diff --git a/.dvc/.gitignore b/.dvc/.gitignore
new file mode 100644
index 00000000..528f30c7
--- /dev/null
+++ b/.dvc/.gitignore
@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache
diff --git a/.dvc/config b/.dvc/config
new file mode 100644
index 00000000..520625c6
--- /dev/null
+++ b/.dvc/config
@@ -0,0 +1,6 @@
+[core]
+    remote = frdc-ds
+    autostage = true
+['remote "frdc-ds"']
+    url = gs://frdc-ds/
+    version_aware = true
diff --git a/.dvcignore b/.dvcignore
new file mode 100644
index 00000000..51973055
--- /dev/null
+++ b/.dvcignore
@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
diff --git a/.github/workflows/python-package.yml b/.github/workflows/basic-tests.yml
similarity index 96%
rename from .github/workflows/python-package.yml
rename to .github/workflows/basic-tests.yml
index d29a0611..10cbf393 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/basic-tests.yml
@@ -38,6 +38,7 @@ jobs:
           python -m pip install flake8 pytest poetry          
           poetry export --with dev --without-hashes -o requirements.txt
           pip install -r requirements.txt
+          pip install torch torchaudio torchvision lightning
 
       - name: Lint with flake8
         run: |
diff --git a/.github/workflows/model.yml b/.github/workflows/model-tests.yml
similarity index 79%
rename from .github/workflows/model.yml
rename to .github/workflows/model-tests.yml
index 902848da..c61342aa 100644
--- a/.github/workflows/model.yml
+++ b/.github/workflows/model-tests.yml
@@ -2,6 +2,9 @@ name: Model Training
 
 on:
   pull_request:
+    branches: ['main']
+  workflow_dispatch:
+
 
 jobs:
   build:
@@ -12,7 +15,9 @@ jobs:
       volumes:
         - /home/runner/work/frdc-ml/_github_home:/root
       env:
+        # This is where setup-python will install and cache the venv
         AGENT_TOOLSDIRECTORY: "/root/venv"
+      options: --gpus all
 
     steps:
       - uses: actions/checkout@v3
@@ -34,7 +39,10 @@ jobs:
           pip3 install -r requirements.txt
           pip3 install torch torchvision torchaudio
 
-      - name: Set up gcloud
+      - name: Check CUDA is available
+        run: nvidia-smi
+
+      - name: Auth gcloud
         id: 'auth'
         uses: 'google-github-actions/auth@v1'
         with:
@@ -47,13 +55,15 @@ jobs:
         run: |
           echo "WANDB_API_KEY=${{ secrets.WANDB_API_KEY }}" >> $GITHUB_ENV
 
+      # Our project has src as a source path, explicitly add that in.
       - name: Add src as PYTHONPATH
         run: |
           echo "PYTHONPATH=src" >> $GITHUB_ENV
 
+      # Do not do cd as it'll break PYTHONPATH.
       - name: Run Model Training
         run: |
-          python3 -m tests.model_tests.chestnut_dec_may.main
+          python3 -m tests.model_tests.chestnut_dec_may.train
 
       - name: Comment results via CML
         run: |
diff --git a/.gitignore b/.gitignore
index 2c949384..4fcff576 100644
--- a/.gitignore
+++ b/.gitignore
@@ -165,4 +165,8 @@ cython_debug/
 rsc/**/*.tif
 
 **/*/lightning_logs
-*.zip
\ No newline at end of file
+*.zip
+*.sh
+*.ckpt
+/rsc
+**/wandb/
diff --git a/poetry.lock b/poetry.lock
index a3f10105..39ed7d6a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,126 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
-
-[[package]]
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-version = "3.8.6"
-description = "Async http client/server framework (asyncio)"
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-python-versions = ">=3.6"
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-aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
-
-[package.extras]
-abfs = ["adlfs"]
-adl = ["adlfs"]
-arrow = ["pyarrow (>=1)"]
-dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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-fuse = ["fusepy"]
-gcs = ["gcsfs"]
-git = ["pygit2"]
-github = ["requests"]
-gs = ["gcsfs"]
-gui = ["panel"]
-hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
-libarchive = ["libarchive-c"]
-oci = ["ocifs"]
-s3 = ["s3fs"]
-sftp = ["paramiko"]
-smb = ["smbprotocol"]
-ssh = ["paramiko"]
-tqdm = ["tqdm"]
-
 [[package]]
 name = "gitdb"
 version = "4.0.11"
@@ -846,13 +586,13 @@ tqdm = ">=4.66.1,<5.0.0"
 
