diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index 87d0df80..6fe01c66 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -25,7 +25,7 @@ jobs:
       run: |
         python --version
         echo "import tests.actions_test" > test.py
-        coverage run --source models,examples test.py
+        coverage run --source facenet_pytorch/models,examples tests/actions_test.py
         coverage report
         coverage xml
     - name: Upload coverage to Codecov
diff --git a/README.md b/README.md
index 44090393..9eb1471f 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
-# Face Recognition Using Pytorch 
+# Face Recognition Using Pytorch
 
 *You can also read a translated version of this file [in Chinese 简体中文版](README_cn.md).*
 
+![banner](static/facenet-pytorch-banner.png)
+
 [![Downloads](https://pepy.tech/badge/facenet-pytorch)](https://pepy.tech/project/facenet-pytorch)
 
 [![Code Coverage](https://img.shields.io/codecov/c/github/timesler/facenet-pytorch.svg)](https://codecov.io/gh/timesler/facenet-pytorch)
@@ -18,12 +20,12 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
 * [Quick start](#quick-start)
 * [Pretrained models](#pretrained-models)
 * [Example notebooks](#example-notebooks)
-  + [*Complete detection and recognition pipeline*](#complete-detection-and-recognition-pipeline)
-  + [*Face tracking in video streams*](#face-tracking-in-video-streams)
-  + [*Finetuning pretrained models with new data*](#finetuning-pretrained-models-with-new-data)
-  + [*Guide to MTCNN in facenet-pytorch*](#guide-to-mtcnn-in-facenet-pytorch)
-  + [*Performance comparison of face detection packages*](#performance-comparison-of-face-detection-packages)
-  + [*The FastMTCNN algorithm*](#the-fastmtcnn-algorithm)
+  * [*Complete detection and recognition pipeline*](#complete-detection-and-recognition-pipeline)
+  * [*Face tracking in video streams*](#face-tracking-in-video-streams)
+  * [*Finetuning pretrained models with new data*](#finetuning-pretrained-models-with-new-data)
+  * [*Guide to MTCNN in facenet-pytorch*](#guide-to-mtcnn-in-facenet-pytorch)
+  * [*Performance comparison of face detection packages*](#performance-comparison-of-face-detection-packages)
+  * [*The FastMTCNN algorithm*](#the-fastmtcnn-algorithm)
 * [Running with docker](#running-with-docker)
 * [Use this repo in your own git project](#use-this-repo-in-your-own-git-project)
 * [Conversion of parameters from Tensorflow to Pytorch](#conversion-of-parameters-from-tensorflow-to-pytorch)
@@ -32,7 +34,7 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
 ## Quick start
 
 1. Install:
-    
+
     ```bash
     # With pip:
     pip install facenet-pytorch
@@ -43,10 +45,10 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
     # or use a docker container (see https://github.com/timesler/docker-jupyter-dl-gpu):
     docker run -it --rm timesler/jupyter-dl-gpu pip install facenet-pytorch && ipython
     ```
-    
+
 1. In python, import facenet-pytorch and instantiate models:
-    
-    ```python
+
+    ```py
     from facenet_pytorch import MTCNN, InceptionResnetV1
     
     # If required, create a face detection pipeline using MTCNN:
@@ -55,10 +57,10 @@ Also included in this repo is an efficient pytorch implementation of MTCNN for f
     # Create an inception resnet (in eval mode):
     resnet = InceptionResnetV1(pretrained='vggface2').eval()
     ```
-    
+
 1. Process an image:
-    
-    ```python
+
+    ```py
     from PIL import Image
     
     img = Image.open(<image path>)
@@ -78,7 +80,7 @@ See `help(MTCNN)` and `help(InceptionResnetV1)` for usage and implementation det
 
 ## Pretrained models
 
-See: [models/inception_resnet_v1.py](models/inception_resnet_v1.py)
+See: [models/inception_resnet_v1.py](facenet_pytorch/models/inception_resnet_v1.py)
 
 The following models have been ported to pytorch (with links to download pytorch state_dict's):
 
@@ -89,7 +91,7 @@ The following models have been ported to pytorch (with links to download pytorch
 
 There is no need to manually download the pretrained state_dict's; they are downloaded automatically on model instantiation and cached for future use in the torch cache. To use an Inception Resnet (V1) model for facial recognition/identification in pytorch, use:
 
-```python
+```py
 from facenet_pytorch import InceptionResnetV1
 
 # For a model pretrained on VGGFace2
@@ -119,7 +121,7 @@ Face recognition can be easily applied to raw images by first detecting faces us
 
 MTCNN can be used to build a face tracking system (using the `MTCNN.detect()` method). A full face tracking example can be found at [examples/face_tracking.ipynb](examples/face_tracking.ipynb).
