efficientnet | Keras and TensorFlow Keras | Machine Learning library
kandi X-RAY | efficientnet Summary
kandi X-RAY | efficientnet Summary
EfficientNets rely on AutoML and compound scaling to achieve superior performance without compromising resource efficiency. The AutoML Mobile framework has helped develop a mobile-size baseline network, EfficientNet-B0, which is then improved by the compound scaling method to obtain EfficientNet-B1 to B7.
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Top functions reviewed by kandi - BETA
- Convert a tensorflow model into a tensorflow model
- Group weights into a list
- Load weights from weights
- Get a model by name
- EfficientNetB1 model
- Return a function that uses swigmoid
- Extract kwargs from kwargs
- Efficient network
- FficientNetL2 model
- EfficientNetB7
- EfficientNet B6
- EfficientNet B0
- EfficientNetB5
- FficientNetB2
- EfficientNet B3 model
- Inject keras modules
- Inject tensorflow modules
efficientnet Key Features
efficientnet Examples and Code Snippets
Lines of Code : 111
License : No License
CopyEfficientNet-B1
EfficientNet-B2
EfficientNet-B3
EfficientNet-B4
EfficientNet-B5
EfficientNet-B6
EfficientNet-B7
MODEL:
NUM_CLASSES: 21
BOX_PREDICTOR: 'SSDLiteBoxPredictor'
BACKBONE:
NAME: 'efficient_net-b0'
OUT_CHANNELS: (40, 112, 320, pt_mnasnet_100
pt_semnasnet_100
pt_mobilenetv2_100
pt_mobilenetv2_110d
pt_mobilenetv2_120d
pt_mobilenetv2_140
pt_fbnetc_100
pt_spnasnet_100
pt_efficientnet_b0
pt_efficientnet_b1
pt_efficientnet_b2
pt_efficientnet_b3
tf_efficientnet_b0
tf_efficientnet from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_name('efficientnet-b0')
from efficientnet_pytorch import EfficientNet
model = EfficientNet.from_pretrained('efficientnet-b0')
import json
from PIL import Image
import torch
f """
Evaluate on ImageNet. Note that at the moment, training is not implemented (I am working on it).
that being said, evaluation is working.
"""
import argparse
import os
import random
import shutil
import time
import warnings
import PIL
import tor def my_func():
print(datetime.now())
from datetime import datetime
exec(open("./mod.py").read())
my_func()
from datetime import datetime
import mod
mod.my_func()
conv_base = tensorflow.keras.applications.EfficientNetB3(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3)
pooling='max'
)
rlroeff_net = EfficientNetB6()
eff_net_state_dict = torch.load(PATH)
eff_net.load_state_dict(eff_net_state_dict)
net = EARUnet(model, 1)
label_mode= 'categorical' then you can use loss=tf.keras.losses.CategoricalCrossentropy
model = EfficientNetB0(
include_top=False, input_tensor=x, weights="imagenet")
model = Emodel = image_classifier.create(train_data, model_spec=efficienetnet_lite4_spec,validation_data=validation_data,epochs=300)
model = image_classifier.create(train_data, model_spec=model_spec.get('efficientnet_lite4'from torch.utils.data import Dataset, DataLoader
class CustomDataset(Dataset):
def __init__(self, path: Path, ...):
# do some preliminary checks, e.g. your path exists, files are there...
assert path.exists()
Community Discussions
Trending Discussions on efficientnet
QUESTION
For instance, consider I have a (32,32,1) input grayscale image. I want to use EfficientNet or any other pre-trained model to classify the data (or basically use transfer learning of any sort). Since EfficientNet takes a minimum of (75,75,3) sized images, is it possible for to upscale my image using ONLY model weights?
For example, any combination of Conv2D, Dense etc which can work for my use case.
ANSWER
Answered 2022-Feb-28 at 18:13- You can use tf.keras.layers.Resizing which resizes an image input to a target height and width after the input layer inside your DL model. check the doc for more details.
or - If you read the images data from a folder you don't need to add a new layer to your model. You can use tf.keras.utils.image_dataset_from_directory method and specify image_size (it's just an argument which is the Size to resize images to after they are read from disk) as your desired target size
QUESTION
I am trying to load the Efficientnet-b6 weights using PyTorch and Fastai:
ANSWER
Answered 2022-Feb-07 at 21:09Given the restrained context, I suspect that the problem resides in model, probably containing an OrderedDict of the EfficientNet model state dict, while the EARUnet expects the EfficientNet nn.Module.
