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    "info": {
        "author": "Ruslan Baikulov",
        "author_email": "ruslan1123@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
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        "description": "# Argus \n\nArgus is easy-to-use flexible library for training neural networks in PyTorch.\n\n## Installation\n\nFrom pip:\n\n```bash\npip install pytorch-argus\n```\n\nFrom source:\n\n```bash\ngit clone https://github.com/lRomul/argus\ncd argus\npython setup.py install\n```\n\n## Examples\n\nSimple image classification example:\n\n```python\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom mnist_utils import get_data_loaders\n\nfrom argus import Model, load_model\nfrom argus.callbacks import MonitorCheckpoint, EarlyStopping, ReduceLROnPlateau\n\n\nclass Net(nn.Module):\n    def __init__(self, n_classes, p_dropout=0.5):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d(p=p_dropout)\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, n_classes)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return x\n\n\nclass MnistModel(Model):\n    nn_module = Net\n    optimizer = torch.optim.SGD\n    loss = torch.nn.CrossEntropyLoss\n\n\nif __name__ == \"__main__\":\n    train_loader, val_loader = get_data_loaders()\n\n    params = {\n        'nn_module': {'n_classes': 10, 'p_dropout': 0.1},\n        'optimizer': {'lr': 0.01},\n        'device': 'cpu'\n    }\n\n    model = MnistModel(params)\n\n    callbacks = [\n        MonitorCheckpoint(dir_path='mnist', monitor='val_accuracy', max_saves=3),\n        EarlyStopping(monitor='val_accuracy', patience=9),\n        ReduceLROnPlateau(monitor='val_accuracy', factor=0.5, patience=3)\n    ]\n\n    model.fit(train_loader,\n              val_loader=val_loader,\n              max_epochs=50,\n              metrics=['accuracy'],\n              callbacks=callbacks,\n              metrics_on_train=True)\n\n    del model\n    model = load_model('mnist/model-last.pth')\n```\n\nUse Argus with `make_model` from [pytorch-cnn-finetune](https://github.com/creafz/pytorch-cnn-finetune).\n\n```python\nfrom cnn_finetune import make_model\nfrom argus import Model\n\nclass CnnFinetune(Model):\n    nn_module = make_model\n\n\nparams = {\n    'nn_module': {\n        'model_name': 'resnet18',\n        'num_classes': 10,\n        'pretrained': False,\n        'input_size': (256, 256)\n    },\n    'optimizer': ('Adam', {'lr': 0.01}),\n    'loss': 'CrossEntropyLoss',\n    'device': 'cuda'\n}\n\nmodel = CnnFinetune(params)\n```\n\nYou can find other examples [here](examples). \n\n## Kaggle solutions  \n\n1. 1st place solution for Freesound Audio Tagging 2019 (mel-spectrograms, mixed precision training with Apex)  \n[https://github.com/lRomul/argus-freesound](https://github.com/lRomul/argus-freesound/blob/master/src/argus_models.py)\n2. 14th place solution for TGS Salt Identification Challenge (segmentation, MeanTeacher)  \n[https://github.com/lRomul/argus-tgs-salt](https://github.com/lRomul/argus-tgs-salt/blob/master/src/argus_models.py)\n3. 50th place solution for Quick, Draw! Doodle Recognition Challenge (gradient accumulation, training on 50M images)   \n[https://github.com/lRomul/argus-quick-draw](https://github.com/lRomul/argus-quick-draw/blob/master/src/argus_models.py)\n4. 66th place solution for Kaggle Airbus Ship Detection Challenge (instance segmentation)  \n[https://github.com/OniroAI/Universal-segmentation-baseline-Kaggle-Airbus-Ship-Detection](https://github.com/OniroAI/Universal-segmentation-baseline-Kaggle-Airbus-Ship-Detection)\n5. Solution for Humpback Whale Identification (metric learning: arcface, center loss)  \n[https://github.com/lRomul/argus-humpback-whale](https://github.com/lRomul/argus-humpback-whale/blob/master/src/argus_models.py)\n6. Solution for VSB Power Line Fault Detection (1d conv)  \n[https://github.com/lRomul/argus-vsb-power](https://github.com/lRomul/argus-vsb-power/blob/master/src/argus_models.py)\n\n\n",
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