 
-![](examples/tracked.gif)
+![Tracking](examples/tracked.gif)
 
 ### *Finetuning pretrained models with new data*
 
@@ -156,7 +158,7 @@ Each package is tested for its speed in detecting the faces in a set of 300 imag
 |dlib|3.80|8.39|14.53|
 |mtcnn|3.04|5.70|8.23|
 
-![](examples/performance-comparison.png)
+![Performance comparison](examples/performance-comparison.png)
 
 See the [notebook on kaggle](https://www.kaggle.com/timesler/comparison-of-face-detection-packages).
 
@@ -193,9 +195,9 @@ Alternatively, the code can be installed as a package using pip:
 
 ## Conversion of parameters from Tensorflow to Pytorch
 
-See: [models/utils/tensorflow2pytorch.py](models/tensorflow2pytorch.py)
+See: [models/utils/tensorflow2pytorch.py](facenet_pytorch/models/tensorflow2pytorch.py)
 
-Note that this functionality is not needed to use the models in this repo, which depend only on the saved pytorch `state_dict`'s. 
+Note that this functionality is not needed to use the models in this repo, which depend only on the saved pytorch `state_dict`'s.
 
 Following instantiation of the pytorch model, each layer's weights were loaded from equivalent layers in the pretrained tensorflow models from [davidsandberg/facenet](https://github.com/davidsandberg/facenet).
 
@@ -205,7 +207,7 @@ The equivalence of the outputs from the original tensorflow models and the pytor
 
 `>>> compare_model_outputs(mdl, sess, torch.randn(5, 160, 160, 3).detach())`
 
-```
+```py
 Passing test data through TF model
 
 tensor([[-0.0142,  0.0615,  0.0057,  ...,  0.0497,  0.0375, -0.0838],
@@ -228,16 +230,16 @@ Distance 1.2874517096861382e-06
 
 ---
 
-In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo _with submodules_, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.
+In order to re-run the conversion of tensorflow parameters into the pytorch model, ensure you clone this repo *with submodules_, as the davidsandberg/facenet repo is included as a submodule and parts of it are required for the conversion.
