- My ZhiHu column
- Introduction
- All task training results
- Environments
- Download my pretrained models and experiments records
- Prepare datasets
- How to train and test model
- Citation
https://www.zhihu.com/column/c_1249719688055193600
This repository provides simple training and testing examples for the following tasks:
Image classification task
Object detection task
Semantic segmentation task
Knowledge distillation task
Contrastive learning task
Masked image modeling task
diffusion model task
support dataset:
CIFAR100
ImageNet1K(ILSVRC2012)
ImageNet21K(Winter 2021 release)
support network:
ResNet
DarkNet
RepVGG
RegNetX
ViT
support dataset:
VOC2007 and VOC2012
COCO2017
Objects365(v2,2020)
support network:
RetinaNet
FCOS
CenterNet
TTFNet
support dataset:
ADE20K
support network:
DeepLabv3+
U2Net
support dataset:
ImageNet1K(ILSVRC2012)
support network:
KD loss(for ResNet)
DML loss(for ResNet)
support dataset:
ImageNet1K(ILSVRC2012)
support network:
DINO(for ResNet)
support dataset:
ImageNet1K(ILSVRC2012)
support network:
MAE(for ViT)
support dataset:
CIFAR10
CIFAR100
CelebA-HQ
FFHQ
support Sampling Method:
DDPM
DDIM
PLMS
See all task training results in results.md.
1、This repository only supports running on ubuntu(verison>=18.04 LTS).
2、This repository only support one machine one gpu/one machine 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.
Use pip or conda to install those Packages in your Python environment:
torch
torchvision
pillow
numpy
Cython
pycocotools
opencv-python
scipy
eniops
tqdm
onnx
onnx-simplifier
thop==0.0.31.post2005241907
gradio==3.32.0
yapf
Install MultiScaleDeformableAttention Packge in your Python environment:
cd to SimpleAICV_pytorch_training_examples_on_ImageNet_COCO_ADE20K/simpleAICV/detection/compile_multiscale_deformable_attention,then run commands:
chmod +x make.sh
./make.sh
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/diffusion_model_training/tree/main
https://huggingface.co/zgcr654321/distillation_training/tree/main
https://huggingface.co/zgcr654321/masked_image_modeling_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
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
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
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.
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.
Make sure the folder architecture as follows:
VOCdataset
| |----Annotations
| |----ImageSets
|----VOC2007------|----JPEGImages
| |----SegmentationClass
| |----SegmentationObject
|
| |----Annotations
| |----ImageSets
|----VOC2012------|----JPEGImages
| |----SegmentationClass
| |----SegmentationObject
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
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
Make sure the folder architecture as follows:
ADE20K
| |----training
|---images--------|----validation
| |----testing
|
| |----training
|---annotations---|----validation
Make sure the folder architecture as follows:
CelebA-HQ
| |----female
|---train---------|----male
|
| |----female
|---val-----------|----male
Make sure the folder architecture as follows:
FFHQ
|
|---images
|---ffhq-dataset-v1.json
|---ffhq-dataset-v2.json
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 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 training/testing experiment folder directory.
Also, You can modify super parameters in train_config.py/test_config.py.
If you find my work useful in your research, please consider citing:
@inproceedings{zgcr,
title={SimpleAICV-pytorch-training-examples},
author={zgcr},
year={2023}
}