 [[package]]
 name = "google-api-core"
-version = "2.12.0"
+version = "2.14.0"
 description = "Google API client core library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -868,13 +608,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.23.4"
+version = "2.24.0"
 description = "Google Authentication Library"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1046,13 +786,13 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
 
 [[package]]
 name = "identify"
-version = "2.5.31"
+version = "2.5.32"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "identify-2.5.32-py2.py3-none-any.whl", hash = "sha256:0b7656ef6cba81664b783352c73f8c24b39cf82f926f78f4550eda928e5e0545"},
+    {file = "identify-2.5.32.tar.gz", hash = "sha256:5d9979348ec1a21c768ae07e0a652924538e8bce67313a73cb0f681cf08ba407"},
 ]
 
 [package.extras]
@@ -1060,29 +800,29 @@ license = ["ukkonen"]
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
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-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
 name = "imageio"
-version = "2.32.0"
+version = "2.33.0"
 description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "imageio-2.32.0.tar.gz", hash = "sha256:e425ad36c605308d9ea6d93eda7b0987926059b8b86220e142a599a7975128dd"},
+    {file = "imageio-2.33.0-py3-none-any.whl", hash = "sha256:d580d6576d0ae39c459a444a23f6f61fe72123a3df2264f5fce8c87784a4be2e"},
+    {file = "imageio-2.33.0.tar.gz", hash = "sha256:39999d05eb500089e60be467dd7d618f56e142229b44c3961c2b420eeb538d7e"},
 ]
 
 [package.dependencies]
 numpy = "*"
-pillow = ">=8.3.2,<10.1.0"
+pillow = ">=8.3.2"
 
 [package.extras]
 all-plugins = ["astropy", "av", "imageio-ffmpeg", "pillow-heif", "psutil", "tifffile"]
@@ -1094,22 +834,22 @@ ffmpeg = ["imageio-ffmpeg", "psutil"]
 fits = ["astropy"]
 full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "pillow-heif", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
 gdal = ["gdal"]
-heif = ["pillow-heif"]
 itk = ["itk"]
 linting = ["black", "flake8"]
+pillow-heif = ["pillow-heif"]
 pyav = ["av"]
 test = ["fsspec[github]", "pytest", "pytest-cov"]
 tifffile = ["tifffile"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.1"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "importlib_resources-6.1.0.tar.gz", hash = "sha256:9d48dcccc213325e810fd723e7fbb45ccb39f6cf5c31f00cf2b965f5f10f3cb9"},
+    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
+    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
 ]
 
 [package.dependencies]
@@ -1130,23 +870,6 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
-[[package]]
-name = "jinja2"
-version = "3.1.2"
-description = "A very fast and expressive template engine."
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
-]
-
-[package.dependencies]
-MarkupSafe = ">=2.0"
-
-[package.extras]
-i18n = ["Babel (>=2.7)"]
-
 [[package]]
 name = "joblib"
 version = "1.3.2"
@@ -1286,191 +1009,41 @@ files = [
 lint = ["pre-commit (>=3.3)"]
 test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 
-[[package]]
-name = "lightning"
-version = "2.1.0"
-description = "The Deep Learning framework to train, deploy, and ship AI products Lightning fast."
-optional = false
-python-versions = ">=3.8"
-files = [
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-    {file = "lightning-2.1.0.tar.gz", hash = "sha256:1f78f5995ae7dcffa1edf34320db136902b73a0d1b304404c48ec8be165b3a93"},
-]
-
-[package.dependencies]
-fsspec = {version = ">2021.06.0,<2025.0", extras = ["http"]}
-lightning-utilities = ">=0.8.0,<2.0"
-numpy = ">=1.17.2,<3.0"
-packaging = ">=20.0,<25.0"
-pytorch-lightning = "*"
-PyYAML = ">=5.4,<8.0"
-torch = ">=1.12.0,<4.0"
-torchmetrics = ">=0.7.0,<3.0"
-tqdm = ">=4.57.0,<6.0"
-typing-extensions = ">=4.0.0,<6.0"
-
-[package.extras]
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 [[package]]
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 [[package]]
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 [[package]]
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 [package.dependencies]
@@ -2304,34 +1750,34 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
 [[package]]
 name = "seaborn"
-version = "0.12.2"
+version = "0.13.0"
 description = "Statistical data visualization"
 optional = false
-python-versions = ">=3.7"
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-    {file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
-    {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
 [[package]]
 name = "zipp"
 version = "3.17.0"
@@ -3039,4 +2245,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "bd79eca0415b2905ce1920cfd5a5fa7cf510e07e1f8eb815ca25d3cde332abc4"
+content-hash = "d0a398b3738e8aa3771631995f2eb161ac863807b20f7161852e323da11a9463"
diff --git a/pyproject.toml b/pyproject.toml
index 18a5ca73..22eaf9fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,24 +12,18 @@ python = ">=3.9,<3.12"
 numpy = "^1.25.2"
 scikit-image = "0.22.0"
 scikit-learn = "^1.3.0"
-seaborn = "^0.12.2"
+seaborn = "^0.13.0"
 matplotlib = "^3.7.3"
 google-cloud-storage = "^2.10.0"
 tqdm = "^4.66.1"
 xxhash = "^3.4.1"
 