 
 ## References
 
 1. David Sandberg's facenet repo: [https://github.com/davidsandberg/facenet](https://github.com/davidsandberg/facenet)
 
-1. F. Schroff, D. Kalenichenko, J. Philbin. _FaceNet: A Unified Embedding for Face Recognition and Clustering_, arXiv:1503.03832, 2015. [PDF](https://arxiv.org/pdf/1503.03832)
+1. F. Schroff, D. Kalenichenko, J. Philbin. *FaceNet: A Unified Embedding for Face Recognition and Clustering_, arXiv:1503.03832, 2015. [PDF](https://arxiv.org/pdf/1503.03832)
 
-1. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. _VGGFace2: A dataset for recognising face across pose and age_, International Conference on Automatic Face and Gesture Recognition, 2018. [PDF](http://www.robots.ox.ac.uk/~vgg/publications/2018/Cao18/cao18.pdf)
+1. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman. *VGGFace2: A dataset for recognising face across pose and age_, International Conference on Automatic Face and Gesture Recognition, 2018. [PDF](http://www.robots.ox.ac.uk/~vgg/publications/2018/Cao18/cao18.pdf)
 
-1. D. Yi, Z. Lei, S. Liao and S. Z. Li. _CASIAWebface: Learning Face Representation from Scratch_, arXiv:1411.7923, 2014. [PDF](https://arxiv.org/pdf/1411.7923)
+1. D. Yi, Z. Lei, S. Liao and S. Z. Li. *CASIAWebface: Learning Face Representation from Scratch_, arXiv:1411.7923, 2014. [PDF](https://arxiv.org/pdf/1411.7923)
 
-1. K. Zhang, Z. Zhang, Z. Li and Y. Qiao. _Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks_, IEEE Signal Processing Letters, 2016. [PDF](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf)
+1. K. Zhang, Z. Zhang, Z. Li and Y. Qiao. *Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks_, IEEE Signal Processing Letters, 2016. [PDF](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf)
diff --git a/README_cn.md b/README_cn.md
index b11c9eca..3ed4efe8 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -118,7 +118,7 @@ img_probs = resnet(img_cropped.unsqueeze(0))
 
 ## 预训练模型
 
-请参阅：[models/inception_resnet_v1.py](models/inception_resnet_v1.py)
+请参阅：[models/inception_resnet_v1.py](facenet_pytorch/models/inception_resnet_v1.py)
 
 以下模型已移植到 pytorch（包含下载 pytorch state_dict 的链接）：
 
@@ -233,7 +233,7 @@ docker run --rm -p 8888:8888
 
 ## Tensorflow 到 Pytorch 的参数转换
 
-请参阅：[models/utils/tensorflow2pytorch.py](models/tensorflow2pytorch.py)
+请参阅：[models/utils/tensorflow2pytorch.py](facenet_pytorch/models/tensorflow2pytorch.py)
 
 请注意，使用此存储库中的模型不需要此功能，该功能仅依赖于pytorch保存的 `state_dict`。
 
diff --git a/__init__.py b/__init__.py
deleted file mode 100644
index 7f1018f1..00000000
--- a/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import warnings
-
-from .models.inception_resnet_v1 import InceptionResnetV1
-from .models.mtcnn import MTCNN, ONet, PNet, RNet, fixed_image_standardization, prewhiten
-from .models.utils import training
-from .models.utils.detect_face import extract_face
-
-warnings.filterwarnings(
-    action="ignore",
-    message="This overload of nonzero is deprecated:\n\tnonzero()",
-    category=UserWarning,
-)
diff --git a/data/.gitignore b/data/.gitignore
deleted file mode 100644
index 832aeb8f..00000000
--- a/data/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-2018*
-*.json
-profile.txt
diff --git a/examples/.gitignore b/examples/.gitignore
new file mode 100644
index 00000000..a81f8bb4
--- /dev/null
+++ b/examples/.gitignore
@@ -0,0 +1 @@
+tmptest.py
diff --git a/examples/finetune.ipynb b/examples/finetune.ipynb
index 45566848..9a21ed4b 100644
--- a/examples/finetune.ipynb
+++ b/examples/finetune.ipynb
@@ -15,6 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
     "import torch\n",
