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Search results for jupyter notebook fpn
fpn
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jupyter-notebook
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20 search results found
Dota Doai
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676
This repo is the codebase for our team to participate in DOTA related competitions, including rotation and horizontal detection.
Detectorch
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567
Detectorch - detectron for PyTorch
Pytorchnethub
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519
项目注释+论文复现+算法竞赛+Pytorch实践
Fpn_tensorflow
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251
This is a tensorflow re-implementation of Feature Pyramid Networks for Object Detection.
Nas_fpn_tensorflow
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188
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.
Severstal Steel Defect Detection
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132
Kaggle Segmentation Challenge
Robust Detection Benchmark
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106
Code, data and benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (NeurIPS 2019 ML4AD)
Tf Eager Fasterrcnn
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66
Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 eager execution.
S4net
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65
Cascade_fpn_tensorflow
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30
Cascade R-CNN: Delving into High Quality Object Detection.
Hnd Ghnd Object Detectors
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23
[ICPR 2020] "Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks" and [ACM MobiCom EMDL 2020] "Split Computing for Complex Object Detectors: Challenges and Preliminary Results"
Tf 1d 2d Segmentation End2endpipelines
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19
1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc.
Hier R Cnn
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18
Hier R-CNN: Instance-level Human Parts Detection and A New Benchmark
Installation Guide Of Maskrcnn
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17
Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.
Bnet
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17
Batch Normalization with Enhanced Linear Transformation
Depth Estimation Pytorch
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13
Monocular depth estimation using Feature Pyramid Network implemented in PyTorch 1.1.0
Singapore Maritime Dataset Trained Deep Learning Models
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10
This repository contains the training configurations for several Deep Learning models trained on the Singapore Maritime Dataset and links to the trained - ready to use - models.
Lyft_perception_challenge
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9
MASK-RCNN implementation for Lyft Perception Challenge
Kits19 Cnn
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6
Using convolutional neural networks for the 2019 Kidney and Kidney Tumor Segmentation Challenge
Pytorch Implementation
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6
Pytorch implementation(LeNet, VGGNet, GAN, UNet, Mask R-CNN, ...)
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