Alexnet jobs
...Responsibilities: - Train 5 classification models using 70% of the data for training and 30% for testing. - Implement the ADAMW optimizer with a learning rate of 0.0001. - Utilize a batch size of either 16 or 32. Compare precision, accuracy, and F1 scores. - Make results comparison of the following models with both datasets: 1. VGG19 2. Resnet101 3. Resnet50 4. InceptionV3 5. Alexnet You are required to generate graphs of trainings Also generate confusion metrics of testing and classification reports. Data Pre-processing: - Normalize the image data. - Apply data augmentation techniques. - Resize the images as necessary. Ideal skills and experience: - Proficiency in Python and experience with image data processing. - Familiarity with machine le...
...machine learning expert to create a cyclone intensity prediction model using sattelite imagery. The successful freelancer will be tasked with: - Implementing AlexNet and VGG16 for this purpose, and, - Creating a new model that takes inspiration from AlexNet and performs more efficiently. The final goal of the project will be refined during initial discussions, yet it will align closely with the themes of cyclone prediction accuracy, model performance comparison, and novel model development based on AlexNet. Ideal Skills and Experience: - Mastery of Python or Java or C++ - Extensive machine learning experience, particularly with AlexNet and VGG16 - Knowledge in utilization of satellite images for climate or weather prediction tasks, where cyclone intensi...
I need help with Matlab please. I have some .png images which I want to train using Matlab. I have been able to convert the size of the grayscale images to format 227 227 but inorder to make them compatable with some pre-trained models available such as Alexnet, Squeezenet etc [227 227 3]. I am not sure how to go about it and would like some help reading in the files from a directory, then writing the ew format to a specified directory. I was told that you have to put three layers of the same image in one matrix. Can someone help me to do this please? convert 227x227 image into 227x227x3 srcFile=dir('E:Fetal_ImagesXOther2*.png'); for i=1:length(srcFile) filename=strcat('E:Fetal_ImagesXOther2', srcFile(i).name); p=imread(filename); ...
I need an Arrhythmia detection system using ecg, I am using the physionet dataset, Mit-bih arrhythmia database (mitdb) for arrhythmia detection and I want to train and test the following python based models for arrhythmia detection, Alexnet, resnet 18, resnet 50, vgg-16, Mobilenet and efficientnet (v1 and v2).
I am building my final year project a...been done in this field) by using multiple Deep CNN architectures, which can recognize handwritten digits from many languages (without taking language as an input) So, I need an expert who can guide/assist me to choose what novelty should I address and then build an ensemble model (combining 2 or more models) for the task mentioned above. Many Deep CNN methods have been applied earlier like AlexNet, ResNet, Inception, etc and fyk, hybrid models have also been built (like CNN-SVM and MobileNet-ResNet). So if you want to work, please work on some new models or a combination of models. You can take as much time you want to build. Don't rush. I just want a good project satisfying all the conditions that I have mentioned. Attaching a ppt t...
...(so-called re-scaling) can distort it and significantly reduce the probability of identifying a small object that occupies only a few (dozens) of pixels. Example: - input image size M x N pixels; - each pixel is coded with one number - intensity within [0, 1]. For example: 256*192. 3. Development of a neural network for object identification and verification. It should be a CNN architecture like AlexNet, VGG, or similar. For training, it is better to take an already trained neural network, add layers for resizing the image (at the beginning), and modeling the desired output (at the end). 4. Preparation of a training sample for training. Here are a few steps. It is the most complex task: (a) Acquisition of thermal images through the camera in their final form. (b) Developmen...
I have a code written in tensorflow with a few encoders. Please add a few lines to make it work for vgg16 and alexnet too
Hello I'm working on a mathlab project and need some help. I need to modify the attached code to match the research paper in terms of image processing and convolutional neural network. (Alexnet) This attached code is generic using only 3 layers we would like to change it so it matches the attached paper. Which uses AlexNet instead. + There are some filter differences. Another thing I would like to have. Is to have the model trained. So that i can use it directly. I have attached below the image dataset i have been using but u can use any dataset you like. Finally i would like a short report on the results. Like: 1) Brief explanation of the Code 2) Training, Testing Results and curves (Accuracy....) Link to dataset.
...extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor’s CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accu...
