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Add new pretrained models (#257)
* add pretrained models
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‎README.md‎

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@@ -74,14 +74,15 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
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|Encoder |Weights |Params, M |
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|--------------------------------|:------------------------------:|:------------------------------:|
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|resnet18 |imagenet |11M |
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|resnet18 |imagenet<br>ssl*<br>swsl* |11M |
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|resnet34 |imagenet |21M |
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|resnet50 |imagenet |23M |
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|resnet50 |imagenet<br>ssl*<br>swsl* |23M |
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|resnet101 |imagenet |42M |
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|resnet152 |imagenet |58M |
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|resnext50_32x4d |imagenet |22M |
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|resnext101_32x8d |imagenet<br>instagram |86M |
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|resnext101_32x16d |instagram |191M |
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|resnext50_32x4d |imagenet<br>ssl*<br>swsl* |22M |
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|resnext101_32x4d |ssl<br>swsl |42M |
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|resnext101_32x8d |imagenet<br>instagram<br>ssl*<br>swsl*|86M |
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|resnext101_32x16d |instagram<br>ssl*<br>swsl* |191M |
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|resnext101_32x32d |instagram |466M |
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|resnext101_32x48d |instagram |826M |
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|dpn68 |imagenet |11M |
@@ -131,6 +132,8 @@ preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
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|timm-efficientnet-b8 |imagenet<br>advprop |84M |
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|timm-efficientnet-l2 |noisy-student |474M |
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\* `ssl`, `wsl` from [here](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models).
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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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@@ -22,6 +22,7 @@
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number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
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depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
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"""
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from copy import deepcopy
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import torch.nn as nn
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@@ -69,6 +70,59 @@ def load_state_dict(self, state_dict, **kwargs):
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super().load_state_dict(state_dict, **kwargs)
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new_settings = {
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"resnet18": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth"
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},
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"resnet50": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth"
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},
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"resnext50_32x4d": {
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"imagenet": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth",
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},
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"resnext101_32x4d": {
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth"
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},
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"resnext101_32x8d": {
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"imagenet": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth",
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},
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"resnext101_32x16d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth",
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"ssl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth",
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"swsl": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth",
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},
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"resnext101_32x32d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth",
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},
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"resnext101_32x48d": {
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"instagram": "https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth",
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}
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}
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pretrained_settings = deepcopy(pretrained_settings)
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for model_name, sources in new_settings.items():
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if model_name not in pretrained_settings:
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pretrained_settings[model_name] = {}
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for source_name, source_url in sources.items():
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pretrained_settings[model_name][source_name] = {
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"url": source_url,
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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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resnet_encoders = {
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"resnet18": {
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"encoder": ResNetEncoder,
@@ -117,17 +171,7 @@ def load_state_dict(self, state_dict, **kwargs):
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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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"pretrained_settings": pretrained_settings["resnext50_32x4d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
@@ -136,28 +180,20 @@ def load_state_dict(self, state_dict, **kwargs):
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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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"resnext101_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/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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"pretrained_settings": pretrained_settings["resnext101_32x4d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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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": 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": pretrained_settings["resnext101_32x8d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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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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"pretrained_settings": pretrained_settings["resnext101_32x16d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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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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"pretrained_settings": pretrained_settings["resnext101_32x32d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,
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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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"pretrained_settings": pretrained_settings["resnext101_32x48d"],
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"params": {
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"out_channels": (3, 64, 256, 512, 1024, 2048),
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"block": Bottleneck,

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