MobileNetV3 | A Keras implementation of MobileNetV3 | Computer Vision library
kandi X-RAY | MobileNetV3 Summary
kandi X-RAY | MobileNetV3 Summary
A Keras implementation of MobileNetV3 and Lite R-ASPP Semantic Segmentation (Under Development). According to the paper: Searching for MobileNetV3.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Builds the hyperparameters
- Bottleneck bottleneck
- Layer convolutional block
- Return activation
- Squeeze tensor
- Build the backbone
- Extract the backbone
- Train model
- Generate training and validation dataset
- Call resize function
- Resize images to bilinear
- Connects the QR code
MobileNetV3 Key Features
MobileNetV3 Examples and Code Snippets
Lines of Code : 7License : Strong Copyleft (CC BY-SA 4.0)
Copy if tf.train.latest_checkpoint(train_logdir):
tf.logging.info('Ignoring initialization; other checkpoint exists')
return None
tf.logging.info('Initializing model from path: %s', tf_initial_checkpoint)
Community Discussions
Trending Discussions on MobileNetV3
QUESTION
I have a model which using a pre-trained MobileNetV3Large model and concatenating the like U-net architecture. That was not a problem. But I want to add model1 with this model2. In model2 I have just batch normalization and dropout which I want to add on the top of this model2. I tried many things but it's not properly working. Any idea?!
Model 2
...ANSWER
Answered 2021-Sep-09 at 07:45You can stack the models functionally:
QUESTION
I'm trying to do transfer learning with MobileNetV3 in Keras but I'm having some issues.
...ANSWER
Answered 2021-Jun-30 at 07:38From comments
Don't mix
tf.kerasand standalonekeras. They are not compatible. Only use one of them (paraphrased from Frightera)
Working code as shown below
QUESTION
I'm trying to do transfer learning on MobileNetV3-Small using Tensorflow 2.5.0 to predict dog breeds (133 classes) and since it got reasonable accuracy on the ImageNet dataset (1000 classes) I thought it should have no problem adapting to my problem.
I've tried a multitude of training variations and recently had a breakthrough but now my training stagnates at about 60% validation accuracy with minor fluctuations in validation loss (accuracy and loss curves for training and validation below).
I tried using ReduceLROnPlateau in the 3rd graph below, but it didn't help to improve matters. Can anyone suggest how I could improve the training?
ANSWER
Answered 2021-Jun-28 at 06:36Your code looks good, but it seems to have one issue - you might be rescaling the inputs twice. According to the docs for MobilenetV3:
The preprocessing logic has been included in the mobilenet_v3 model implementation. Users are no longer required (...) to normalize the input data.
Now, in your code, there is:
test_datagen = ImageDataGenerator(rescale=1.0/255)
which essentially, makes the first model layers to rescale, already rescaled values.
The same applies for train_datagen.
You could try removing the rescale argument from both train and test loaders, or setting rescale=None.
This could also explain why the model did not learn well with the backbone frozen.
QUESTION
I'm looking into training an object detection network using tensorflow, and i had a look at the TF2 model zoo. I noticed there are noticeably less models there than in the directory /models/research/models/, including the mobiledet with ssdlite developed for the jetson xavier.
to clarify, the readme says that there is a mobildet gpu with ssdlite, and that model and checkpoints trained on COCO are provided, yet i couldn't find them anywhere in the repo
How is one supposed to use those models?
I already have a custom-trained mobilenetv3 for image classification, and i was hoping to see a way to turn the network into an object detection network, in accordance to the mobilenetv3 paper. If this is not straightforward, training one network from scratch could be ok too, i just need to know where to even start from
...ANSWER
Answered 2021-Mar-24 at 14:30If you plan to use the object detection API, you can't use your existing model. You have to choose from a list of models here for v2 and here for v1
The documentation is very well maintained and the steps to train or validate or run inference (test) on custom data is very well explained here by the TensorFlow team. The link is meant for TensorFlow version v2. However, if you wish to use v1, the process is fairly similar and there are numerous blogs/videos explaining how to go about it
QUESTION
I am fine-tuning SSD-MobileNetV3 Large and SSD-MobileDet-CPU on the COCO 2017 dataset but with only book class. I have created a new dataset for this and inspected the dataset and it is good. I have also modified the config file to my needs. When I start the training, it just ignores the 'fine_tune_checkpoint' provided in the config file and starts from scratch. However, if I do the same process but with the checkpoint in the 'model_dir' directory instead, it tries to restore it but since I have different number of classes, it gives an error. How can I make the training process restore the checkpoint properly? I also tried with normal COCO dataset with all 90 classes, and when I start the training, 'fine_tune_checkpoint' is ignored, but if I put the checkpoint in the 'model_dir', it is restored properly.
