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gluoncv
Table Of Contents
  • Installation
  • Model Zoo
    • Classification
    • Detection
    • Segmentation
    • Pose Estimation
    • Action Recognition
    • Depth Prediction
  • MXNet Tutorials
    • Image Classification
      • 1. Getting Started with Pre-trained Model on CIFAR10
      • 2. Dive Deep into Training with CIFAR10
      • 3. Getting Started with Pre-trained Models on ImageNet
      • 4. Transfer Learning with Your Own Image Dataset
      • 5. Train Your Own Model on ImageNet
    • Object Detection
      • 01. Predict with pre-trained SSD models
      • 02. Predict with pre-trained Faster RCNN models
      • 03. Predict with pre-trained YOLO models
      • 04. Train SSD on Pascal VOC dataset
      • 05. Deep dive into SSD training: 3 tips to boost performance
      • 06. Train Faster-RCNN end-to-end on PASCAL VOC
      • 07. Train YOLOv3 on PASCAL VOC
      • 08. Finetune a pretrained detection model
      • 09. Run an object detection model on your webcam
      • 10. Skip Finetuning by reusing part of pre-trained model
      • 11. Predict with pre-trained CenterNet models
      • 12. Run an object detection model on NVIDIA Jetson module
    • Instance Segmentation
      • 1. Predict with pre-trained Mask RCNN models
      • 2. Train Mask RCNN end-to-end on MS COCO
    • Semantic Segmentation
      • 1. Getting Started with FCN Pre-trained Models
      • 2. Test with PSPNet Pre-trained Models
      • 3. Test with DeepLabV3 Pre-trained Models
      • 4. Train FCN on Pascal VOC Dataset
      • 5. Train PSPNet on ADE20K Dataset
      • 6. Reproducing SoTA on Pascal VOC Dataset
      • 7. Test with ICNet Pre-trained Models for Multi-Human Parsing
    • Pose Estimation
      • 1. Predict with pre-trained Simple Pose Estimation models
      • 2. Predict with pre-trained AlphaPose Estimation models
      • 3. Estimate pose from your webcam
      • 4. Dive deep into Training a Simple Pose Model on COCO Keypoints
    • Action Recognition
      • 1. Getting Started with Pre-trained TSN Models on UCF101
      • 10. Introducing Decord: an efficient video reader
      • 2. Dive Deep into Training TSN mdoels on UCF101
      • 3. Getting Started with Pre-trained I3D Models on Kinetcis400
      • 4. Dive Deep into Training I3D mdoels on Kinetcis400
      • 5. Getting Started with Pre-trained SlowFast Models on Kinetcis400
      • 6. Dive Deep into Training SlowFast mdoels on Kinetcis400
      • 7. Fine-tuning SOTA video models on your own dataset
      • 8. Extracting video features from pre-trained models
      • 9. Inference on your own videos using pre-trained models
    • Object Tracking
      • 01. Single object tracking with pre-trained SiamRPN models
      • 02. Train SiamRPN on COCO、VID、DET、Youtube_bb
      • 03. Multiple object tracking with pre-trained SMOT models
    • Depth Prediction
      • 01. Predict depth from a single image with pre-trained Monodepth2 models
      • 02. Predict depth from an image sequence or a video with pre-trained Monodepth2 models
      • 03. Monodepth2 training on KITTI dataset
      • 04. Testing PoseNet from image sequences with pre-trained Monodepth2 Pose models
    • Prepare Datasets
      • Prepare ADE20K dataset.
      • Prepare COCO datasets
      • Prepare COCO datasets
      • Prepare Cityscapes dataset.
      • Prepare ILSVRC 2015 DET dataset
      • Prepare ILSVRC 2015 VId dataset
      • Prepare Multi-Human Parsing V1 dataset
      • Prepare OTB 2015 dataset
      • Prepare PASCAL VOC datasets
      • Prepare Youtube_bb dataset
      • Prepare custom datasets for object detection
      • Prepare the 20BN-something-something Dataset V2
      • Prepare the HMDB51 Dataset
      • Prepare the ImageNet dataset
      • Prepare the Kinetics400 dataset
      • Prepare the UCF101 dataset
      • Prepare your dataset in ImageRecord format
    • Auto Module
      • 01. Load web datasets with GluonCV Auto Module
      • 02. Train Image Classification with Auto Estimator
      • 03. Train classifier or detector with HPO using GluonCV Auto task
    • Distributed Training
      • 1. Distributed training of deep video models
    • Deployment
      • 1. Export trained GluonCV network to JSON
      • 2. GluonCV C++ Inference Demo
      • 3. Inference with Quantized Models
  • PyTorch Tutorials
    • Action Recognition
      • 1. Getting Started with Pre-trained I3D Models on Kinetcis400
      • 2. Fine-tuning SOTA video models on your own dataset
      • 3. Extracting video features from pre-trained models
      • 4. Computing FLOPS, latency and fps of a model
      • 5. DistributedDataParallel (DDP) Framework
  • API Reference
    • gluoncv.data
    • gluoncv.data.batchify
    • gluoncv.data.transforms
    • gluoncv.model_zoo
    • gluoncv.nn
    • gluoncv.loss
    • gluoncv.utils
  • Community
    • Community
    • Contribute to GluonCV
  • Slides
gluoncv
Table Of Contents
  • Installation
  • Model Zoo
    • Classification
    • Detection
    • Segmentation
    • Pose Estimation
    • Action Recognition
    • Depth Prediction
  • MXNet Tutorials
    • Image Classification
      • 1. Getting Started with Pre-trained Model on CIFAR10