     "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
@@ -41,7 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_dir = '../data/test_images'\n",
+    "root_dir = Path(__file__).parent.parent\n",
+    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
     "\n",
     "batch_size = 32\n",
     "epochs = 8\n",
@@ -106,10 +108,10 @@
    "source": [
     "dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))\n",
     "dataset.samples = [\n",
-    "    (p, p.replace(data_dir, data_dir + '_cropped'))\n",
+    "    (p, p.replace(str(data_dir), str(data_dir.parent / (data_dir.name + \"_aligned\"))))\n",
     "        for p, _ in dataset.samples\n",
     "]\n",
-    "        \n",
+    "\n",
     "loader = DataLoader(\n",
     "    dataset,\n",
     "    num_workers=workers,\n",
@@ -117,8 +119,8 @@
     "    collate_fn=training.collate_pil\n",
     ")\n",
     "\n",
-    "for i, (x, y) in enumerate(loader):\n",
-    "    mtcnn(x, save_path=y)\n",
+    "for i, (input_file, output_file) in enumerate(loader):\n",
+    "    mtcnn(input_file, save_path=output_file)\n",
     "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')\n",
     "    \n",
     "# Remove mtcnn to reduce GPU memory usage\n",
@@ -168,7 +170,7 @@
     "    transforms.ToTensor(),\n",
     "    fixed_image_standardization\n",
     "])\n",
-    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "dataset = datasets.ImageFolder(data_dir.parent / (data_dir.name + \"_aligned\"), transform=trans)\n",
     "img_inds = np.arange(len(dataset))\n",
     "np.random.shuffle(img_inds)\n",
     "train_inds = img_inds[:int(0.8 * len(img_inds))]\n",
diff --git a/examples/finetune_cn.ipynb b/examples/finetune_cn.ipynb
index 8190504c..f80ff183 100644
--- a/examples/finetune_cn.ipynb
+++ b/examples/finetune_cn.ipynb
@@ -15,6 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1, fixed_image_standardization, training\n",
     "import torch\n",
     "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
@@ -41,7 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_dir = '../data/test_images'\n",
+    "root_dir = Path(__file__).parent.parent\n",
+    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
     "\n",
     "batch_size = 32\n",
     "epochs = 8\n",
@@ -106,7 +108,7 @@
    "source": [
     "dataset = datasets.ImageFolder(data_dir, transform=transforms.Resize((512, 512)))\n",
     "dataset.samples = [\n",
-    "    (p, p.replace(data_dir, data_dir + '_cropped'))\n",
+    "    (p, p.replace(str(data_dir), str(data_dir.parent / (data_dir.name + \"_aligned\"))))\n",
     "        for p, _ in dataset.samples\n",
     "]\n",
     "        \n",
@@ -168,7 +170,7 @@
     "    transforms.ToTensor(),\n",
     "    fixed_image_standardization\n",
     "])\n",
-    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "dataset = datasets.ImageFolder(data_dir + '_aligned', transform=trans)\n",
     "img_inds = np.arange(len(dataset))\n",
     "np.random.shuffle(img_inds)\n",
     "train_inds = img_inds[:int(0.8 * len(img_inds))]\n",
diff --git a/examples/infer.ipynb b/examples/infer.ipynb
index d36a056f..3218656c 100644
--- a/examples/infer.ipynb
+++ b/examples/infer.ipynb
@@ -20,13 +20,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import os\n",
     "\n",
     "workers = 0 if os.name == 'nt' else 4"
    ]
@@ -118,7 +119,10 @@
     "def collate_fn(x):\n",
     "    return x[0]\n",
     "\n",
-    "dataset = datasets.ImageFolder('../data/test_images')\n",
+    "root_dir = Path(__file__).parent.parent\n",
+    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
+    "\n",
