Cntk onnxJobs

OpenCV 4.9.0 should be build for the new C++ Builder 12.1 (bcc64x, Windows modern). With this compiler It should be easier to build ...see (splitted in the 2 zips) and there. This was done for OpenCV 4.8.1 for the old compiler (bcc64). This should be updated for the new compiler (bcc64x) and OpenCV 4.9.0. The produced files when clicking "onnx" should be equal to the given files in Win64ReleaseresultsNominal Prerequisite: - C++ Builder 12.1 (Maybe there is a trial version, you can check yourself.) - no access to my PC Task: - Give instructions and changed code that I can build OpenCV 4.9.0 on my PC with CMake GUI and C++ Builder - Test, especially "onnx" have to be passed (see above). Money will paid if I can build OpenCV 4.9.0 and the tests are pas...

My project involves image classification and I'm seeking a skilled developer to convert my Python project/models to ONNX. The current format of these models is PyTorch. It's important that the conversion is flawless and optimized for face liveness detection in a C# environment. Next, I want the ONNX models integrated into a sample C# application. The focus will be on user interaction, so knowledge of GUI development would be beneficial. Ideal applicants should have: - Expertise in ONNX, Python, and C# programming - Experience with PyTorch and face liveness detection models - Proficient with integrating ONNX models into C# apps Please detail your experience with similar projects in your bid.

im looking for a freelancer who can do pytorch,unet segmentation, onnx inference , tensorrt, tracking algorithms and make changes or modify the architecture of unet and modify the tracking algorithms architectures

...and evaluate performance using different tracking algorithms. - Aim to improve the inference speed of the detection process. **Comparative Analysis Focus:** - Accuracy of detection. - Speed of inference. - CPU/GPU resource consumption. **Skills and Experience Needed:** - Proficiency in Python programming. - Extensive experience with deep learning frameworks, specifically TensorFlow, PyTorch, and ONNX and tensorrt and openvino. - Expertise in implementing YOLO (You Only Look Once) models for object detection. - Familiarity with different tracking algorithms, plus an interest in MOSSE and CSRT trackers for a broader understanding. - Ability to analyze and optimize performance for real-time applications. **Outcome Expectations:** I am striving to not only achieve but surpass the...

...machine learning models, specifically Huggingface and ONNX. The project involves converting the Salesforce/blip-image-captioning-large model from Huggingface to ONNX Runtime format. I need someone to convert this image-to-text model on into an ONNX model run in Qt / C++: Salesforce/blip-image-captioning-large The job is done when a very simple Qt console app is able to read an image into a QImage and output the same text to the console that is displayed when running the model in the hugging demo (Inference API). You should also provide a documentation on steps taken and eventually scripts implemented for the conversion. Ideal Skills for Job: - Proficiency in machine learning models - Experience with Huggingface and ONNX - Familiarity with Salesforce/blip...

Hi, I want to port the open source application to iPad. Two solutions for running onnx models on iOS. 1. Integrate ONNX Runtime for iOS into your project. 2. It might be a good idea to convert your ONNX model to Core ML format, as Core ML is optimized for iOS devices and can more effectively take advantage of the device's hardware acceleration capabilities. This requires model transformation using Core ML Tools. can you help me?

I am looking for a skilled developer who can write me a Python code quantize an ONNX object detection model (weights and bias) to Int16. There should be some Quantization Frameworks like tensorflow, onnxruntime etc. to convert float32 to int16, but i could not find it. (i only found int8 converter but i need int16 conversion) Ideal skills and experience for this project include: - Proficiency in working with ONNX Yolo object detection models - Experience with quantization processes and techniques - Knowledge of Int16 data type and its implications in machine learning models

Hi, just gto set up another project. You know, I just send you the notebook concerning this project. I'ii wait the 2-3 hours until you are back.

Need sample code in Flutter calling onnx model and also utilize GPU. It should work for both iOS/android devices.

I would like to use ONNX in React Native and convert Python models to optimized ONNX models for mobile. Specifically, I would like to use this project with ONNX Runtime in Typescript: Project Link: Here are some additional links to see if you can help me: - Models: - More about ONNX Runtime: - ONNX Runtime GitHub: - ONNX Runtime for React Native: - ONNX Runtime Inference Examples in JavaScript: I hope you can assist me.

