The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.
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Updated
Dec 18, 2023 - Jupyter Notebook
The project focuses on classifying brain tumors using the Multi-Modal Squeeze and Excitation Network.
Realtime Face Recognition using FaceNet architecture
TensorFlow Lite classification on a bare Raspberry Pi 4 at 33 FPS
implementation of Inflated 3D ConvNet in TensorFlow
💵Model Peruvian Bills (MLR, Mask, Inceptionv2) RCNN💶
Music emotions and themes classifier app could recognize 56 classes using three trained models (based on ResNet50, InceptionNetV2, EfficientNetB3), applying the transfer learning approach.
Implementation of various state-of-the-art architectures in Tensorflow, Keras and Python
PyTorch implements `Rethinking the Inception Architecture for Computer Vision` paper.
benchmark of object detection algorithms for license plate detection
Flowers Recognition with transfer learning
Create your own databse, compile tripletloss with pre-trained FaceNet model, run real-time face recognition on local host
This project was completed under the course Deep Learning(CSE674) at University at Buffalo.
Computer Vision .Libraries used matplot, numpy, openCV, mazelib standard machine learning libs you know dude
使用TensorFlow自己搭建一些经典的CNN模型,并使用统一的数据来测试效果。
My PyTorch implementation of CNNs. All networks in this repository are using CIFAR-100 dataset for training.
TensorFlow Lite classification on a bare Raspberry Pi 4 with 64-bit OS at 23 FPS
TensorFlow based mobile neural network model resources
TensorFlow implementation of GoogLeNet.
Models Supported: Inception [v1, v2, v3, v4], SE-Inception, Inception_ResNet [v1, v2], SE-Inception_ResNet (1D and 2D version with DEMO for Classification and Regression)
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