1. Data augmentation using traditional techniques and GAN 2. Run pre-trained models- VGG16, Alexnet, Renet-50 plus two other models 3. select the best performance and optimize it. 4. Paper writing
Hii, To make model robust by using adversearial training, Model: Alexnet, Lenet Dataset: cifar10 Framework: pytorch
Given a dataset that contains images of retina you have to extract the features from images using pretrain architecture (Google Net, Alexnet, ResNet) . And integrate the features extracted by all three architecture. Divide the features set as train and test. And on train dataset used sparse autoencoder for features reduction to subset. And classify it in stages
Looking for Expert In Artificial intelligence and Machine learning to solve some technical problems in My capstone project. Project details in short - Epilepsy analysis seizure detection Using Standards EEG database such as Temple university EEG Database or Bonn university or MIT University Database Having some financial budget if anyone wants to freelance. #artificialinlligence #machinelearning #freelancer
I want to run a pre-trained Caffe model for material recognition, it can be downloaded here: (GoogLeNet, VGG-16, and AlexNet fine-tuned for MINC) I have a shallow understanding of Python scripting but am not experienced enough. So, I need someone to shortly teach me how to: _Install Caffe and its dependencies on my mac system, import it in the terminal and Jupyter notebook. _Run the code (in the link above) for a new input image.
I have 4 cnn (Alexnet, Mobilenet, resnet and inception) trained on a dataset, I need to get a better result using ensemble learning.
...extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor’s CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accu...
There are different categories of food products. This project identifies the image and then classifies it into a particular category. In the current CNN model, the products with white or plain packaging are misclassified as flour. This must be rectified. Optimize CNN: 1. balancing, feature selection 2. Optimize the existing CNN model to increase the prediction rate
Needed someone Expert About Cnn model: There are different models such as LetNet-5, AlexNet, VGG-16, Inception-VI, Inception-V3, ResNet-50, Xception,Xception-V4, Xception-ResNets and ResNeXt-50. You can use any one of them that is easier for you. what i need is to create the graphs for these metrics Accuracy, Roc, Auc.
Hi James Munyori G.,I want python work,About Cnn model: There are different models such as LetNet-5, AlexNet, VGG-16, Inception-VI, Inception-V3, ResNet-50, Xception,Xception-V4, Xception-ResNets and ResNeXt-50. You can use any one of them that is easier for you.I need You to create the graphs for these metrics Accuracy, Roc, Auc.
I am looking for a high quality researcher in computer science and vision who has scientific knowledge in deep neural networks and machine learning ..I am looking for an expert in deep learning neural network, machine learning and python, who is knowledgeable in triplets network, alexnet, ResNet architecture and python libaries, training the network and extracting key features and who also has solid background in SVM, image processing, face detection and recognition the project is about doing research creating course contents and writing a technical report on CNN and DNN about the experiments i have conducted, it will include deep research analysis ...different deep learning methodologies, their pros and cons ..it will be a highly technical report like a journal paper
Project OUTLINE REAL-TIME OBJECT DETECTING USING SPIKING CONVOLUTIONAL NEURAL NETWORK In this research, a real-time object detection was planned to be processed using a Spiking Convolutional Neural Network (SCNN) with temporal coding into the AlexNet architecture with a novel adaptive chimp optimiser for real-time object detection. This novel Adaptive spiking AlexNet convolutional chimp optimiser is the spiked-based real-time object detection model that provides near-lossless information transmission in a shorter period of time for deep SCNN. The primary layer of this SCNN consists of dissimilar Gaussian filters to predict the contrast of the video frames from the real-time video datasets. Hereafter, it encodes the contrast strength over the latencies. The design of spikes...
You will be provided with a public face database that contains multiple face images from 100 subjects. The face images were captured in different poses and different lighting conditions. You will use one face image from each subject to train/build your computer program and recognise the remaining face images of these subjects. At least two face recognition method like AlexNet etc. All programming must be done in MATLAB.
...have access to Old Task data. Then we should apply both methods on two New Tasks (VOC and CUB) At the end we should have a table of results like this: ILSVRC 2012 -> PASCAL VOC 2012 ILSVRC 2012 -> CUB Learning without Forgetting Joint Training I also need a report which should contain: 1- Literature review of previous works 2- Description of Methodology used in the assignment for example AlexNet 3- A little information about Datasets 4- Results This project is supposed to be done by Python, preferably PyTorch, however, TensorFlow or Keras are fine as well. ...