My config file is as below.
...ANSWER
Answered 2020-Sep-30 at 08:14You have to specify a model_dir that is different from the directory where your are loading the previously trained checkpoint.
At the very beginning of the training, the Tensorflow Object Detection API training script (either the current model_main or the legacy/train) will create a new checkpoint corresponding to your new config in your model_dir and then train over this checkpoint. If your directory already contains the pre-trained checkpoints, it will indeed raise an issue corresponding to the number of classe.
If that doesn't work your could also change in your config file the field :
QUESTION
I'm trying to transfer learn a SSD MobileNet v3 (small) model using the object detection API, but my dataset has only 8 classes, while the provided model is pre-trained on COCO (90 classes). If I leave the number of classes of the model intact, I can train with no problem.
ProblemChanging the pipeline.config num_classes yields an assignment error because the layers shape doesn't match with checkpoint variables:
...ANSWER
Answered 2020-Sep-16 at 12:59Yes it is mainly the idea of the Tensorflow object detection garden to fine-tune the models! You should change :
QUESTION
I am modifying deeplab Network. I added a node to the mobilenet-v3 feature extractor's first layer, which reused the existing variables. As no extra parameters would be needed, I could theoretically load the old checkpoint.
Here comes the situation I couldn't understand:
when I start training in a new empty folder, load checkpoint like this:
...ANSWER
Answered 2020-Jul-15 at 19:33I found the following code in train_utils.py: (Line 203)
QUESTION
I am struggling to get my quantized pytorch mobile model (custom MobilenetV3) running on android. I have followed this tutorial https://pytorch.org/tutorials/advanced/static_quantization_tutorial.html and I managed to quantize my model without any problems. However, when I try to load the model via. module = Module.load(assetFilePath(this, MODEL_NAME));
I get the following Exception:
Unknown builtin op: quantized::linear_unpack_fp16.
Could not find any similar ops to quantized::linear_unpack_fp16. This op may not exist or may not be currently supported in TorchScript.
Why are there even float16 values in the quantized model, I thought quantization would replace all float32 vlaues with qint8/quint8? Any ideas on how this can be fixed?
This is how the pytorch model was quantized and saved:
...ANSWER
Answered 2020-Jun-23 at 23:38I found that this error was caused by a version mismatch between the pytorch version that was used for model quantization and pytorch_android.
The model was quantized with pytorch 1.5.1 torchvision 0.6.1 cudatoolkit 10.2.89 but I used org.pytorch:pytorch_android:1.4.0 for building.
Switching to org.pytorch:pytorch_android:1.5.0 solved it.
QUESTION
Using Keras API, I am trying to write the MobilenetV3 as explained in this article: https://arxiv.org/pdf/1905.02244.pdf with the architecture as described in this picture:
For that, I need to implement the bottloneck_blocks from the previous article https://arxiv.org/pdf/1801.04381.pdf. See image for architecture:
I managed to glue together the Initial and final Conv layers:
...ANSWER
Answered 2020-Apr-19 at 11:04In your bottolneck layers, there are Add().
Now, Add expects two tensors with same shape. But, as you have skipped so many layers when this line is run, tf.keras.layers.Add()([m, x]) - m and x have different dimensions.
So, either design a smaller network with fewer layers or just implement all of the intermediate layers.
QUESTION
ANSWER
Answered 2020-Feb-21 at 10:30Since it is a constant division, you could just multiply by (a close approximation of) the inverse:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install MobileNetV3
You can use MobileNetV3 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.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
