SmiffyKMc

import tensorflow as tf
from tensorflow import keras
from keras import layers

conv_base = keras.applications.mobilenet_v2.MobileNetV2(
    weights="imagenet",
    include_top=False
)
conv_base.trainable = False

SmiffyKMc

conv_base = keras.applications.vgg16.VGG16(
    weights="imagenet",
    include_top=False
)
conv_base.trainable = False

inputs = keras.Input(shape=(256, 256, 3))
x = data_augmentation(inputs)
x = keras.applications.vgg16.preprocess_input(x)
x = conv_base(x)

SmiffyKMc

name: tflite_image_classification
description: A new Flutter project.

publish_to: 'none' # Remove this line if you wish to publish to pub.dev

# https://developer.apple.com/library/archive/documentation/General/Reference/InfoPlistKeyReference/Articles/CoreFoundationKeys.html
version: 1.0.0+1

environment:
  sdk: ">=2.16.1 <3.0.0"

SmiffyKMc

import 'package:flutter/material.dart';
import 'package:tflite_image_classification/tflite_model.dart';

void main() {
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {
  const MyApp({Key? key}) : super(key: key);

SmiffyKMc

import 'dart:io';

import 'package:flutter/material.dart';
import 'package:image_picker/image_picker.dart';
import 'package:tflite/tflite.dart';

class TfliteModel extends StatefulWidget {
  const TfliteModel({Key? key}) : super(key: key);

  @override

SmiffyKMc

from tensorflow import keras
import pathlib

test_model = keras.models.load_model(f"{hotDogDir}hotdog_classifier_v4.keras")

converter = tensorflow.lite.TFLiteConverter.from_keras_model(test_model)
tflite_model = converter.convert()

tflite_models_dir = pathlib.Path(f"{hotDogDir}")
tflite_models_dir.mkdir(exist_ok=True, parents=True)

SmiffyKMc

from PIL import Image
import tensorflow as tf
from tensorflow import keras
from keras import layers
import os
import matplotlib.pyplot as plt
import numpy as np

def download_and_predict(url, filename):
    test_model = keras.models.load_model(f"{hotDogDir}hotdog_classifier_v4.keras")

SmiffyKMc

conv_base = keras.applications.vgg16.VGG16(
    weights="imagenet",
    include_top=False
)
conv_base.trainable = False

inputs = keras.Input(shape=(256, 256, 3))
x = data_augmentation(inputs)
x = keras.applications.vgg16.preprocess_input(x)
x = conv_base(x)

SmiffyKMc

inputs = keras.Input(shape=(8, 8, 512))
x = layers.Flatten()(inputs)
x = layers.Dense(256)(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(1, activation= keras.activations.sigmoid)(x)
model = keras.Model(inputs, outputs)

model.compile(optimizer=keras.optimizers.RMSprop(),
              loss=keras.losses.BinaryCrossentropy(),
              metrics=["accuracy"])

SmiffyKMc

import numpy as np

def get_features_and_labels(dataset):
  all_features = []
  all_labels = []
  for images, labels in dataset:
    preprocessed_images = keras.applications.vgg16.preprocess_input(images)
    features = conv_base.predict(preprocessed_images)
    all_features.append(features)
    all_labels.append(labels)