I have a few Python deep learning architectures and I am getting errors. so, I need an expert who can resolve the error. AlexNet (2012) for Image Classification GoogleNet (2014) for Image Classification Inception-v1 (2014) for Image Classification MobileNet (2016) for Image Classification Resnet-50 for Image Classification VGG-16 for Image Classification Xception (2016) for Image Classification ZF Net(2013) for Image Classification I have code of these architecture
When I describe a photo of a plant, I want it to write to me which plant it is. I want to use Folio Dataset. It is preferred to use Python. You are free in the architecture to be used. AlexNet or others. Where the application works; Could be Jupyter Notebook and Colab
Dataset provided includes labelled Cytological Images. GAN to be used along with augmentation Data pre-processing should include both with and without Image augmentation. Implement the following architectures for the given dataset in Google Colab. 1. AlexNet 2. VGG16 3. VGG19 4. GoogleNet Comparative analysis of the used architectures with following plots in a document: 1. training accuracy vs epoch 2. validation accuracy vs epoch 3. test accuracy (scratch vs fine tune)/epoch 4. confusion matrix without normalization 5. confusion matrix with normalization 6. accuracy score 7. Cross Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch 8. model accuracy(accuracy versus epoch) 9. false positive 10. false negative A simple web application (graphical user interface) ...
Dataset provided includes labelled Cytological Images Data pre-processing should include both with and without Image augmentation. Implement the following architectures for the given dataset in Google Colab 1. AlexNet 2. VGG16 3. VGG19 4. GoogleNet Comparative analysis of the used architectures with following plots in a document: 1. training accuracy vs epoch 2. validation accuracy vs epoch 3. test accuracy (scratch vs fine tune)/epoch 4. confusion matrix without normalization 5. confusion matrix with normalization 6. accuracy score 7. Cross Entropy loss vs epoch, train accuracy vs epoch, test accuracy vs epoch 8. model accuracy(accuracy versus epoch) 9. false positive 10. false negative A simple web application (graphical user interface) for validation with options to choose the ...
customize the standard CNN architectures for image classification. Standard CNNs such as AlexNet, GoogleNet, ResNet, etc. should be used to create customized version of the architectures. You are also required to implement a custom CNN architecture for object detection and localization. Both the customized CNNs (image classification and object detection) should be trained and tested using the dataset provided.
This is simple implementation of CNN. With distributed skeleton code, I need to implement both LeNet and AlexNet for my research review. This will be an educational example for deep-learning beginners. You can edit only Model section, and need to add few lines. I hope you can do this work right now.
I have already done threeshold based pruning on Fully connected layer of Alexnet, I need someone to implement a GA framework to Further prune the Convolutional layers of Alexnet with slight loss of accuracy
hello Sajjad, I am doing project face recognition system in MATLAB with using alexnet. I have trained the classifier and test it on images. but I need to apply it in video and complete the recognition. if you interested we can talk about details and price. best regards
hello Vladimir, like you I am also trying to complete my master degree and I need help for some project. I am doing project face recognition system in MATLAB with using alexnet. I have trained the classifier and test it on images. but I need to apply it in video and complete the recognition. if you interested we can talk about details and price. best regards mother Russia
Hello Muhammad, I have a project about face verification with alexnet using MATLAB. I have completed some part of it but for finish it I need your help. If you interested with job offer please answer me so we can talk about details
Hello Ahmed, I have a project about face verification with using alexnet in MATLAB. I have completed some part of it and I would like to ask your help for complete it all. If you interested with job offer we can talk about details
I want a website written in python that have the following: 1- 3 account types (doctor, patient, ambulance) different permision and functionaities for each user account (I will mention them later) 2- chatting functi...that have the following: 1- 3 account types (doctor, patient, ambulance) different permision and functionaities for each user account (I will mention them later) 2- chatting functionality between doctor and patient with sending location option 3- automatic diagnosis for Afib desise by uploading ECG images from patient dashboard. the system must use nural network approach specifically must use AlexNet model to diagnose the desiese 4- the website should have appointment reservation system 5- the website should have patient medical history we will discuss about the deta...
