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December 27, 2021 - MODEL_posted_by
Prasad Chaskar,
938 views, 3 likes
Crab farming is a major aquaculture activity as there is a huge consumption demand of crabs in India. Commercial crab... moreCrab farming is a major aquaculture activity as there is a huge consumption demand of crabs in India. Commercial crab farming is a growing business in coastal areas of India and is looking profitable. Mud crab is highly popular due to its great demand in the export market. The commercial scale mud crab culture is developing fast along the coastal areas of Andhra Pradesh, Tamil Nadu, Kerala and Karnataka.Dataset Link : https://www.kaggle.com/sidhus/crab-age-predictionMean Squared Error : 4.71Root Mean Squared Error : 2.17 less
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Eating rotten meat can result in many gastrointestinal problems such as vomiting, persistent diarrhoea, cramps, infections,... moreEating rotten meat can result in many gastrointestinal problems such as vomiting, persistent diarrhoea, cramps, infections, and severe dehydration, resulting in a fatality.
The project aims to detect rotten Food using deep learning.
The dataset includes multiple fresh and rotten fruits images.
The "Accuracy_score" metric has been used to measure the model's performance.
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August 22, 2021 - MODEL_posted_by
Dev Agrawal,
665 views, 2 likes
Problem Statement:
In handwritten text recognition (HTR), the device predicts the person's handwritten characters or words... moreProblem Statement:
In handwritten text recognition (HTR), the device predicts the person's handwritten characters or words or lines into a format that the computer recognizes ( e.g., Unicode text). There are many levels of HTR, starting from the recognition of simplified individual characters to the recognition of whole words and sentences of handwriting.
Usage:
Usage of handwriting recognition is numerous: recognizing postal addresses, bank checks, and forms. Eventually, OCR plays an essential role for digital libraries, allowing the entry of image textual information into computers by digitization, image restoration, and recognition methods.
Dataset:
Dataset link: https://www.nist.gov/itl/products-and-services/emnist-dataset
The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19 and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset.
Model Used:
Model summary for the neural network created is-
Conv2D - This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. If use_bias is True, a bias vector is created and added to the outputs.
MaxPooling2D - Downsamples the input along its spatial dimensions (height and width) by taking the maximum value over an input window (of size defined by pool_size) for each channel of the input. Strides shift the window along each dimension.
Flatten - Flatten is the function that converts the pooled feature map to a single column that is passed to the fully connected layer.
Dropout - To prevent the nodes from depending on each other. In addition, it reduces overfitting by dropping nodes in each layer with some probability.
Dense - used in the output layer, with 2 units, and activation function as softmax.
Results:
Some of the predictions on different images are:
Note: Input Format: Capital letter aligned in a line with proper space between words.
Future Vision: Improving the model for cursive letters and use it for multiple lines. less
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Problem StatementThis model was built in order to explore an application of Computer Vision known as Crowd counting. Crowd... moreProblem StatementThis model was built in order to explore an application of Computer Vision known as Crowd counting. Crowd Counting is a task to count people in an image. It is mainly used in real life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.Dataset DescriptionWe used the ShanghaiTech dataset it is a introduce a new large-scale crowd counting dataset named Shanghaitech which contains 1198 annotated images, with a total of 330,165 people with centres of their heads annotated.As far as we know, this dataset is the largest one in terms of the number of annotated people. This dataset consists of two parts: there are 482 images in Part A which are randomly crawled from the Internet, and 716 images in Part B which are taken from the busy streets of metropolitan areas in Shanghai. The crowd density varies significantly between the two subsets, making accurate estimation of the crowd more challenging than most existing datasets. Both Part A and Part B are divided into training and testing: 300 images of Part A are used for training and the remaining 182 images for testing, and 400 images of Part B are for training and 316 for testingHow does the model work?We first create a density map for the objects. Then, the algorithm learns a linear mapping between the extracted features and their object density maps. We can also use random forest regression to learn non-linear mapping. Our model will first predict the density map for a given image. The pixel value will be 0 if no person is present. A certain pre-defined value will be assigned if that pixel corresponds to a person. So, calculating the total pixel values corresponding to a person will give us the count of people in that image. How to use the model?1) Choose the crowd image that you want to predict(Aerial view of the image is preferred).2) Click predict.3) Voila! You can see your output now!.What are the metrics used to evaluate the model?The evaluation metric used in CSRNet is MAE and MSE, i.e., Mean Absolute Error and Mean Square Error. These are given by:Results of the model less
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Problem Statement
This model was built in order to create a system that is capable of identifying American Sign Language... moreProblem Statement
This model was built in order to create a system that is capable of identifying American Sign Language (ASL) hand gestures. Sign language recognition is a problem that has been addressed in research for years. However, we are still far from finding a complete solution available in our society.
Among the works developed to address this problem, the majority of them have been based on basically two approaches: contact-based systems, such as sensor gloves; or vision-based systems, using only cameras. The latter is way cheaper and the boom of deep learning makes it more appealing.ObjectiveWe sought to create a system that is capable of identifying American Sign Language (ASL) hand gestures. Since ASL has both static and dynamic hand gestures, we needed to build a system that can identify both types of gestures.Data Description
American Sign Language Letters - v1 v1
This dataset was exported via roboflow.ai on October 20, 2020, at 4:54 PM GMT
It includes 1728 images. Letters are annotated in Tensorflow TFRecord (raccoon) format.
The following pre-processing was applied to each image:
Auto-orientation of pixel data (with EXIF-orientation stripping)
Resize to 416x416 (Stretch)
The following augmentation was applied to create 3 versions of each source image:
50% probability of horizontal flip.
Randomly crop between 0 and 20 % of the image.
Random rotation of between -5 and +5 degrees.
Random shear of between -5° to +5° horizontally and -5° to +5° vertically.
Random brightness adjustment of between -25 and +25 % and Random Gaussian blur of between 0 and 1.25 pixels.
How to use the model?First, choose the file you want to predict.Press submit.Voila! You can see your output now!What are the evaluation metrics used?Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss FunctionLearning rate affects how quickly our model can converge to local minima (aka arrive at the best accuracy).For a more detailed explanation of the model, you can click here or go to the Model Overview section. less
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