We need a script, which will be used to check that an image contains a face which is detectable or not. This script's main goal is basically face detection and it will decide that we can process the image or not. We use this line for face swapping: insightface.model_zoo.get_model('inswapper_128.onnx')

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NOTE: This is an urgent requirement, and hence if you have either prior experience or better a pre-existing model/algorithm in a demo-able state, preference would be given. I am in need of a freelancer who can develop a computer vision detector to check if any person in view is using a mobile phone. Platform: Python is okay, however final model needs to be delivered in .onnx format of a given opset version(I can assist you with same) The overall algorithm should be realtime. Accuracy: I expect a high accuracy of above 90% in detecting if a person is using a mobile phone, the algorithm needs to be robust across reasonable camera views. Deadline: This project is urgent and needs to be completed within a week. Skills and Experience: - Strong proficiency in computer vision an...

...accuracy fluid spillage/leak detector that operates in a camera view. The project requires the following: Fluid Identification: - The detector should be able to accurately identify water/oil spills or leaks. Environment: - The detector should be designed to work in both indoor and outdoor environments. Detection Method: - Only Algorithm is required, all the ai models used need to be delivered in .onnx format with a specified opset version. The platform is in the place already Ideal Skills and Experience: - Experience in designing and developing fluid detection systems. - Strong knowledge of computer vision and image processing. - Familiarity with camera technology and integration. - Proficient in programming languages such as Python or C++. - Ability to create a user-friendly...

I am looking for a frontend developer to create the frontend for our AI/Healthcare platform. Specific design in mind: - I have a specific design in mind for the frontend and would like the developer to follow this design. Main functionalities: - User Registration/Login - Data Upload - Data Visualization - Calling APIs and running AI-models (e.g. onnx format) shoudl be possible - Nice Animation for waiting time Ideal skills and experience: - Strong frontend development skills, with experience in creating user-friendly and visually appealing designs. - Proficiency in HTML, CSS, and JavaScript. - Experience with designing and implementing user registration/login systems. - Familiarity with search functionality and user profile customization. - You can choose your preferred framewo...

I am looking for someone to convert code from CNTK to Keras. The code is currently written in Python and I would like the conversion to maintain a high level of accuracy. The new code should be written using the Keras deep learning framework. Skills and experience required for this project include: - Strong proficiency in Python programming language - Experience with deep learning frameworks, particularly Keras - Knowledge of CNTK and the ability to convert code between frameworks - Attention to detail to ensure a high level of accuracy in the conversion process. Below is the github repository of the code to be converted to keras from cntk. This is a simple hangman game and I need

I need code for inference (usage) of onnx models exported from Yolov8 segmentation (and object detection and classification) to run under Visual Studio c++. I can provide snippets in Python. The A. onnxruntime 1.13.1 and OpenCV 4.5 B. OpenCV 4.7 Important is, I need it for 32 bit and 64 bit. If A or B does not work for 32 bit it cannot be used. Deadline Monday morning (at least A. segmentation)

I need code for inference (usage) of onnx models exported from Yolov8 segmentation (and object detection and classification) to run under Visual Studio c++. I can provide snippets in Python. The A. onnxruntime 1.13.1 and OpenCV 4.5 B. OpenCV 4.7 Important is, I need it for 32 bit and 64 bit. If A or B does not work for 32 bit it cannot be used. Deadline Monday morning (at least A. segmentation)

I need code for inference (usage) of onnx models exported from Yolov8 segmentation (and object detection and classification) to run under Visual Studio c++. I can provide snippets in Python. The A. onnxruntime 1.13.1 and OpenCV 4.5 B. OpenCV 4.7 Important is, I need it for 32 bit and 64 bit. If A or B does not work for 32 bit it cannot be used. Python files were sent in the chat, as well as discussion there. Time: 1 day Budget: 100 Euro

I am looking for a computer vision expert who can assist me...Mtcnn multi-face detection using the tensorrt engine - Class Facenet pytorch for creating embedding and compare faces This needs to produce the same result of function or cosine similarities and give the inference with almost the same accuracy as in the normal model. The above are the functionalities expected of the python code/classes to be provided to me along with the tensorrt/onnx model and the functions/command to do the conversions. If you can use any of the classes from that would be great. If not is also fine. I will be providing the dataset for training and testing, and the desired accuracy of results should be high (>95%). Contact via message for more info.

I'm looking for a Unity developer who can help me implement a key point detection ONNX model in Unity. The task involves integrating the model into a Unity project and utilizing it to detect key points in real time. Additionally, I would like to apply the model in both AR (Augmented Reality) and VR (Virtual Reality) scenarios.

