- Getting started
- Introduction to the Wiki
- Overview of topics
- How to contribute
- General best practices
- Key principles of Computer Vision
- Convolution
- Advanced convolution techniques and layers
- Pooling
- Overfitting
- Underfitting
- Overfitting Vs. Underfitting in Machine Learning
- Upsampling and Downsampling techniques in Machine Learning
- Computer Vision tasks
- The complete glossary of the modern Computer Vision tasks
- Classification / Tagging
- Object Detection
- Semantic Segmentation
- Instance Segmentation
- Panoptic Segmentation
- Attribute Prediction
- Computer Vision model architectures
- ResNet
- Faster R-CNN
- Mask R-CNN
- DeepLabv3+
- U-Net
- FBNetV3
- U-Net++
- Efficient Net
- PAN
- PSPNet
- LinkNet
- FPN
- RetinaNet
- Cascade R-CNN
- FBNetV3IS
- FBNetV3OD
- CascadeMask R-CNN
- HybridTask Cascade
- Computer Vision metrics
- Confusion Matrix
- Intersection over Union (IoU)
- Accuracy
- Hamming score
- Precision
- Recall
- Precision-Recall curve and AUC-PR
- F-score
- Average Precision
- mean Average Precision (mAP)
- Loss functions in Machine Learning
- Comprehensive overview of loss functions in Machine Learning
- Cross-Entropy Loss
- Binary Cross-Entropy Loss
- Focal loss
- Bounding Box Regression Loss
- CrossEntropyIoULoss2D
- Average Loss
- Solver / Optimizer
- Comprehensive overview of solvers/optimizers in Deep Learning
- Adam
- SGD
- Adadelta
- Adagrad
- AdaMax
- Adamw
- ASGD
- Rprop
- RMSprop
- Lion
- Weight Decay
- Base Learning Rate
- Momentum (SGD)
- Epsilon Coefficient
- Training Parameters
- Patience
- Min delta
- Seed
- Everything you need to know about batches in Machine Learning
- Iterations
- Epoch
- Scheduler
- Comprehensive overview of learning rate schedulers in Machine Learning
- ExponentialLR
- CyclicLR
- StepLR
- MultiStepLR
- ReduceLROnPlateau
- CosineAnnealingLR
- Computer Vision augmentations
- Comprehensive overview of augmentations in Machine Learning
- Horizontal Flip
- Vertical Flip
- Random Crop
- Random Sized Crop
- Rotate
- Resize
- Blur
- Smallest max size
- Center Crop
- Color Jitter
- Gaussian Noise
- Shift Scale Rotate
- Longest max size
- Equalize
- To gray
- Shear
- Mosaic
- Copy Paste
- Extrapolation methods
- Interpolation methods
- Deployment
- Primitive deployment using web frameworks
- Commonly used web frameworks
- Containerized Deployment
- Orchestrated Deployment
- Challenges of Deployment
- Splits
- Data Splitting in Machine Learning

- Getting started
- Introduction to the Wiki
- Overview of topics
- How to contribute
- General best practices
- Key principles of Computer Vision
- Convolution
- Advanced convolution techniques and layers
- Pooling
- Overfitting
- Underfitting
- Overfitting Vs. Underfitting in Machine Learning
- Upsampling and Downsampling techniques in Machine Learning
- Computer Vision tasks
- The complete glossary of the modern Computer Vision tasks
- Classification / Tagging
- Object Detection
- Semantic Segmentation
- Instance Segmentation
- Panoptic Segmentation
- Attribute Prediction
- Computer Vision model architectures
- ResNet
- Faster R-CNN
- Mask R-CNN
- DeepLabv3+
- U-Net
- FBNetV3
- U-Net++
- Efficient Net
- PAN
- PSPNet
- LinkNet
- FPN
- RetinaNet
- Cascade R-CNN
- FBNetV3IS
- FBNetV3OD
- CascadeMask R-CNN
- HybridTask Cascade
- Computer Vision metrics
- Confusion Matrix
- Intersection over Union (IoU)
- Accuracy
- Hamming score
- Precision
- Recall
- Precision-Recall curve and AUC-PR
- F-score
- Average Precision
- mean Average Precision (mAP)
- Loss functions in Machine Learning
- Comprehensive overview of loss functions in Machine Learning
- Cross-Entropy Loss
- Binary Cross-Entropy Loss
- Focal loss
- Bounding Box Regression Loss
- CrossEntropyIoULoss2D
- Average Loss
- Solver / Optimizer
- Comprehensive overview of solvers/optimizers in Deep Learning
- Adam
- SGD
- Adadelta
- Adagrad
- AdaMax
- Adamw
- ASGD
- Rprop
- RMSprop
- Lion
- Weight Decay
- Base Learning Rate
- Momentum (SGD)
- Epsilon Coefficient
- Training Parameters
- Patience
- Min delta
- Seed
- Everything you need to know about batches in Machine Learning
- Iterations
- Epoch
- Scheduler
- Comprehensive overview of learning rate schedulers in Machine Learning
- ExponentialLR
- CyclicLR
- StepLR
- MultiStepLR
- ReduceLROnPlateau
- CosineAnnealingLR
- Computer Vision augmentations
- Comprehensive overview of augmentations in Machine Learning
- Horizontal Flip
- Vertical Flip
- Random Crop
- Random Sized Crop
- Rotate
- Resize
- Blur
- Smallest max size
- Center Crop
- Color Jitter
- Gaussian Noise
- Shift Scale Rotate
- Longest max size
- Equalize
- To gray
- Shear
- Mosaic
- Copy Paste
- Extrapolation methods
- Interpolation methods
- Deployment
- Primitive deployment using web frameworks
- Commonly used web frameworks
- Containerized Deployment
- Orchestrated Deployment
- Challenges of Deployment
- Splits
- Data Splitting in Machine Learning

