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resnext model (#55)
* added instagram pretrains for resnext encoders * added resnext info into readme
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‎README.md‎

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@@ -60,23 +60,25 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
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#### Encoders <a name="encoders"></a>
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| Type | Encoder names |
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|------------|-----------------------------------------------------------------|
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| VGG | vgg11, vgg13, vgg16, vgg19, vgg11bn, vgg13bn, vgg16bn, vgg19bn |
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| DenseNet | densenet121, densenet169, densenet201, densenet161 |
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| DPN | dpn68, dpn68b, dpn92, dpn98, dpn107, dpn131 |
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| Inception | inceptionresnetv2 |
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| ResNet | resnet18, resnet34, resnet50, resnet101, resnet152 |
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| SE-ResNet | se_resnet50, se_resnet101, se_resnet152 |
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| SE-ResNeXt | se_resnext50_32x4d, se_resnext101_32x4d |
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| SENet | senet154 | |
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| Type | Encoder names |
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|------------|---------------------------------------------------------------------------------------------|
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| VGG | vgg11, vgg13, vgg16, vgg19, vgg11bn, vgg13bn, vgg16bn, vgg19bn |
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| DenseNet | densenet121, densenet169, densenet201, densenet161 |
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| DPN | dpn68, dpn68b, dpn92, dpn98, dpn107, dpn131 |
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| Inception | inceptionresnetv2 |
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| ResNet | resnet18, resnet34, resnet50, resnet101, resnet152 |
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| ResNeXt | resnext50_32x4d, resnext101_32x8d, resnext101_32x16d, resnext101_32x32d, resnext101_32x48d |
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| SE-ResNet | se_resnet50, se_resnet101, se_resnet152 |
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| SE-ResNeXt | se_resnext50_32x4d, se_resnext101_32x4d |
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| SENet | senet154 |
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#### Weights <a name="weights"></a>
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| Weights name | Encoder names |
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|--------------|-----------------------|
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| imagenet+5k | dpn68b, dpn92, dpn107 |
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| imagenet | * all other encoders |
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| Weights name | Encoder names |
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|---------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| imagenet+5k | dpn68b, dpn92, dpn107 |
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| imagenet | vgg11, vgg13, vgg16, vgg19, vgg11bn, vgg13bn, vgg16bn, vgg19bn, <br> densenet121, densenet169, densenet201, densenet161, dpn68, dpn98, dpn131, <br> inceptionresnetv2, <br> resnet18, resnet34, resnet50, resnet101, resnet152, <br> resnext50_32x4d, resnext101_32x8d, <br> se_resnet50, se_resnet101, se_resnet152, <br> se_resnext50_32x4d, se_resnext101_32x4d, <br> senet154 |
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| [instagram](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/) | resnext101_32x8d, resnext101_32x16d, resnext101_32x32d, resnext101_32x48d |
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### Models API <a name="api"></a>
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- `model.encoder` - pretrained backbone to extract features of different spatial resolution

‎segmentation_models_pytorch/encoders/resnet.py‎

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'layers': [3, 8, 36, 3],
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},
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},
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'resnext50_32x4d': {
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'encoder': ResNetEncoder,
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'pretrained_settings': {
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'imagenet': {
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'url': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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},
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'out_shapes': (2048, 1024, 512, 256, 64),
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'params': {
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'block': Bottleneck,
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'layers': [3, 4, 6, 3],
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'groups': 32,
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'width_per_group': 4
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},
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},
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'resnext101_32x8d': {
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'encoder': ResNetEncoder,
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'pretrained_settings': {
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'imagenet': {
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'url': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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},
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'instagram': {
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'url': 'https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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},
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'out_shapes': (2048, 1024, 512, 256, 64),
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'params': {
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'block': Bottleneck,
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'layers': [3, 4, 23, 3],
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'groups': 32,
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'width_per_group': 8
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},
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},
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'resnext101_32x16d': {
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'encoder': ResNetEncoder,
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'pretrained_settings': {
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'instagram':{
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'url': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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},
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'out_shapes': (2048, 1024, 512, 256, 64),
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'params': {
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'block': Bottleneck,
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'layers': [3, 4, 23, 3],
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'groups': 32,
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'width_per_group': 16
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},
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},
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'resnext101_32x32d': {
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'encoder': ResNetEncoder,
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'pretrained_settings': {
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'instagram': {
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'url': 'https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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},
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'out_shapes': (2048, 1024, 512, 256, 64),
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'params': {
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'block': Bottleneck,
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'layers': [3, 4, 23, 3],
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'groups': 32,
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'width_per_group': 32
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},
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},
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'resnext101_32x48d': {
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'encoder': ResNetEncoder,
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'pretrained_settings': {
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'instagram': {
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'url': 'https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth',
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'input_space': 'RGB',
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'input_size': [3, 224, 224],
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'input_range': [0, 1],
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225],
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'num_classes': 1000
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}
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},
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'out_shapes': (2048, 1024, 512, 256, 64),
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'params': {
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'block': Bottleneck,
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'layers': [3, 4, 23, 3],
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'groups': 32,
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'width_per_group': 48
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},
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},
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}

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