I'm looking for a Unity developer who can utilize Unity to execute the ONNX model. It's a relatively small project. Please direct message me if interested. Contact number: +91 8790654369. The task involves integrating the model into a Unity project and utilizing it to detect key points in real time. Additionally, I would like to apply the model in both AR (Augmented Reality) and VR (Virtual Reality) scenarios.

I am looking for a Full Stack Developer to build our Web Interface for our API. The ideal candidate will have experience with JavaScript and React, Python. Skills and Experience: - Full Stack Development - JavaScript - Python - React//MERN - ONNX Framework - Nvidia Triton/Nvidia NGC - Understanding of UI/UX Web App control methodologies and frameworks - Docker We have all of our proprietary documentation that provides a guide on how developers can build a web interface for the WebAPI. By following these steps, you will be able to create a user-friendly web application that interacts with our API to generate predictions based on user input. Dev must have understanding of how stable diffusion apps work.

We're looking for help with converting a pre-trained PyTorch convolutional neural network into a format (e.g., ONNX runtime) that allows for inference to be run in browser. We're a research group focusing on studying methods to evaluate and boost creative thinking. Currently, we're developing a creativity training application that employs AI to offer instant feedback to users on their drawings.

I will be providing an ONNX V.6 AI model and a WPF application referencing a functional ONNX V.3 model. Your job would be to replace the ONNX V.3. model referenced in the WPF application with the ONNX V.6 model provided. I should then be be able to enter a sport image on the UI and get the model output on the screen (just like V.3.). Your second deliverable is to provide a C# console application that iterates through a folder full of sport images and at each image the application calls the ONNX V6. model and pops a message box with the model's output.

I have .pth file. You need to convert to .onnx and then to .trt file

Perform Object Detection Inference with Pretrained YOLOv8 Model - Purpose: Object Detection - Preferred Pretrained Model: YOLO - Expected Output Format: Class labels with confidence levels for each predicted level - Language: Python 3.10 The YOLOv8 object detection model is stored as an ONNX model in a GCP storage bucket. The model needs to be loaded, an image needs to be preprocessed, and the object detection inference needs to be performed with the expected output format. Ideal Skills and Experience: - Experience with object detection and computer vision - Familiarity with YOLOv8 framework and pretrained models - Proficiency in Python

Title: 3D Detection Project for Feet and Shoes I am looking for a freelancer who can develop a 3D detection system for feet and shoes. The system will be used for surveillance and robotics applications. Ideal Skills and Experience: - Experience in developing 3D detection technology for surveillance and robotics - Proficiency in using tflite or onnx for 3D detection methods - Ability to create detailed project proposals outlining the approach and methodology - Past work demonstrating success in developing 3D detection technology The successful freelancer will be required to include a detailed project proposal outlining their approach and methodology for the project.

I have an YOLOv8 (ultralytics) segmentation model which is exported as onnx. With python, it should be developed the calculation of inference with the exported onnx model for segmentation. The bus (see below) should be used as sample image. Results must be (almost) identical as the original prediction (see below, ). Except of standard python packages, only the following packages are allowed: - cv2 (opencv) - numpy The code should be similar to the sample for object detection from ultralytics (). Files: - in Colab calculated model and export as well as sample prediction - original image () - result with original

I need a model for gender and age recognition from the live camera stream, so as model's integration into my existing QT/C++/OpenCV application. App currently supports person's detection only (e.g. body as a whole). App must work on Nvidia Jetson Nano....Nvidia Jetson Nano. Requirement is that you should already have model developed, because when I accept You I'd need to test the solution with my own face through the live camera's stream, and testing results we would check together in real-time through desktop share. Additional questions: 1.) What model do you use? I ask because I use optimized inference engine that I generate in the 2nd step from Onnx models. 2.) Can your model be trained on my own Pytorch 1.12.1 server with Nvidia 1050Ti GPU? 3.) I don'...

...com/research/whisper The deliverables should be available via github and implemented on the afore-mentioned server, with a documentation. 4 milestones; 1- developing the flask app (use python request, android request and to get and post information from the app) 2- training data of korean whispers data and optimize it. (building korean whisper tiny, debugging and speed tests on inference with onnx and hyperparameter search) 3- make real time prediction. (to tune on this korean dataset to give a korean whisper tiny model) 4- REST API for any device (provide a link for making API request to the app, FASTAPI/Streamlit and frontend building ) ...

I am looking for someone to optimise GFPGAN to make it faster. Currently, it is running at a moderate speed, but I am looking for an experienced expert who can increase the performance significantly. I expect the selected individual to work on improving the GFPGAN model by changing it to ONNX / TensorRT. The expected outcomes from the optimisation should be improved inference speed. If you have the skills and expertise to help me realise this goal, I look forward to hearing from you!