FBNetV3 makes up a family of state-of-art compact neural networks that is generated through Network Architecture Recipe Search, NARS. NARS is an advanced version of Network Architecture Search that searches for both the architecture and the training recipes. FBNetV3 has been shown to improve the mAP (mean Average Precision).

Network Architecture Search is the technique of automating the design of Artificial Neural Network.

Many image segmentation or object detection task use feature extraction and use of regional proposals as it was proven to be more cost effective. Therefore, FBNetV3 has similar backbone network at the beginning to extract such features.

It is the size to pool proposals before feeding them to the mask predictor, in model playground the default value is set as 14.

It is the size to pool proposals before feeding them to the mask predictor, in model playground the default value is set as 6.

Before the training process, the weights in the neural network has to be initialized to a certain values. The users will initialize the weights to FBNetV3a-DSMask-C4 COCO.

The IOU threshold is used to decide whether the bounding box contains a background or an object.

Everything above the value of the upper bound will be classified as objects and everything lower than the lower bound will be classified as background. The values in between the lower and the upper bound are ignored.

Normalization techniques help to decrease the overall training time of the model. It makes the contribution of the features uniform by normalizing the weights. This also helps to avoid the weights from exploding and hence makes the optimization faster.

There are three available normalization method in model playground:

- GN
- SyncBN
- naiveSyncBN

In this normalization techniques, where the weights are scaled and shifted by the variance and the mean. Mathematically, it is given as:

$\$\$\backslash hat\{x\}=\backslash frac\{x-E(x)\}\{\backslash sqrt\{Var(x)\}-\backslash epsilon\}\backslash \backslash y=\backslash gamma\; \backslash cdot\; \backslash hat\{x\}+\backslash beta\$\$$

The mean and standard-deviation are calculated per-dimension over all mini-batches of the same process groups. Later again, the scaling and shifting happens with other two constants: **γ** and **β**. These are hyperparameters, and are usually learnable through the network.

In this normalization technique the weights are assigned equally to all the images regardless of their dimension. With this, we reduce the need to accurately compute mean and variance for each of the batches. A little difference has been observed between such simplified calculation and accurate mean and variance calculation.

Group Batch normalization, abbreviated as GN, is another normalization technique that normalizes a group of parameters. If the input dimension is 50, them the GN normalization can group those 50 parameters in a group of 5, and normalize each group with its own mean and variance.

It is the maximum number of the proposals to be considered before the non maximal suppression. The proposals are sorted descending after confidence and only the ones with the highest confidence are chosen.

It is the maximum number of proposals to be considered after the non maximal suppression. The probability of detecting more objects is high if this number is high but the computation cost is also increased since more regional proposals has to be processed.

```
import urllib
import torch
from mobile_cv.model_zoo.models.fbnet_v2 import fbnet
from mobile_cv.model_zoo.models.preprocess import get_preprocess
from PIL import Image
def _get_input():
# Download an example image from the pytorch website
url, filename = (
"https://github.com/pytorch/hub/raw/master/dog.jpg",
"dog.jpg",
)
local_filename, headers = urllib.request.urlretrieve(url, filename)
input_image = Image.open(local_filename)
return input_image
def run_fbnet_v2():
# fbnet models, supported models could be found in
# mobile_cv/model_zoo/models/model_info/fbnet_v2/*.json
model_name = "dmasking_l3"
# load model
model = fbnet(model_name, pretrained=True)
model.eval()
preprocess = get_preprocess(model.arch_def.get("input_size", 224))
# load and process input
input_image = _get_input()
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)
# run model
with torch.no_grad():
output = model(input_batch)
output_softmax = torch.nn.functional.softmax(output[0], dim=0)
print(output_softmax.max(0))
if __name__ == "__main__":
run_fbnet_v2()
```

Last modified 22d ago

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