Torch. hub

Torch.hub

torch.hub is a very powerful tool with a wide range of available models. With the help of AI tools, testing and learning of the following models can be completed quickly. Practical testing and development are the only ways to gain useful knowledge, and AI tools greatly accelerate the efficiency of testing and development. Efficiency has no limits, but time is limited. If one only focuses on time-saving and is not willing to learn and try, they may end up not being able to use the tool and learn effectively, let alone repeatedly test and improve it.

torch.hub.list('pytorch/vision')

['alexnet',
 'convnext_base',
 'convnext_large',
 'convnext_small',
 'convnext_tiny',
 'deeplabv3_mobilenet_v3_large',
 'deeplabv3_resnet101',
 'deeplabv3_resnet50',
 'densenet121',
 'densenet161',
 'densenet169',
 'densenet201',
 'efficientnet_b0',
 'efficientnet_b1',
 'efficientnet_b2',
 'efficientnet_b3',
 'efficientnet_b4',
 'efficientnet_b5',
 'efficientnet_b6',
 'efficientnet_b7',
 'efficientnet_v2_l',
 'efficientnet_v2_m',
 'efficientnet_v2_s',
 'fcn_resnet101',
 'fcn_resnet50',
 'get_model_weights',
 'get_weight',
 'googlenet',
 'inception_v3',
 'lraspp_mobilenet_v3_large',
 'maxvit_t',
 'mc3_18',
 'mnasnet0_5',
 'mnasnet0_75',
 'mnasnet1_0',
 'mnasnet1_3',
 'mobilenet_v2',
 'mobilenet_v3_large',
 'mobilenet_v3_small',
 'mvit_v1_b',
 'mvit_v2_s',
 'r2plus1d_18',
 'r3d_18',
 'raft_large',
 'raft_small',
 'regnet_x_16gf',
 'regnet_x_1_6gf',
 'regnet_x_32gf',
 'regnet_x_3_2gf',
 'regnet_x_400mf',
 'regnet_x_800mf',
 'regnet_x_8gf',
 'regnet_y_128gf',
 'regnet_y_16gf',
 'regnet_y_1_6gf',
 'regnet_y_32gf',
 'regnet_y_3_2gf',
 'regnet_y_400mf',
 'regnet_y_800mf',
 'regnet_y_8gf',
 'resnet101',
 'resnet152',
 'resnet18',
 'resnet34',
 'resnet50',
 'resnext101_32x8d',
 'resnext101_64x4d',
 'resnext50_32x4d',
 's3d',
 'shufflenet_v2_x0_5',
 'shufflenet_v2_x1_0',
 'shufflenet_v2_x1_5',
 'shufflenet_v2_x2_0',
 'squeezenet1_0',
 'squeezenet1_1',
 'swin3d_b',
 'swin3d_s',
 'swin3d_t',
 'swin_b',
 'swin_s',
 'swin_t',
 'swin_v2_b',
 'swin_v2_s',
 'swin_v2_t',
 'vgg11',
 'vgg11_bn',
 'vgg13',
 'vgg13_bn',
 'vgg16',
 'vgg16_bn',
 'vgg19',
 'vgg19_bn',
 'vit_b_16',
 'vit_b_32',
 'vit_h_14',
 'vit_l_16',
 'vit_l_32',
 'wide_resnet101_2',
 'wide_resnet50_2']

I have tested all the YOLOv5 modls with torch.hub one the objective project, is it petty easy to load and use a model in torch. hub

Here are some of the typical tasks that can be completed by different deep learning models in torch.hub:

1. AlexNet: used for image classification tasks and was one of the first models to win the ImageNet Large Scale Visual Recognition Challenge.
2. VGG: also used for image classification tasks and has a very deep network structure with many convolution and pooling layers.
3. GoogleNet / Inception: also used for image classification tasks and has a special structure that uses an "Inception module" to reduce parameters and calculation.
4. ResNet: used for image classification tasks and has a very deep network structure that uses residual blocks to solve the problem of vanishing gradients in deep neural networks.
5. DenseNet: also used for image classification tasks and uses dense connections to enhance feature reuse and improve accuracy.
6. MobileNet: used for image classification, object detection, and segmentation tasks. This model is lightweight and can adapt to low-resource environments such as mobile devices.
7. EfficientNet: also used for image classification, object detection, and segmentation tasks. This model is efficient and uses Compound Scaling to balance the depth, width, and resolution of the model.
8. Transformer: used for natural language processing tasks such as machine translation, text generation, and keyword extraction. This model uses attention mechanisms to better understand long-range dependencies.
9. Swin Transformer: a transformer model for natural language processing tasks that has higher efficiency and accuracy.
10. S3D / R(2+1)D / R(3+1)D: used for action recognition tasks. These models use spatial and temporal convolutions to better extract features from dynamic images.
11. FCN / Deeplab: used for image segmentation tasks such as medical image segmentation and grassland segmentation. These models use fully convolutional neural networks to classify each pixel and generate a corresponding segmentation image.

In summary, these are just a few of the typical tasks that can be completed by deep learning models. The actual applications and variations of these models are constantly expanding, and they help us better deal with complex data and problems.