Please go through the project details and lets discuss afterward. Here is what I am expecting: 1. Find he contact point than keep detecting or tracking the correct contact point even switching of catenary wires in video input. 2. Making a full pipeline using yolo model to detect Pantograph, converting your model or algorithm to Onnx format and running inference with opencv dnn module using c++ code. 3. Testing on different videos or scenarios and overall accuracy which should be more than 85%. If needed reducing DL Model Size, optimization for Nvidia Board and obtaining more 15FPS speed. Here are some of the reference files: is what we are looking for. is the yolov8 model trained to detect the pantograph. file is running the object detection using opencv dnn

I'm looking for an...team. The candidate that I'm looking for should have significant expertise in Python and Supervised Learning, as well as knowledge in Java and Scala. It would be great if you have experiences in working on different ML models, in particular for building prediction models and computer vision applications. Ideally, you should have expertise in deep learning frameworks such as TensorFlow, Keras, and CNTK, as well as knowledge of standard ML algorithms such as regression, clustering, and classification. Experience in cloud platform like AWS or Azure is an added bonus. I'm looking for an expert level candidate who can work independently and can provide high-level evaluations and presentations of the results. If you think you match this description, don...

...used for inference models developed with YOLOv8 which are exported as onnx models. 1) Open (based on the from YOLOv8) in Colab and run 2) After running through there are a) 3 sample models: - model_det Detection model model_det started with YOLO('') # load a pretrained YOLOv8n detection model - model_seg Segmentation model model_seg started with YOLO('') # load a pretrained YOLOv8n segmentation model - model_cls Classification model model_cls started with YOLO('') # load a pretrained YOLOv8n classification model b) sample results for usage of the models with the sample images (included in the outputs) 3) Each model should be exported as onnx and downloaded for inference calculation on a Windows 10 system using

...used for inference models developed with YOLOv8 which are exported as onnx models. 1) Open (based on the from YOLOv8) in Colab and run 2) After running through there are a) 3 sample models: - model_det Detection model model_det started with YOLO('') # load a pretrained YOLOv8n detection model - model_seg Segmentation model model_seg started with YOLO('') # load a pretrained YOLOv8n segmentation model - model_cls Classification model model_cls started with YOLO('') # load a pretrained YOLOv8n classification model b) sample results for usage of the models with the sample images (included in the outputs) 3) Each model should be exported as onnx and downloaded for inference calculation on a Windows 10 system using

...used for inference models developed with YOLOv8 which are exported as onnx models. 1) Open (based on the from YOLOv8) in Colab and run 2) After running through there are a) 3 sample models: - model_det Detection model model_det started with YOLO('') # load a pretrained YOLOv8n detection model - model_seg Segmentation model model_seg started with YOLO('') # load a pretrained YOLOv8n segmentation model - model_cls Classification model model_cls started with YOLO('') # load a pretrained YOLOv8n classification model b) sample results for usage of the models with the sample images (included in the outputs) 3) Each model should be exported as onnx and downloaded for inference calculation on a Windows 10 system using

...10 to 15 years • 4+ years of experience in a Presales role in Data Science/predictive modelling. 2+ years very strong experience as a Presales Data Scientist will also be considered. • 2+ years of experience in customer facing role in US markets preferable. Experience on machine learning algorithms, DL frameworks and compilers, such as PyTorch, TensorFlow, TensorFlow Lite, TVM, TFLM, Glow, ONNX • Experience in building machine learning solutions applied to real-world problems with optimization techniques. • Collaborate with a cross-disciplinary team to design and implement novel AI solutions for edge devices. • Hands-on experience building and implementing machine learning or AI models Experience collaborating with business stakeholders to ensure ...

The project is to analyse a cough sound using a pre-trained PyTorch model inside a React Native application. This need to be offline. Need help from someone who have done this already. ONNX runtime is preferred.

Fix the following issues in the conversion of the yolov7 and yolov7-w6 based two models to TFLite format- 1. The input tensor should have dimensions (1 x 1280 x 1280 x 3) instead of (1 x 3 x 1280 x 1280) as required by TFLite in android studio. 2. Converted onnx file must generate same results as original yolov7-w6 model. 3. Converted ONNX to TFLite model must generate same results as original one. 4. Test the TFLite in android studio. The code for conversion from yolov7 to onnx is here- But it generates tensor with (1 x 3 x 1280 x 1280) dimension instead of (1 x 1280 x 1280 x 3) so you need to debug or dependent code to fix this issue for me.

