Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.
| Date | Stars |
|---|---|
| 2026-08-04 | 1270 |
| 2026-08-06 | 1271 |
| 2026-08-11 | 1270 |
| 2026-08-13 | 1270 |
| 2026-08-14 | 1270 |
| 2026-08-16 | 1269 |
| 2026-08-18 | 1269 |
| 2026-08-22 | 1269 |
| 2026-09-09 | 1270 |
| 2026-09-27 | 1269 |
| 2026-10-05 | 1268 |
| 2026-10-06 | 1269 |
| 2026-10-14 | 1268 |
| 2026-10-24 | 1267 |
| 2026-10-25 | 1268 |
| 2026-10-31 | 1269 |
| 2026-11-04 | 1269 |
Today
— stars today
This week
+1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.08%/day
# Torch-template-for-deep-learning
Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms **.
### Requirements
· torch, torch-vision
· torchsummary
· other necessary
### usage
A training script is supplied in “train_baseline.py”, the arguments are in “args.py
<img src="https://github.com/ZhugeKongan/torch-template-for-deep-learning/blob/main/results/training.png" width=80% />
### autoaug: Data enhancement and CNNs regularization
- StochDepth
- label smoothing
- Cutout
- DropBlock
- Mixup
- Manifold Mixup
- ShakeDrop
- cutmix
### dataset_loader: Loaders for various datasets
```python
from dataloder.scoliosis_dataloder import ScoliosisDataset
from dataloder.facial_attraction_dataloder import FacialAttractionDataset
from dataloder.fa_and_sco_dataloder import ScoandFaDataset
from dataloder.scofaNshot_dataloder import ScoandFaNshotDataset
from dataloder.age_dataloder import MegaAsiaAgeDataset
def load_dataset(data_config):
if data_config.dataset == 'cifar10':
training_transform=training_transforms()
if data_config.autoaug:
print('auto Augmentation the data !')
training_transform.transforms.insert(0, Augmentation(fa_reduced_cifar10()))
train_dataset = torchvision.datasets.CIFAR10(root=data_config.data_path,
train=True,
transform=training_transform,
download=True)
val_dataset = torchvision.datasets.CIFAR10(root=data_config.data_path,
train=False,
transform=validation_transforms(),
download=True)
return train_dataset,val_dataset
elif data_config.dataset == 'cifar100':
train_dataset = torchvision.datasets.CIFAR100(root=data_config.data_path,
train=True,
transform=training_transforms(),
download=True)
val_dataset = torchvision.datasets.CIFAR100(root=data_config.data_path,
train=False,
transform=validation_transforms(),
download=True)
return train_dataset, val_dataset
```
### deployment: Deployment mode of pytorch model
Two deployment modes of pytorch model are provided, one is web deployment and the other is C + + deployment
Store the training weight file in ` flash_ Deployment ` folder
Then modify ' server.py ' path
Leverage ' client.Py ' call
### models: Various classical deep learning models
##### Classical network
- **AlexNet**
- **VGG**
- **ResNet**
- **ResNext**
- **InceptionV1**
- **InceptionV2 and InceptionV3**
- **InceptionV4 and Inception-ResNet**
- **GoogleNet**
- **EfficienNet**
- **MNasNet**
- **DPN**
##### Attention network
- **SE Attention**
- **External Attention**
- **Self Attention**
- **SK Attention**
- **CBAM Attention**
- **BAM Attention**
- **ECA Attention**
- **DANet Attention**
- **Pyramid Split Attention(PSA)**
- **EMSA Attention**
- **A2Attention**
- **Non-Local Attention**
- **CoAtNet**
- **CoordAttention**
- **HaloAttention**
- **MobileViTAttention**
- **MUSEAttention**
- **OutlookAttention**
- **ParNetAttention**
- **ParallelPolarizedSelfAttention**
- **residual_attention**
- **S2Attention**
- **SpatialGroupEnhance Attention**
- **ShuffleAttention**
- **GFNet Attention**
- **TripletAttention**
- *Excerpt of 7,974 characters
Read on GitHub
39
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9e5bd7dd9431b0f6, llm:description: 'Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.' language: Python
matched fp:9e5bd7dd9431b0f6, llm:description: 'Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.' language: Python
matched fp:9e5bd7dd9431b0f6, llm:description: 'Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.' language: Python