+    "dataset = datasets.ImageFolder(data_dir)\n",
     "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
     "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
    ]
@@ -242,4 +246,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/infer_cn.ipynb b/examples/infer_cn.ipynb
index 114f0d07..cf69650a 100644
--- a/examples/infer_cn.ipynb
+++ b/examples/infer_cn.ipynb
@@ -26,13 +26,14 @@
    },
    "outputs": [],
    "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "\n",
     "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
     "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "from torchvision import datasets\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "import os\n",
     "\n",
     "workers = 0 if os.name == 'nt' else 4"
    ]
@@ -144,7 +145,10 @@
     "def collate_fn(x):\n",
     "    return x[0]\n",
     "\n",
-    "dataset = datasets.ImageFolder('../data/test_images')\n",
+    "root_dir = Path(__file__).parent.parent\n",
+    "data_dir = root_dir / \"tests\" / \"data\" / \"test_images\"\n",
+    "\n",
+    "dataset = datasets.ImageFolder(data_dir)\n",
     "dataset.idx_to_class = {i:c for c, i in dataset.class_to_idx.items()}\n",
     "loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=workers)"
    ]
diff --git a/examples/lfw_evaluate.ipynb b/examples/lfw_evaluate.ipynb
index 7cde66e6..ef1a1fdc 100644
--- a/examples/lfw_evaluate.ipynb
+++ b/examples/lfw_evaluate.ipynb
@@ -141,7 +141,7 @@
     "box_probs = []\n",
     "\n",
     "for i, (x, b_paths) in enumerate(loader):\n",
-    "    crops = [p.replace(data_dir, data_dir + '_cropped') for p in b_paths]\n",
+    "    crops = [p.replace(str(data_dir), str(data_dir.parent / (data_dir.name + \"_aligned\"))) for p in b_paths]\n",
     "    mtcnn(x, save_path=crops)\n",
     "    crop_paths.extend(crops)\n",
     "    print('\\rBatch {} of {}'.format(i + 1, len(loader)), end='')"
@@ -176,7 +176,7 @@
     "    fixed_image_standardization\n",
     "])\n",
     "\n",
-    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "dataset = datasets.ImageFolder(data_dir.parent / data_dir.name + \"_aligned\", transform=trans)\n",
     "\n",
     "embed_loader = DataLoader(\n",
     "    dataset,\n",
@@ -452,7 +452,7 @@
    "outputs": [],
    "source": [
     "pairs = read_pairs(pairs_path)\n",
-    "path_list, issame_list = get_paths(data_dir+'_cropped', pairs)\n",
+    "path_list, issame_list = get_paths(data_dir+'_aligned', pairs)\n",
     "embeddings = np.array([embeddings_dict[path] for path in path_list])\n",
     "\n",
     "tpr, fpr, accuracy, val, val_std, far, fp, fn = evaluate(embeddings, issame_list)"
@@ -519,4 +519,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/examples/lfw_evaluate_cn.ipynb b/examples/lfw_evaluate_cn.ipynb
index ff73624f..9096b4f7 100644
--- a/examples/lfw_evaluate_cn.ipynb
+++ b/examples/lfw_evaluate_cn.ipynb
@@ -146,7 +146,7 @@
     "box_probs = []\n",
     "\n",
     "for i, (x, b_paths) in enumerate(loader):\n",
-    "    crops = [p.replace(data_dir, data_dir + '_cropped') for p in b_paths]\n",
+    "    crops = [p.replace(str(data_dir), str(data_dir.parent / (data_dir.name + \"_aligned\"))) for p in b_paths]\n",
     "    mtcnn(x, save_path=crops)\n",
     "    crop_paths.extend(crops)\n",
     "    print('\\r第 {} 批，共 {} 批'.format(i + 1, len(loader)), end='')"
@@ -185,7 +185,7 @@
     "    fixed_image_standardization\n",
     "])\n",
     "\n",
-    "dataset = datasets.ImageFolder(data_dir + '_cropped', transform=trans)\n",
+    "dataset = datasets.ImageFolder(data_dir.parent / data_dir.name + \"_aligned\", transform=trans)\n",
     "\n",
