Object Tf Unaware

⚡ 👉🏻👉🏻👉🏻 INFORMATION AVAILABLE CLICK HERE 👈🏻👈🏻👈🏻

Docs »
Examples »
Object Detection From TF2 Checkpoint
Edit on GitHub
Click here to download the full example code
This demo will take you through the steps of running an “out-of-the-box” TensorFlow 2 compatible detection model on a collection of images. More specifically, in this example we will be using the Checkpoint Format to load the model.
First we will download the images that we will use throughout this tutorial. The code snippet shown bellow will download the test images from the TensorFlow Model Garden and save them inside the data/images folder.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)
import pathlib
import tensorflow as tf

tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)

# Enable GPU dynamic memory allocation
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)

def download_images():
base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/test_images/'
filenames = ['image1.jpg', 'image2.jpg']
image_paths = []
for filename in filenames:
image_path = tf.keras.utils.get_file(fname=filename,
origin=base_url + filename,
untar=False)
image_path = pathlib.Path(image_path)
image_paths.append(str(image_path))
return image_paths

IMAGE_PATHS = download_images()

The code snippet shown below is used to download the pre-trained object detection model we shall use to perform inference. The particular detection algorithm we will use is the CenterNet HourGlass104 1024x1024. More models can be found in the TensorFlow 2 Detection Model Zoo. To use a different model you will need the URL name of the specific model. This can be done as follows:
Right click on the Model name of the model you would like to use;
Click on Copy link address to copy the download link of the model;
Paste the link in a text editor of your choice. You should observe a link similar to download.tensorflow.org/models/object_detection/tf2/YYYYYYYY/XXXXXXXXX.tar.gz;
Copy the XXXXXXXXX part of the link and use it to replace the value of the MODEL_NAME variable in the code shown below;
Copy the YYYYYYYY part of the link and use it to replace the value of the MODEL_DATE variable in the code shown below.
For example, the download link for the model used below is: download.tensorflow.org/models/object_detection/tf2/20200711/centernet_hg104_1024x1024_coco17_tpu-32.tar.gz
# Download and extract model
def download_model(model_name, model_date):
base_url = 'http://download.tensorflow.org/models/object_detection/tf2/'
model_file = model_name + '.tar.gz'
model_dir = tf.keras.utils.get_file(fname=model_name,
origin=base_url + model_date + '/' + model_file,
untar=True)
return str(model_dir)

MODEL_DATE = '20200711'
MODEL_NAME = 'centernet_hg104_1024x1024_coco17_tpu-32'
PATH_TO_MODEL_DIR = download_model(MODEL_NAME, MODEL_DATE)

The coode snippet shown below is used to download the labels file (.pbtxt) which contains a list of strings used to add the correct label to each detection (e.g. person). Since the pre-trained model we will use has been trained on the COCO dataset, we will need to download the labels file corresponding to this dataset, named mscoco_label_map.pbtxt. A full list of the labels files included in the TensorFlow Models Garden can be found here.
# Download labels file
def download_labels(filename):
base_url = 'https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/'
label_dir = tf.keras.utils.get_file(fname=filename,
origin=base_url + filename,
untar=False)
label_dir = pathlib.Path(label_dir)
return str(label_dir)

LABEL_FILENAME = 'mscoco_label_map.pbtxt'
PATH_TO_LABELS = download_labels(LABEL_FILENAME)

import time
from object_detection.utils import label_map_util
from object_detection.utils import config_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.builders import model_builder

PATH_TO_CFG = PATH_TO_MODEL_DIR + "/pipeline.config"
PATH_TO_CKPT = PATH_TO_MODEL_DIR + "/checkpoint"

print('Loading model... ', end='')
start_time = time.time()

# Load pipeline config and build a detection model
configs = config_util.get_configs_from_pipeline_file(PATH_TO_CFG)
model_config = configs['model']
detection_model = model_builder.build(model_config=model_config, is_training=False)

