_1_h"
  top: "conv8_1_h"
}
layer {
  name: "conv8_2_h"
  type: "Convolution"
  bottom: "conv8_1_h"
  top: "conv8_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv8_2_relu"
  type: "ReLU"
  bottom: "conv8_2_h"
  top: "conv8_2_h"
}
layer {
  name: "conv9_1_h"
  type: "Convolution"
  bottom: "conv8_2_h"
  top: "conv9_1_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_1_relu"
  type: "ReLU"
  bottom: "conv9_1_h"
  top: "conv9_1_h"
}
layer {
  name: "conv9_2_h"
  type: "Convolution"
  bottom: "conv9_1_h"
  top: "conv9_2_h"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv9_2_relu"
  type: "ReLU"
  bottom: "conv9_2_h"
  top: "conv9_2_h"
}
layer {
  name: "conv4_3_norm"
  type: "Normalize"
  bottom: "layer_256_1_bn1"
  top: "conv4_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 20
    }
    channel_shared: false
  }
}
layer {
  name: "conv4_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_loc"
  param {
    lr_mult: 1
    decay_mult: 1
...

This file has been truncated, please download it to see its full contents.

Arduino Code

C/C++
int inByte = 0;  // initialize the variable inByte
const int ledPin = 13;       // pin that the LED is attached to

void setup(){
 
  pinMode(ledPin, OUTPUT);  // initialize the LED pin as an output
  Serial.begin(57600);  // set serial monitor to same speed
}
void loop(){
  if (Serial.available()>0) {  // check if any data received
    inByte = Serial.read(); // yes, so read it from incoming buffer
    if (inByte == 1){
    digitalWrite(ledPin, HIGH);
  }
  else {
    digitalWrite(ledPin,LOW);
    }
  } 
}

Credits

Karem BenChikha

Karem BenChikha

12 projects • 3 followers
I am an Automation Engineer with over 4-years of experience in Embedded Systems & Electronics and 2-years as an IoT and Cloud developer.

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