-
-[[tool.poetry.source]]
-name = "pytorch"
-url = "https://download.pytorch.org/whl/cu121"
-priority = "explicit"
-
+#torch = {version="^2.1.0", source="pytorch"}
+#torchvision = {version="^0.16.0", source="pytorch"}
+#torchaudio = {version="^2.1.0", source="pytorch"}
+#lightning = "^2.0.9.post0"
 
 [tool.poetry.group.dev.dependencies]
-torch = "^2.1.0"
-torchvision = "^0.16.0"
-torchaudio = "^2.1.0"
-lightning = "^2.0.9.post0"
 pytest = "^7.4.2"
 pre-commit = "^3.5.0"
 black = "^23.10.0"
@@ -37,6 +31,8 @@ flake8 = "^6.1.0"
 wandb = "^0.16.0"
 
 
+
+
 [tool.poetry.group.glcm.dependencies]
 glcm-cupy = "0.2.1"
 cupy-cuda12x = "^12.2.0"
diff --git a/rsc.dvc b/rsc.dvc
new file mode 100644
index 00000000..f9ae0e31
--- /dev/null
+++ b/rsc.dvc
@@ -0,0 +1,424 @@
+outs:
+- hash: md5
+  path: rsc
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+        etag: 08ebe3a2a58de682031001
+        version_id: '1701154214425067'
+  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_NIR.tif
+    md5: ab3509fac188fe452d01957237181f31
+    size: 59034799
+    cloud:
+      frdc-ds:
+        etag: 0893bcfca48de682031001
+        version_id: '1701154213797395'
+  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_Red.tif
+    md5: 6b0531431f0d898aacda6334c10fca8a
+    size: 60369184
+    cloud:
+      frdc-ds:
+        etag: 08e8a58ea58de682031001
+        version_id: '1701154214089448'
+  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/result_RedEdge.tif
+    md5: 75e67bc7c1b01e060c68062354192b8e
+    size: 59567305
+    cloud:
+      frdc-ds:
+        etag: 08f0dc91ad8de682031001
+        version_id: '1701154230922864'
+  - relpath: chestnut_nature_park/20210510/90deg60m84.5pct255deg/segment.tif
+    md5: 2b5856c859b44e54517b7891fd53bd60
+    size: 12784
+    cloud:
+      frdc-ds:
+        etag: 08838e869c8de682031001
+        version_id: '1701154195080963'
+  - relpath: chestnut_nature_park/20210510/Join/adding -90deg 60m data/cloud.las
+    md5: a4755ad17e0c0f2686a1fb9cacc712b7
+    size: 74248398
+    cloud:
+      frdc-ds:
+        etag: 0892daa1a48de682031001
+        version_id: '1701154212310290'
+  - relpath: chestnut_nature_park/20210510/Join/cloud.las
+    md5: 8ce9bccccab90a8a5c17ad5b3a982f0d
+    size: 61187376
+    cloud:
+      frdc-ds:
+        etag: 08fda5a8a58de682031001
+        version_id: '1701154214515453'
diff --git a/rsc/DEBUG/0/scratch.py b/rsc/DEBUG/0/scratch.py
deleted file mode 100644
index 007997e3..00000000
--- a/rsc/DEBUG/0/scratch.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import numpy as np
-from PIL import Image
-
-# %%
-for i in (
-    "result.tif",
-    "result_Red.tif",
-    "result_Green.tif",
-    "result_Blue.tif",
-    "result_RedEdge.tif",
-    "result_NIR.tif",
-):
-    im = Image.open(i)
-    ar = np.array(im)
-    ar_crop = ar[2000:5000:20, 1000:3000:20]
-    im = Image.fromarray(ar_crop)
-    im.save(i)
diff --git a/rsc/README.md b/rsc/README.md
deleted file mode 100644
index 62b530f2..00000000