     "embed_loader = DataLoader(\n",
     "    dataset,\n",
@@ -454,7 +454,7 @@
    "outputs": [],
    "source": [
     "pairs = read_pairs(pairs_path)\n",
-    "path_list, issame_list = get_paths(data_dir+'_cropped', pairs)\n",
+    "path_list, issame_list = get_paths(data_dir+'_aligned', pairs)\n",
     "embeddings = np.array([embeddings_dict[path] for path in path_list])\n",
     "\n",
     "tpr, fpr, accuracy, val, val_std, far, fp, fn = evaluate(embeddings, issame_list)"
@@ -490,8 +490,7 @@
    ],
    "source": [
     "print(accuracy)\n",
-    "np.mean(accuracy)\n",
-    "\n"
+    "np.mean(accuracy)"
    ]
   }
  ],
diff --git a/facenet_pytorch/__init__.py b/facenet_pytorch/__init__.py
new file mode 100644
index 00000000..f11d3287
--- /dev/null
+++ b/facenet_pytorch/__init__.py
@@ -0,0 +1,14 @@
+import warnings
+
+from facenet_pytorch.models.inception_resnet_v1 import InceptionResnetV1
+from facenet_pytorch.models.mtcnn import MTCNN, ONet, PNet, RNet, fixed_image_standardization, prewhiten
+from facenet_pytorch.models.utils import training
+from facenet_pytorch.models.utils.detect_face import extract_face
+
+__all__ = ["InceptionResnetV1", "MTCNN", "ONet", "PNet", "RNet", "fixed_image_standardization", "prewhiten", "training", "extract_face"]
+
+warnings.filterwarnings(
+    action="ignore",
+    message="This overload of nonzero is deprecated:\n\tnonzero()",
+    category=UserWarning,
+)
diff --git a/data/onet.pt b/facenet_pytorch/data/onet.pt
similarity index 100%
rename from data/onet.pt
rename to facenet_pytorch/data/onet.pt
diff --git a/data/pnet.pt b/facenet_pytorch/data/pnet.pt
similarity index 100%
rename from data/pnet.pt
rename to facenet_pytorch/data/pnet.pt
diff --git a/data/rnet.pt b/facenet_pytorch/data/rnet.pt
similarity index 100%
rename from data/rnet.pt
rename to facenet_pytorch/data/rnet.pt
diff --git a/models/inception_resnet_v1.py b/facenet_pytorch/models/inception_resnet_v1.py
similarity index 100%
rename from models/inception_resnet_v1.py
rename to facenet_pytorch/models/inception_resnet_v1.py
diff --git a/models/mtcnn.py b/facenet_pytorch/models/mtcnn.py
similarity index 98%
rename from models/mtcnn.py
rename to facenet_pytorch/models/mtcnn.py
index 7eb64550..202943d0 100644
--- a/models/mtcnn.py
+++ b/facenet_pytorch/models/mtcnn.py
@@ -1,10 +1,12 @@
 import os
+import importlib.resources
 
 import numpy as np
 import torch
 from torch import nn
 
-from .utils.detect_face import detect_face, extract_face
+from facenet_pytorch.models.utils.detect_face import detect_face, extract_face
+import facenet_pytorch.data
 
 
 class PNet(nn.Module):
@@ -31,7 +33,7 @@ def __init__(self, pretrained=True):
         self.training = False
 
         if pretrained:
-            state_dict_path = os.path.join(os.path.dirname(__file__), "../data/pnet.pt")
+            state_dict_path = importlib.resources.path(facenet_pytorch.data, "pnet.pt")
             state_dict = torch.load(state_dict_path)
             self.load_state_dict(state_dict)
 
@@ -76,7 +78,7 @@ def __init__(self, pretrained=True):
         self.training = False
 
         if pretrained:
-            state_dict_path = os.path.join(os.path.dirname(__file__), "../data/rnet.pt")
+            state_dict_path = importlib.resources.path(facenet_pytorch.data, "rnet.pt")
             state_dict = torch.load(state_dict_path)
             self.load_state_dict(state_dict)
 
@@ -129,7 +131,7 @@ def __init__(self, pretrained=True):
         self.training = False
 
         if pretrained:
-            state_dict_path = os.path.join(os.path.dirname(__file__), "../data/onet.pt")