You should instead, try something like:
QUESTION
I am executing model for several folds. After each fold I want to clear the TPU memory so that I don't get OOM error.
Full trace of the current error.
...ANSWER
Answered 2021-Jul-29 at 22:25I personally wouldn't try to clear TPU memory. If there is an OOM on a Google Colab TPU, either use a smaller batch size, smaller model, or use a Kaggle TPU which has twice the memory as a Colab TPU.
QUESTION
I am working in colab to test a code. While importing models, its giving error No module named 'efficientnet' I am sharing the code and error here.
...ANSWER
Answered 2022-Jan-26 at 06:25It should be,
QUESTION
I am new to the tf.data API and trying to use it to load images from disk in the Dogs vs. Cats Redux: Kernels Edition Kaggle competition. To do this, I first created a pandas DataFrame named train_df with two columns - file_path containing the relative path of images and target containing the target labels 0 (for cat) and 1(for dog). Here's how the first 10 rows of the DataFrame looks like:
Then, I tried loading the images with the following code:
...ANSWER
Answered 2021-Dec-26 at 10:22In the above notebook, change the input reading function read_images as follows:
QUESTION
I'm modifying a pretrained efficient-net model in pytorch. I'm doing the following in order:
Create the default model, load the imagenet weights.
Then, change the number of channels in the first layer, and delete few layers while adding few.
ANSWER
Answered 2021-Jul-27 at 05:44If you are re-defining some of the layers, which you seem to be doing with model._conv_stem and model._fc. Then, yes those will be randomly initialized, thus meaning the loaded weights will no longer be used on those layers. The rest of the model will of course stay untouched and will use the loaded weights.
QUESTION
I'm following this tutorial on implementing EfficientNet on Keras.:
I am using my own dataset and so had to load it manually. For some reason the batch size is being included in the tensor shape and it is throwing an error.
...ANSWER
Answered 2021-Oct-19 at 11:36As commented by @Frightera, tf.keras.utils.image_dataset_from_directory returns image shape of 4Dimension.
Sample code
QUESTION
I'm working on an implementation of EfficientNet in Tensorflow. My model is overfitting and predicting all three classes as just a single class. My training and validation accuracy is in the 99% after a few epochs and my loss is <0.5. I have 32,000 images between the three classes (12, 8, 12).
My hypothesis is that it has to do with the way I input the data and one hot coded the labels. Perhaps it is due to everything being labeled the same accidentally, but I can't figure out where.
...ANSWER
Answered 2021-Oct-07 at 15:56If you use labels='inferred', you should also specify class_names which will be the name of the three folders you're getting images from.
QUESTION
ANSWER
Answered 2021-Sep-28 at 03:30From comments
Code change from
QUESTION
Is my classification correct?
LeNet-5: Image classification,
AlexNet: Image classification,
VGG-16: Image classification,
ResNet: Image classification,
Inception module: Image classification,
MobileNet: Image classification,
EfficientNet: Image classification,
Neural Style Transfer: Image generation,
Sliding Windows Detection algorithm: Object detection,
R-CNN: Object detection,
YOLO: Object detection,
Siamese network: Image recognition,
U-Net: Semantic segmentation
If wrong, please correct me. THANKS!
...ANSWER
Answered 2021-Sep-14 at 15:36Your classification is correct if the purpose is - why they were invented initially. However rather than the task based taxonomy, CNNs are better studied on the basis of what different they are doing. Initially CNNs were designed for image classification, but the same network works for Object detection with slight modifications in last layer. For example, Faster RCNN (designed for Object detection) can use any of the architecture designed for classification such as VGG, ResNet etc (link). Similarly Faster-RCNN can be modified to do segmentation task in Mask-RCNN architecture (link).
Here is a chart showing evolutionary history of deep CNNs showing architectural innovations (source)
Here is another taxonomy showing different categories based on architecture style.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install efficientnet
You can use efficientnet like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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