About me: ML/AI Researcher - wants to get the best res...and we need the implementation run on GPU with the best results possible. The second step(next milestone) would be to integrate it into a mobile application. This project requires experience with: -Successfully implementing and achieving good results with deep learning models + transfer learning. -Computer vision, Deep learning, OpenCV, python, TensorFlow, Keras, Convolution Neural Network, Alexnet, VGG16, GoogleNet etc. + Jupiter notebook/Pycharm -writing skills to draft the methodology used along with the results - deliverable Deliverable 1. Ideas + Implementation and testing on the dataset(best results in the domain) with image processing, transfer learning and CNN 2. 1-2 page brief writeup on the implementation + result...
I need an Android app. I would like it designed and built. Deep compression of AlexNet
Need to use deep learning using Matlab. More details will be provided.
Emotion recognition using convolution neural network , implement paython code using AlexNet, and ck+ dataset , and i want it demo and live , and i hope the accuracy is good . Please if you can do it tell me the price and time that you need it and the expected accuracy for your work. Regards
This project is used to identify the human act by image processing in real time, and the main intention is to communicate the identified gestures using the camera system we want to show image analysis with different kind of algorithms and dataset on multiple GPU .. we want to get the machine...identify the human act by image processing in real time, and the main intention is to communicate the identified gestures using the camera system we want to show image analysis with different kind of algorithms and dataset on multiple GPU .. we want to get the machine benchmarking done .. we will test and train it on multiple kinds of machines example : we can use algorithms like CNN and Dataset : Alexnet, imagenet , etc So we can use a performance tools or write scripts to get Machine be...
looking for an expert in deep learning neural network, machine learning and classification via cnn , alexnet, who is knowledgeable in alexnet architecture and keras or matlab and etc and who also has a solid background in SVM, image processing, face detection, and recognition. - must be capable of training CNN neural network, extracting features for further training - train and test models and improve the accuracy of the classification results ONLY Apply for this job if u can implement this paper and generate good results instead, I want to work with the plant for the detection of the diseases. - the goal is to show that alexnet + SVM yields better result that alexnet + softmax
Alexnet on PYNQ should be implemented by creating IP of nueral network
I would like to create a project for face recognition from video using Alexnet and Googlenet
...rate say 5 frames per second. This is what should happen in analysis - for each frame of a video (at the given frame rate), a state-of-the-art deep learning based object detector (for example YOLOv3) should be used to detect several objects like man, woman, face, child, vehicle, bus, truck, car, bicycle, motorbike etc. and for each detected object, it should extract features/embeddings using, say AlexNet and storing them to an efficient persistent store with appropriate tagging to be able to retrieve the analysis results efficiently for any given frame of any given video. The important thing is that the hardware of choice for this "solution" should be such that 24 hours of videos can be analyzed (as above) in 10 mins. Also, the solution should be developed as a module a...
In project we have two types of images data, one normal and effected image and we need to classify the both type image by using these following method below. Project Requirements Classified the both type of images, by using following type of method. Normal and effected images [1]. Logistic Regression [2]. ...types of images data, one normal and effected image and we need to classify the both type image by using these following method below. Project Requirements Classified the both type of images, by using following type of method. Normal and effected images [1]. Logistic Regression [2]. SVM (support Vector machine) [3]. LR+SVM [4]. CNN (Convolutional Neural Network) but in this you cannot use Alexnet, GoogleNet, VGG, you can only use some other method like ResNet-101, in...
Face Recognition of CNN and AlexNet by python Details: Cost of project :250 Canadian dollar. 25 C$ : start project , 100 c$: received code, 50 c$ : after setup and run, 75 c$ :after out put result and testing. Last step (output and testing): maybe need two weeks to four weeks. Deadline: August 10, 2018. ORL database Implement below algorithms by python (version 3.6 or 3.5) 1- CNN : attach paper help you. 2- Alex Net : the link below help you . ~guerzhoy/tf_alexnet/  A. First experiment: Only one image per person will be used for the training. Thus, the total number of images in the training dataset is 40 images. During this experiment, we will check three different use-cases
Looking for a software developer with experience in image classification and segmentation. We are currently using DIGITS software, Tensorflow/Keras architectures, and open source frameworks (AlexNet, LeNet, etc.). We are looking for help with customizing these frameworks for our specific applications. Favour will go to applicants who have had experience with image based deep learning.