# Restore checkpoint
ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
ckpt.restore(os.path.join(PATH_TO_CKPT, 'ckpt-0')).expect_partial()

@tf.function
def detect_fn(image):
"""Detect objects in image."""

image, shapes = detection_model.preprocess(image)
prediction_dict = detection_model.predict(image, shapes)
detections = detection_model.postprocess(prediction_dict, shapes)

return detections

end_time = time.time()
elapsed_time = end_time - start_time
print('Done! Took {} seconds'.format(elapsed_time))

Loading model... Done! Took 0.506481409072876 seconds

Label maps correspond index numbers to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine.
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,
use_display_name=True)

The code shown below loads an image, runs it through the detection model and visualizes the detection results, including the keypoints.
Note that this will take a long time (several minutes) the first time you run this code due to tf.function’s trace-compilation — on subsequent runs (e.g. on new images), things will be faster.
Here are some simple things to try out if you are curious:
Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).
Print out detections[‘detection_boxes’] and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).
Set min_score_thresh to other values (between 0 and 1) to allow more detections in or to filter out more detections.
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore') # Suppress Matplotlib warnings

def load_image_into_numpy_array(path):
"""Load an image from file into a numpy array.

Puts image into numpy array to feed into tensorflow graph.
Note that by convention we put it into a numpy array with shape
(height, width, channels), where channels=3 for RGB.

Args:
path: the file path to the image

Returns:
uint8 numpy array with shape (img_height, img_width, 3)
"""
return np.array(Image.open(path))


for image_path in IMAGE_PATHS:

print('Running inference for {}... '.format(image_path), end='')

image_np = load_image_into_numpy_array(image_path)

# Things to try:
# Flip horizontally
# image_np = np.fliplr(image_np).copy()

# Convert image to grayscale
# image_np = np.tile(
# np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)

input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)

detections = detect_fn(input_tensor)

# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy()
for key, value in detections.items()}
detections['num_detections'] = num_detections

# detection_classes should be ints.
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

label_id_offset = 1
image_np_with_detections = image_np.copy()

viz_utils.visualize_boxes_and_labels_on_image_array(
image_np_with_detections,
detections['detection_boxes'],
detections['detection_classes']+label_id_offset,
detections['detection_scores'],
category_index,
use_normalized_coordinates=True,
max_boxes_to_draw=200,
min_score_thresh=.30,
agnostic_mode=False)

plt.figure()
plt.imshow(image_np_with_detections)
print('Done')
plt.show()

# sphinx_gallery_thumbnail_number = 2

Running inference for C:\Users\sglvladi\.keras\datasets\image1.jpg... Done
Running inference for C:\Users\sglvladi\.keras\datasets\image2.jpg... Done

Total running time of the script: ( 0 minutes 20.819 seconds)
© Copyright 2020, Lyudmil Vladimirov Revision 6770aff6.

