• My column
• 📢 News!
• Introduction
• All task training results
• Environments
• Download my pretrained models and experiments records
• Prepare datasets
  • CIFAR10
  • CIFAR100
  • ImageNet 1K(ILSVRC2012)
  • ImageNet 21K(Winter 2021 release)
  • ACCV2022
  • VOC2007 and VOC2012
  • COCO2017
  • SAMACOCO
  • Objects365(v2,2020)
  • ADE20K
  • CelebA-HQ
  • FFHQ
  • Places365-standard/challenge
• How to train and test model
• How to use gradio demo
• Citation

My column

https://www.zhihu.com/column/c_1692623656205897728

📢 News!

• 2024/04/15: support segment-anything model training/testing/jupyter demo/gradio demo.

Introduction

This repository provides simple training and testing examples for the following tasks:

task	support dataset	support network
Image classification task	CIFAR100 ImageNet1K(ILSVRC2012) ImageNet21K(Winter 2021 release) ACCV2022	ResNet DarkNet RepVGG RegNetX ViT VAN
Object detection task	VOC2007 and VOC2012 COCO2017 Objects365(v2,2020)	RetinaNet FCOS CenterNet TTFNet DETR DINO-DETR
Semantic segmentation task	ADE20K	DeepLabv3+ U2Net
Instance segmentation task	COCO2017	YOLACT SOLOv2
Knowledge distillation task	ImageNet1K(ILSVRC2012)	KD loss(for ResNet) DML loss(for ResNet)
Contrastive learning task	ImageNet1K(ILSVRC2012)	DINO(for ResNet)
Masked image modeling task	ImageNet1K(ILSVRC2012) ACCV2022	MAE(for ViT)
OCR text detection task	/	DBNet
OCR text recognition task	/	CTC Model
Human matting task	/	PFAN Matting model
Salient object detection task	/	PFAN Segmentation model
Interactive segmentation task	/	SAM(segment-anything)
Image inpainting task	CelebA-HQ Places365-standard Places365-challenge	AOT-GAN TRANSX-LKA-AOT-GAN
Diffusion model task	CIFAR10 CIFAR100 CelebA-HQ FFHQ	DDPM DDIM PLMS

All task training results

See all task training results in results.md.

Environments

1、This repository only supports running on ubuntu(verison>=18.04 LTS).

2、This repository only support one node one gpu/one node multi gpus mode with pytorch DDP training.

3、Please make sure your Python environment version>=3.7.

4、Please make sure your pytorch version>=1.10.

5、If you want to use torch.complie() function,please make sure your pytorch version>=2.0.Using pytorch2.0/2.2/2.3,don't use pytorch2.1.

Use pip or conda to install those Packages in your Python environment:

torch
torchvision
pillow
numpy
Cython
colormath
pycocotools
opencv-python
scipy
einops
scikit-image
pyclipper
shapely
imagesize
nltk
tqdm
onnx
onnx-simplifier
thop==0.0.31.post2005241907
gradio==4.26.0
yapf

If you want to use dino-detr model,install MultiScaleDeformableAttention Packge in your Python environment:

cd to simpleAICV/detection/compile_multiscale_deformable_attention,then run commands:

chmod +x make.sh
./make.sh

Download my pretrained models and experiments records

You can download all my pretrained models and all my experiments records/checkpoints from huggingface or Baidu-Netdisk.

If you only want to download all my pretrained models(model.state_dict()),you can download pretrained_models folder.

# huggingface
https://huggingface.co/zgcr654321/classification_training/tree/main
https://huggingface.co/zgcr654321/contrastive_learning_training/tree/main
https://huggingface.co/zgcr654321/detection_training/tree/main
https://huggingface.co/zgcr654321/image_inpainting_training/tree/main
https://huggingface.co/zgcr654321/diffusion_model_training/tree/main
https://huggingface.co/zgcr654321/distillation_training/tree/main
https://huggingface.co/zgcr654321/instance_segmentation_training/tree/main
https://huggingface.co/zgcr654321/masked_image_modeling_training/tree/main
https://huggingface.co/zgcr654321/ocr_text_detection_training/tree/main
https://huggingface.co/zgcr654321/ocr_text_recognition_training/tree/main
https://huggingface.co/zgcr654321/human_matting_training/tree/main
https://huggingface.co/zgcr654321/salient_object_detection_training/tree/main
https://huggingface.co/zgcr654321/interactive_segmentation_training/tree/main
https://huggingface.co/zgcr654321/semantic_segmentation_training/tree/main
https://huggingface.co/zgcr654321/pretrained_models/tree/main

# Baidu-Netdisk
链接：https://pan.baidu.com/s/1yhEwaZhrb2NZRpJ5eEqHBw 
提取码：rgdo

Prepare datasets

CIFAR10

Make sure the folder architecture as follows:

