372-deep-clustering-for-unsupervised-learning-of-visual-features

https://github.com/szu-advtech-2023/372-deep-clustering-for-unsupervised-learning-of-visual-features

Repository

https://github.com/SZU-AdvTech-2023/372-Deep-Clustering-for-Unsupervised-Learning-of-Visual-Features/blob/main/

# 
caron2018BaselineBackbone
\(compute_features(dataloader, model, N) \)


 \(clustering.py\)   \(clustering.py\)
  \(clustering0.py\)
  \(clustering.py\)   \(faiss\) 


	

 \(faiss\)
	
  \(clustering.py\) 



	




## 
caron2018deep
- Python  2.7
- SciPy  scikit-learn 
- PyTorch  0.1.8 (pytorch.org)
- CUDA 8.0
- Faiss 
- ImageNet 

## 

\(main_promoted2.1.sh\)

ImageNet
ImageNet
\(eval_linear.sh\)

## 

- UAD
- UAD
- UAD


## 
:
```
$ ./main.sh
```

```
DIR=/datasets01/imagenet_full_size/061417/train
```
AlexNet`ARCH=alexnet`VGG-16`ARCH=vgg16`

checkpoint
```
EXP=exp
```
n`--checkpoints``${EXP}/checkpoints/checkpoint_0.pth.tar`
pickle`${EXP}/clusters`

`main.py``main_promoted2.0.py``mainpromoted2.1.py`
```
usage: main.py [-h] [--arch ARCH] [--sobel] [--clustering {Kmeans,PIC}]
               [--nmb_cluster NMB_CLUSTER] [--lr LR] [--wd WD]
               [--reassign REASSIGN] [--workers WORKERS] [--epochs EPOCHS]
               [--start_epoch START_EPOCH] [--batch BATCH]
               [--momentum MOMENTUM] [--resume PATH]
               [--checkpoints CHECKPOINTS] [--seed SEED] [--exp EXP]
               [--verbose]
               DIR

PyTorch Implementation of DeepCluster

positional arguments:
  DIR                   path to dataset

optional arguments:
  -h, --help            show this help message and exit
  --arch ARCH, -a ARCH  CNN architecture (default: alexnet)
  --sobel               Sobel filtering
  --clustering {Kmeans,PIC}
                        clustering algorithm (default: Kmeans)
  --nmb_cluster NMB_CLUSTER, --k NMB_CLUSTER
                        number of cluster for k-means (default: 10000)
  --lr LR               learning rate (default: 0.05)
  --wd WD               weight decay pow (default: -5)
  --reassign REASSIGN   how many epochs of training between two consecutive
                        reassignments of clusters (default: 1)
  --workers WORKERS     number of data loading workers (default: 4)
  --epochs EPOCHS       number of total epochs to run (default: 200)
  --start_epoch START_EPOCH
                        manual epoch number (useful on restarts) (default: 0)
  --batch BATCH         mini-batch size (default: 256)
  --momentum MOMENTUM   momentum (default: 0.9)
  --resume PATH         path to checkpoint (default: None)
  --checkpoints CHECKPOINTS
                        how many iterations between two checkpoints (default:
                        25000)
  --seed SEED           random seed (default: 31)
  --exp EXP             path to exp folder
  --verbose             chatty
```

## 

```
$ ./eval_linear.sh
```
ImageNetPlaces:
```
DATA=/datasets01/imagenet_full_size/061417/
```
:
```
MODEL=/private/home/mathilde/deepcluster/checkpoint.pth.tar
```

```
CONV=3
```
checkpoint
```
EXP=exp
```

:
```
usage: eval_linear.py [-h] [--data DATA] [--model MODEL] [--conv {1,2,3,4,5}]
                      [--tencrops] [--exp EXP] [--workers WORKERS]
                      [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--lr LR]
                      [--momentum MOMENTUM] [--weight_decay WEIGHT_DECAY]
                      [--seed SEED] [--verbose]

Train linear classifier on top of frozen convolutional layers of an AlexNet.

optional arguments:
  -h, --help            show this help message and exit
  --data DATA           path to dataset
  --model MODEL         path to model
  --conv {1,2,3,4,5}    on top of which convolutional layer train logistic
                        regression
  --tencrops            validation accuracy averaged over 10 crops
  --exp EXP             exp folder
  --workers WORKERS     number of data loading workers (default: 4)
  --epochs EPOCHS       number of total epochs to run (default: 90)
  --batch_size BATCH_SIZE
                        mini-batch size (default: 256)
  --lr LR               learning rate
  --momentum MOMENTUM   momentum (default: 0.9)
  --weight_decay WEIGHT_DECAY, --wd WEIGHT_DECAY
                        weight decay pow (default: -4)
  --seed SEED           random seed
  --verbose             chatty

Owner

Citation (citation.txt)

@inproceedings{REPO372,
    author = "Caron, Mathilde and Bojanowski, Piotr and Joulin, Armand and Douze, Matthijs",
    booktitle = "Proceedings of the European conference on computer vision (ECCV)",
    pages = "132--149",
    title = "{Deep Clustering for Unsupervised Learning of Visual Features}",
    year = "2018"
}

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