      • 2. Dive Deep into Training with CIFAR10
      • 3. Getting Started with Pre-trained Models on ImageNet
      • 4. Transfer Learning with Your Own Image Dataset
      • 5. Train Your Own Model on ImageNet
    • Object Detection
      • 01. Predict with pre-trained SSD models
      • 02. Predict with pre-trained Faster RCNN models
      • 03. Predict with pre-trained YOLO models
      • 04. Train SSD on Pascal VOC dataset
      • 05. Deep dive into SSD training: 3 tips to boost performance
      • 06. Train Faster-RCNN end-to-end on PASCAL VOC
      • 07. Train YOLOv3 on PASCAL VOC
      • 08. Finetune a pretrained detection model
      • 09. Run an object detection model on your webcam
      • 10. Skip Finetuning by reusing part of pre-trained model
      • 11. Predict with pre-trained CenterNet models
      • 12. Run an object detection model on NVIDIA Jetson module
    • Instance Segmentation
      • 1. Predict with pre-trained Mask RCNN models
      • 2. Train Mask RCNN end-to-end on MS COCO
    • Semantic Segmentation
      • 1. Getting Started with FCN Pre-trained Models
      • 2. Test with PSPNet Pre-trained Models
      • 3. Test with DeepLabV3 Pre-trained Models
      • 4. Train FCN on Pascal VOC Dataset
      • 5. Train PSPNet on ADE20K Dataset
      • 6. Reproducing SoTA on Pascal VOC Dataset
      • 7. Test with ICNet Pre-trained Models for Multi-Human Parsing
    • Pose Estimation
      • 1. Predict with pre-trained Simple Pose Estimation models
      • 2. Predict with pre-trained AlphaPose Estimation models
      • 3. Estimate pose from your webcam
      • 4. Dive deep into Training a Simple Pose Model on COCO Keypoints
    • Action Recognition
      • 1. Getting Started with Pre-trained TSN Models on UCF101
      • 10. Introducing Decord: an efficient video reader
      • 2. Dive Deep into Training TSN mdoels on UCF101
      • 3. Getting Started with Pre-trained I3D Models on Kinetcis400
      • 4. Dive Deep into Training I3D mdoels on Kinetcis400
      • 5. Getting Started with Pre-trained SlowFast Models on Kinetcis400
      • 6. Dive Deep into Training SlowFast mdoels on Kinetcis400
      • 7. Fine-tuning SOTA video models on your own dataset
      • 8. Extracting video features from pre-trained models
      • 9. Inference on your own videos using pre-trained models
    • Object Tracking
      • 01. Single object tracking with pre-trained SiamRPN models
      • 02. Train SiamRPN on COCO、VID、DET、Youtube_bb
      • 03. Multiple object tracking with pre-trained SMOT models
    • Depth Prediction
      • 01. Predict depth from a single image with pre-trained Monodepth2 models
      • 02. Predict depth from an image sequence or a video with pre-trained Monodepth2 models
      • 03. Monodepth2 training on KITTI dataset
      • 04. Testing PoseNet from image sequences with pre-trained Monodepth2 Pose models
    • Prepare Datasets
      • Prepare ADE20K dataset.
      • Prepare COCO datasets
      • Prepare COCO datasets
      • Prepare Cityscapes dataset.
      • Prepare ILSVRC 2015 DET dataset
      • Prepare ILSVRC 2015 VId dataset
      • Prepare Multi-Human Parsing V1 dataset
      • Prepare OTB 2015 dataset
      • Prepare PASCAL VOC datasets
      • Prepare Youtube_bb dataset
      • Prepare custom datasets for object detection
      • Prepare the 20BN-something-something Dataset V2
      • Prepare the HMDB51 Dataset
      • Prepare the ImageNet dataset
      • Prepare the Kinetics400 dataset
      • Prepare the UCF101 dataset
      • Prepare your dataset in ImageRecord format
    • Auto Module
      • 01. Load web datasets with GluonCV Auto Module
      • 02. Train Image Classification with Auto Estimator
      • 03. Train classifier or detector with HPO using GluonCV Auto task
    • Distributed Training
      • 1. Distributed training of deep video models
    • Deployment
      • 1. Export trained GluonCV network to JSON
      • 2. GluonCV C++ Inference Demo
      • 3. Inference with Quantized Models
  • PyTorch Tutorials
    • Action Recognition
      • 1. Getting Started with Pre-trained I3D Models on Kinetcis400
      • 2. Fine-tuning SOTA video models on your own dataset
      • 3. Extracting video features from pre-trained models
      • 4. Computing FLOPS, latency and fps of a model
      • 5. DistributedDataParallel (DDP) Framework
  • API Reference
    • gluoncv.data
    • gluoncv.data.batchify
    • gluoncv.data.transforms
    • gluoncv.model_zoo
    • gluoncv.nn
    • gluoncv.loss
    • gluoncv.utils
  • Community
    • Community
    • Contribute to GluonCV
  • Slides

Auto Module¶

01. Load web datasets with GluonCV Auto Module

01. Load web datasets with GluonCV Auto Module¶

02. Train Image Classification with Auto Estimator

02. Train Image Classification with Auto Estimator¶

03. Train classifier or detector with HPO using GluonCV Auto task

03. Train classifier or detector with HPO using GluonCV Auto task¶

Download all examples in Python source code: examples_auto_module_python.zip

Download all examples in Jupyter notebooks: examples_auto_module_jupyter.zip

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01. Load web datasets with GluonCV Auto Module