CIFAR10
|
|-----batches.meta  unzip from cifar-10-python.tar.gz
|-----data_batch_1  unzip from cifar-10-python.tar.gz
|-----data_batch_2  unzip from cifar-10-python.tar.gz
|-----data_batch_3  unzip from cifar-10-python.tar.gz
|-----data_batch_4  unzip from cifar-10-python.tar.gz
|-----data_batch_5  unzip from cifar-10-python.tar.gz
|-----readme.html   unzip from cifar-10-python.tar.gz
|-----test_batch    unzip from cifar-10-python.tar.gz

CIFAR100

Make sure the folder architecture as follows:

CIFAR100
|
|-----train unzip from cifar-100-python.tar.gz
|-----test  unzip from cifar-100-python.tar.gz
|-----meta  unzip from cifar-100-python.tar.gz

ImageNet 1K(ILSVRC2012)

Make sure the folder architecture as follows:

ILSVRC2012
|
|-----train----1000 sub classes folders
|-----val------1000 sub classes folders
Please make sure the same class has same class folder name in train and val folders.

ImageNet 21K(Winter 2021 release)

Make sure the folder architecture as follows:

ImageNet21K
|
|-----train-----------10450 sub classes folders
|-----val-------------10450 sub classes folders
|-----small_classes---10450 sub classes folders
|-----imagenet21k_miil_tree.pth
Please make sure the same class has same class folder name in train and val folders.

ACCV2022

Make sure the folder architecture as follows:

ACCV2022
|
|-----train-------------5000 sub classes folders
|-----testa-------------60000 images
|-----accv2022_broken_list.json

VOC2007 and VOC2012

Make sure the folder architecture as follows:

VOCdataset
|                 |----Annotations
|                 |----ImageSets
|----VOC2007------|----JPEGImages
|                 |----SegmentationClass
|                 |----SegmentationObject
|        
|                 |----Annotations
|                 |----ImageSets
|----VOC2012------|----JPEGImages
|                 |----SegmentationClass
|                 |----SegmentationObject

COCO2017

Make sure the folder architecture as follows:

COCO2017
|                |----captions_train2017.json
|                |----captions_val2017.json
|--annotations---|----instances_train2017.json
|                |----instances_val2017.json
|                |----person_keypoints_train2017.json
|                |----person_keypoints_val2017.json
|                 
|                |----train2017
|----images------|----val2017

SAMACOCO

Make sure the folder architecture as follows:

SAMA-COCO
|                |----sama_coco_train.json
|                |----sama_coco_validation.json
|--annotations---|----train_labels.json
|                |----validation_labels.json
|                |----test_labels.json
|                |----image_info_test2017.json
|                |----image_info_test-dev2017.json
|                 
|                |----train
|----images------|----validation

Objects365(v2,2020)

Make sure the folder architecture as follows:

objects365_2020
|
|                |----zhiyuan_objv2_train.json
|--annotations---|----zhiyuan_objv2_val.json
|                |----sample_2020.json
|                 
|                |----train all train patch folders
|----images------|----val   all val patch folders
                 |----test  all test patch folders

ADE20K

Make sure the folder architecture as follows:

ADE20K
|                 |----training
|---images--------|----validation
|                 |----testing
|        
|                 |----training
|---annotations---|----validation

CelebA-HQ

Make sure the folder architecture as follows:

CelebA-HQ
|                 |----female
|---train---------|----male
|        
|                 |----female
|---val-----------|----male

FFHQ

Make sure the folder architecture as follows:

FFHQ
|
|---images
|---ffhq-dataset-v1.json
|---ffhq-dataset-v2.json

Places365-standard/challenge

Make sure the folder architecture as follows:

Places365-standard/challenge
|
|                            |---train_large all sub folders
|---high_resolution_images---|---val_large   all images
|                            |---test_large  all images

How to train and test model

If you want to train or test model,you need enter a training experiment folder directory,then run train.sh or test.sh.

For example,you can enter in folder classification_training/imagenet/resnet50.

If you want to restart train this model,please delete checkpoints and log folders first,then run train.sh:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/train_classification_model.py --work-dir ./

if you want to test this model,you need have a pretrained model first,modify trained_model_path in test_config.py,then run test.sh:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --nproc_per_node=2 --master_addr 127.0.1.0 --master_port 10000 ../../../tools/test_classification_model.py --work-dir ./

CUDA_VISIBLE_DEVICES is used to specify the gpu ids for this training.Please make sure the number of nproc_per_node equal to the number of using gpu cards.Make sure master_addr/master_port are unique for each training.

All checkpoints/log are saved in your executing training/testing experiment folder directory.

Also, You can modify super parameters in train_config.py/test_config.py.

How to use gradio demo

cd to gradio_demo,we have:

classification demo
detection demo
semantic_segmentation demo
instance_segmentation demo
text_detection demo
text_recognition demo
human_matting demo
salient_object_detection demo
segment_anything demo

For example,you can run detection gradio demo(please prepare trained model weight first):

python gradio_detect_single_image.py

Citation

If you find my work useful in your research, please consider citing:

@inproceedings{zgcr,
 title={SimpleAICV-pytorch-training-examples},
 author={zgcr},
 year={2020-2024}
}