We are looking for a talented MLOPS engineer to work on a challenging speech recognition project. The project has a tight deadline of 5 days. The tasks involved are: Based on the wav2vec2 model available in the repository lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2, convert it to ONNX and TensorRT Evaluate the WER of the model in TensorRT compared to the original model in Hugginface Create a Dockerfile with the Triton server configured with an endpoint to consume the model in TensorRT Create a Dockerfile with a Python server using to send audio to the Triton server for inference Create a Dockerfile with a JavaScript client sending audio from the microphone to the Python server, from Python to the Triton server through GRPC, and back to the browser with the transcription

We need a machine learning and data science expert who can create a facial recognition software for us using Insightface face recognition library. Insightface gives you pre trained onnx models, based on Pytorch framework. As Insightface is Python library, the software must be in Python. Detailed specification is attached here as docx. You can expect response by tomorrow. We may provide you GUI for the software.

The goal is to complete Yolov5 image segmentation inferences in Java. So far, I am using OnnxRuntime to send the image tensor to the model and receive the inference tensors. However, the functions used for converting the output tensors to masks are more challenging and need to be completed. ...com/ultralytics/yolov5/blob/064365d8683fd002e9ad789c1e91fa3d021b44f0/utils/segment/general.py#L43 The job is successful once we can apply non_max_suppression and process_mask to our arrays/tensors in Java. However, there should not be any significant differences in speed between the Python and Java Implementations. Please send a proposal if you have strong experience with numpy/pytorch, tensors, arrays, ONNX and feel you can complete the job AFTER looking at the two Python functions displa...

Hello Android Developers! PLEASE read this description very carefully. Take your time, I will not decide for a freelancer within hours. This is a computer vision / computation photography project. If you are not expert in this field, please skip. ONLY APPLY if you are fluent in JAVA, openCV, Camera2 API, Gradle & ONNX on Android!!! I need someone to implement an Android app as an Android Studio Gradle project in Java, openCV and ONNX with the following basic features: - Show (fullscreen) HDR camera preview (like in this demo implementation ) - if possible without the depreciated Renderscript implementation - Add a „take image“ button (round botton in the middle on the right screen edge when in landscape), like in this screenshot:

I'm using ESP32 discovery board. The goal of the project is that, i want the device to take an audio wav file from the SD card and use AI to classify and display the result on the board's LCD screen. This is an audio neural network i designed which is now in ONNX format. I want to use XCUBE AI library from CUBE IDE or if there is any other way possible also is fine, but i prefer xcube ai so it generates the required files. Only bid person who can create breadboard and test it with firmware.

...have an Ubuntu-Dev-Server to work with to install : OpenAi (pip install openai) OpenAi-Server (git , pip3 install -r ) OpenAI-API (api-key-create) Install RNN and Transformers (pip3 install transformers) Tensor Flow (pip install tensorflow keras), Pytorch (pip install pytorch) and ONNX (pip install onnx) with BERT (pip3 install transformers) Installed - with simple custom form interface to allow import into either Tensorflow, Pytorch or via ONNX a .html page or .pdf file. - Import existing learning models eg: Davinci-3, ChatGPT or others. (Download the text-davinci-003 model from the OpenAI API by following the instructions here: ) I consider this a small project as you will have a clean

I need someone that from a resnet 50, pre-trained, created with pytorch, export it to onnx, then to engine and create a pipeline with Nvidia deepstream (if possible with Python) that opens a video from a webcam (comes from /dev/video0), paint on the video the detected class and send the video by udp or rtsp to a given port. You will have to give me all the code

...org/get-started/locally/ (recommended) torchvision>=0.8.1 tqdm>=4.64.0 # protobuf<=3.20.1 # # logging ------------------------------------- tensorboard>=2.4.1 # wallb # clearml # Plot ------------------------------------ pandas>=1.1.4 seaborn>=0.11.0 # Export ------------------------------------- # coremltools>=6.0 # CoreML export # onnx>=1.9.0 # ONNX export # onnx-simplifier>=0.4.1 # ONNX simplifier # nvidia-pyindex # TensorRT export # nvidia-tensorrt # TensorRT export # scikit-learn<=1.1.2 # CoreML quantization # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) # tensorflowjs>=3.9.0 # export # openvino-dev # OpenVINO export # Deploy -------------------------------------- # tritonclient[all]~=2.24.0 # Tools -...

we are facing some issues while converting onnx model to rknn model , i need someone to fix the issue. @100 - 300