Easier object detection on mobile with TensorFlow Lite
Posted by Khanh LeViet, Developer Advocate on behalf of the TensorFlow Lite team
At Google I/O this year, we are excited to announce several product updates that simplify training and deployment of object detection models on mobile devices:
Despite being a very common ML use case, object detection can be one of the most difficult to do. We've worked hard to make it easier for you, and in this blog post we'll show you how to leverage the latest offerings from TensorFlow Lite to build a state-of-the-art mobile object detector using your own domain data.
Training a custom object detection model and deploying it to an Android app has become super easy with TensorFlow Lite. We released a learning pathway that teaches you step-by-step how to do it.
In the video, you can learn the steps to build a custom object detector:
There’s also a codelab with source code on GitHub for you to run through the code yourself. Please try it out and let us know your feedback!
Running machine learning models on mobile devices means we always need to consider the trade-off between model accuracy vs. inference speed and model size. The state-of-the-art mobile-optimized model doesn’t only need to be more accurate, but it also needs to run faster and be smaller. We adapted the neural architecture search technique published in the EfficientDet paper, then optimized the model architecture for running on mobile devices and came up with a novel mobile object detection model family called EfficientDet-Lite.
EfficientDet-Lite has 5 different versions: Lite0 to Lite4. The smaller version runs faster but is not as accurate as the larger version. You can experiment with multiple versions of EfficientNet-Lite and choose the one that is most suitable for your use case.
* Size of the integer quantized models.
** Latency measured on Pixel 4 using 4 threads on CPU.
*** Average Precision is the mAP (mean Average Precision) on the COCO 2017 validation dataset.
We have released the EfficientDet-Lite models trained on the COCO dataset to TensorFlow Hub. You also can train EfficientDet-Lite custom models using your own training data with TensorFlow Lite Model Maker.
TensorFlow Lite Model Maker is a Python library that significantly simplifies the process of training a machine learning model using a custom dataset. It leverages transfer learning to enable training high quality models using just a handful of images.
Model Maker accepts datasets in the PASCAL VOC format and the Cloud AutoML’s CSV format. As you can create your own dataset using open-source GUI tools such as LabelImg or makesense.ai, everyone can create training data for Model Maker without writing a single line of code.
Once you have your training data, you can start training a TensorFlow Lite custom object detectors.
Check out this notebook to learn more.
TensorFlow Lite Task Library is a cross-platform library which simplifies TensorFlow Lite model deployments on mobile. Custom object detection models trained with TensorFlow Lite Model Maker can be deployed to an Android app in just a few lines of Kotlin code:
See our documentation to learn more about the customization options in Task Library, including how to configure the minimum detection threshold or the maximum number of detected objects.
Task Library relies on the model metadata bundled in the TensorFlow Lite model to execute the preprocessing and postprocessing logic required to run inference using the model. They include how to normalize the input image, or how to map the class id to human readable labels. Models trained using Model Maker have these metadata by default, making them compatible with Task Library. But if you train a TensorFlow Lite object detection model using a training pipeline other than Model Maker, you can add the metadata using TensorFlow Lite Metadata Writer API.
For example, if you train a model using TensorFlow Object Detection API, you can add metadata to the TensorFlow Lite model using this Python code:
Here we specify the normalization parameters (input_norm_mean=[0], input_norm_std=[255]) so that the input image will be normalized into the [0..1] range. You need to specify normalization parameters to be the same as in the preprocessing logic used during the model training.
See this notebook for a full tutorial on how to convert models trained with the TensorFlow Object Detection API to TensorFlow Lite and add metadata.
Our goal is to make machine learning easier to use for every developer, with or without machine learning expertise. We are working with the Model Garden team to bring more object detection model architectures to Model Maker. We will also continue to work with researchers in Google to make future state-of-the-art object detection models available via Model Maker, shortening the path from cutting-edge research to production for everyone. Stay tuned for more updates!
Training with Multiple Workers using TensorFlow Quantum
June 11, 2021 — Posted by Cheng Xing and Michael Broughton, Google Training large machine learning models is a core ability for TensorFlow. Over the years, scale has become an important feature in many modern machine learning systems for NLP, image recognition, drug discovery etc. Making use of multiple machines to boost computational power and throughput has led to great advances in the field. Similarly in quan…
Build, deploy, and experiment easily with TensorFlow

Homemade Cameltoe Cum
Best Beautiful Xxx
Best Sex Scene Ever
A Hat In Time Big Tits
Very Nice Tits
An Object Transformation Adventure - Writing.Com
Tensorflow Object Detection with Tensorflow 2
Object Detection From TF2 Checkpoint — TensorFlow 2 Objec…
Easier object detection on mobile with TensorFlow Lite ...
python - Unable to restore custom object of type _tf_keras ...
Inanimate Object TF - YouTube
tf-object-detection · PyPI
ValueError: Unable to save the object ListWrapper(...) (a ...
Object detection TF 2.0 export to tf lite · Issue #8872 ...
Adversarial example using FGSM | TensorFlow Core
Object Tf Unaware