+            state_dict_path = importlib.resources.path(facenet_pytorch.data, "onet.pt")
             state_dict = torch.load(state_dict_path)
             self.load_state_dict(state_dict)
 
diff --git a/models/utils/detect_face.py b/facenet_pytorch/models/utils/detect_face.py
similarity index 100%
rename from models/utils/detect_face.py
rename to facenet_pytorch/models/utils/detect_face.py
diff --git a/models/utils/download.py b/facenet_pytorch/models/utils/download.py
similarity index 100%
rename from models/utils/download.py
rename to facenet_pytorch/models/utils/download.py
diff --git a/models/utils/tensorflow2pytorch.py b/facenet_pytorch/models/utils/tensorflow2pytorch.py
similarity index 99%
rename from models/utils/tensorflow2pytorch.py
rename to facenet_pytorch/models/utils/tensorflow2pytorch.py
index 7972b74b..a55e2634 100644
--- a/models/utils/tensorflow2pytorch.py
+++ b/facenet_pytorch/models/utils/tensorflow2pytorch.py
@@ -1,14 +1,12 @@
 import json
 import os
-import sys
 
 import tensorflow as tf
 import torch
 
 from dependencies.facenet.src import facenet
 from dependencies.facenet.src.align import detect_face
-from dependencies.facenet.src.models import inception_resnet_v1 as tf_mdl
-from models.inception_resnet_v1 import InceptionResnetV1
+from facenet_pytorch.models.inception_resnet_v1 import InceptionResnetV1
 from models.mtcnn import ONet, PNet, RNet
 
 
diff --git a/models/utils/training.py b/facenet_pytorch/models/utils/training.py
similarity index 100%
rename from models/utils/training.py
rename to facenet_pytorch/models/utils/training.py
diff --git a/setup.py b/setup.py
index 3c096e97..b7086667 100644
--- a/setup.py
+++ b/setup.py
@@ -30,7 +30,6 @@
         "facenet_pytorch.models.utils",
         "facenet_pytorch.data",
     ],
-    package_dir={"facenet_pytorch": "."},
     package_data={"": ["*net.pt"]},
     classifiers=[
         "Programming Language :: Python :: 3",
diff --git a/data/facenet-pytorch-banner.png b/static/facenet-pytorch-banner.png
similarity index 100%
rename from data/facenet-pytorch-banner.png
rename to static/facenet-pytorch-banner.png
diff --git a/tests/actions_test.py b/tests/actions_test.py
index 307673f9..ce575228 100644
--- a/tests/actions_test.py
+++ b/tests/actions_test.py
@@ -1,10 +1,12 @@
 """
-The following code is intended to be run only by Github actions for continuius intengration and
-testing purposes. For implementation examples see notebooks in the examples folder.
+The following code is intended to be run only by GitHub actions for continuous integration and
+testing purposes.
+For implementation examples, see notebooks in the examples folder.
 """
 
 import glob
 import os
+from pathlib import Path
 import sys
 from time import time
 
@@ -12,11 +14,10 @@
 import pandas as pd
 import torch
 from PIL import Image, ImageDraw
-from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
 
-from models.inception_resnet_v1 import InceptionResnetV1, get_torch_home
-from models.mtcnn import MTCNN, fixed_image_standardization
+from facenet_pytorch.models.inception_resnet_v1 import InceptionResnetV1, get_torch_home
+from facenet_pytorch.models.mtcnn import MTCNN, fixed_image_standardization
 
 #### CLEAR ALL OUTPUT FILES ####
 
@@ -32,19 +33,17 @@
 
 
 #### TEST EXAMPLE IPYNB'S ####
+test_dir = Path(__file__).parent
+root_dir = test_dir.parent
+example_dir = root_dir / "examples"
 
-os.system("jupyter nbconvert --to script --stdout examples/infer.ipynb examples/finetune.ipynb > examples/tmptest.py")
-os.chdir("examples")
-try:
-    import examples.tmptest
-except:
-    import tmptest
-os.chdir("..")
