Number detection implemented using TensorFlow with custom CNN architecture for fast inference and custom dataset
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Updated
Jun 2, 2021 - Python
Number detection implemented using TensorFlow with custom CNN architecture for fast inference and custom dataset
Code to mass download images from Google Images using JavaScript Console Window and python script.
Custom keypoint detection using Tensorflow object detection API
Implement image classification in pytorch
PyTorch tutorial for computer vision
A chatbot based on OpenAI, suitable for enterprise privatized data fine-tuning. It can answer various questions related to enterprise products raised by users.
Pytorch implements yolov3.Good performance, easy to use, fast speed.
Yolov3_Tiny hardhat detection using Tensorflow
Make Custom ORB_SLAM2 RGB-D dataset with real sense camera
Part Grouping Network (PGN) implementation in TensorFlow, for custom parsing dataset
Using Keras MobileNet-v2 model with your custom images dataset
Project about developing a model that can detect multiple cards and identify their suit and rank. Includes multiple models, own created dataset, utility functions, presentations, research paper and live demo application. Course Deep Learning, FMI, 2024.
A implementation of Faster RCNN model
This is a deep learning network: ResNet with an attention layer that can be used on a custom data set.
With this tool you can create custom TTS dataset from video or audio.
Instance Segmentation using Mask R-CNN on a Custom Dataset
Instance segmentation using Detectron 2
Custom Dataset Training pipeline using Pytorch and Meta's object detection model DETR.
A computer vision–powered smart self-checkout system that combines YOLOv11 for real-time product detection and Flask for a seamless, interactive billing interface — designed to revolutionize the retail experience.
Add a description, image, and links to the custom-dataset topic page so that developers can more easily learn about it.
To associate your repository with the custom-dataset topic, visit your repo's landing page and select "manage topics."