+os.system(f"jupyter nbconvert --to python --stdout {example_dir}/infer.ipynb {example_dir}/finetune.ipynb > {example_dir}/tmptest.py")
 
-
-#### TEST MTCNN ####
+exec((example_dir / "tmptest.py").open().read())
+os.chdir(test_dir)
 
 
+#### TEST MTCNN ####
 def get_image(path, trans):
     img = Image.open(path)
     img = trans(img)
@@ -146,10 +145,9 @@ def get_image(path, trans):
         assert total_error_fromfile < 1e-2
 
 
-#### TEST CLASSIFICATION ####
-
-resnet_pt = InceptionResnetV1(pretrained=ds, classify=True).eval()
-prob = resnet_pt(aligned)
+    #### TEST CLASSIFICATION ####
+    resnet_pt = InceptionResnetV1(pretrained=ds, classify=True).eval()
+    prob = resnet_pt(aligned)
 
 
 #### MULTI-FACE TEST ####
diff --git a/data/multiface.jpg b/tests/data/multiface.jpg
similarity index 100%
rename from data/multiface.jpg
rename to tests/data/multiface.jpg
diff --git a/data/multiface_detected.png b/tests/data/multiface_detected.png
similarity index 100%
rename from data/multiface_detected.png
rename to tests/data/multiface_detected.png
diff --git a/data/test_images/angelina_jolie/1.jpg b/tests/data/test_images/angelina_jolie/1.jpg
similarity index 100%
rename from data/test_images/angelina_jolie/1.jpg
rename to tests/data/test_images/angelina_jolie/1.jpg
diff --git a/data/test_images/bradley_cooper/1.jpg b/tests/data/test_images/bradley_cooper/1.jpg
similarity index 100%
rename from data/test_images/bradley_cooper/1.jpg
rename to tests/data/test_images/bradley_cooper/1.jpg
diff --git a/data/test_images/kate_siegel/1.jpg b/tests/data/test_images/kate_siegel/1.jpg
similarity index 100%
rename from data/test_images/kate_siegel/1.jpg
rename to tests/data/test_images/kate_siegel/1.jpg
diff --git a/data/test_images/paul_rudd/1.jpg b/tests/data/test_images/paul_rudd/1.jpg
similarity index 100%
rename from data/test_images/paul_rudd/1.jpg
rename to tests/data/test_images/paul_rudd/1.jpg
diff --git a/data/test_images/shea_whigham/1.jpg b/tests/data/test_images/shea_whigham/1.jpg
similarity index 100%
rename from data/test_images/shea_whigham/1.jpg
rename to tests/data/test_images/shea_whigham/1.jpg
diff --git a/data/test_images_aligned/angelina_jolie/1.png b/tests/data/test_images_aligned/angelina_jolie/1.png
similarity index 100%
rename from data/test_images_aligned/angelina_jolie/1.png
rename to tests/data/test_images_aligned/angelina_jolie/1.png
diff --git a/data/test_images_aligned/bradley_cooper/1.png b/tests/data/test_images_aligned/bradley_cooper/1.png
similarity index 100%
rename from data/test_images_aligned/bradley_cooper/1.png
rename to tests/data/test_images_aligned/bradley_cooper/1.png
diff --git a/data/test_images_aligned/kate_siegel/1.png b/tests/data/test_images_aligned/kate_siegel/1.png
similarity index 100%
rename from data/test_images_aligned/kate_siegel/1.png
rename to tests/data/test_images_aligned/kate_siegel/1.png
diff --git a/data/test_images_aligned/paul_rudd/1.png b/tests/data/test_images_aligned/paul_rudd/1.png
similarity index 100%
rename from data/test_images_aligned/paul_rudd/1.png
rename to tests/data/test_images_aligned/paul_rudd/1.png
diff --git a/data/test_images_aligned/shea_whigham/1.png b/tests/data/test_images_aligned/shea_whigham/1.png
similarity index 100%
rename from data/test_images_aligned/shea_whigham/1.png
rename to tests/data/test_images_aligned/shea_whigham/1.png