--- a/rsc/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
-# FRDC Resources
-
-We have 2 type of resources:
-
-1) Raw: Uncompressed, raw data from our UAV drones
-2) Debug: Compressed version of the raw data used for
-   1) Unit Testing
-   2) Integration Testing
-   3) Experimentation Debugging
-
-You shouldn't need to touch this, this will be used to cache large raw `tif` files.
diff --git a/scripts/gen_result_jpg.py b/scripts/gen_result_jpg.py
new file mode 100644
index 00000000..06d05d6c
--- /dev/null
+++ b/scripts/gen_result_jpg.py
@@ -0,0 +1,22 @@
+"""This script traverses a directory and generates a jpg for every file
+matched."""
+from pathlib import Path
+
+from PIL import Image
+from tqdm import tqdm
+
+
+def main(
+    d: Path = Path(__file__).parents[1] / "rsc", glob: str = "**/result.tif"
+):
+    for fp in tqdm(d.glob(glob)):
+        if (fp_jpg := fp.with_suffix(".jpg")).exists():
+            print(f"Skipping {fp_jpg}")
+            continue
+
+        print(f"Generating {fp_jpg}")
+        Image.open(fp).convert("RGB").save(fp_jpg)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/frdc/load/dataset.py b/src/frdc/load/dataset.py
index 5ba8ba8f..65752bce 100644
--- a/src/frdc/load/dataset.py
+++ b/src/frdc/load/dataset.py
@@ -6,13 +6,21 @@
 from collections import OrderedDict
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Iterable
+from typing import Iterable, Callable, Any
 
 import numpy as np
 import pandas as pd
+import torch
 from PIL import Image
 from google.cloud import storage
 from google.oauth2.service_account import Credentials
+from torch.utils.data import Dataset, ConcatDataset
+from torchvision.transforms.v2 import (
+    Compose,
+    ToImage,
+    ToDtype,
+    Resize,
+)
 
 from frdc.conf import (
     LOCAL_DATASET_ROOT_DIR,
@@ -20,6 +28,7 @@
     GCS_BUCKET_NAME,
     BAND_CONFIG,
 )
+from frdc.preprocess.extract_segments import extract_segments_from_bounds
 from frdc.utils import Rect
 
 
@@ -147,21 +156,68 @@ def download_file(
 
 
 @dataclass
-class FRDCDataset:
-    site: str
-    date: str
-    version: str | None
-    dl: FRDCDownloader = field(default_factory=FRDCDownloader)
+class FRDCDataset(Dataset):
+    def __init__(
+        self,
+        site: str,
+        date: str,
+        version: str | None,
+        transform: Callable[[list[np.ndarray]], Any] = None,
+        target_transform: Callable[[list[str]], list[str]] = None,
+    ):
+        """Initializes the FRDC Dataset.
+
+        Args:
+            site: The site of the dataset, e.g. "chestnut_nature_park".
+            date: The date of the dataset, e.g. "20201218".
+            version: The version of the dataset, e.g. "183deg".
+        """
+        self.site = site
+        self.date = date
+        self.version = version
+
+        self.dl = FRDCDownloader()
+
+        self.ar, self.order = self.get_ar_bands()
+        bounds, self.targets = self.get_bounds_and_labels()
+        self.ar_segments = extract_segments_from_bounds(self.ar, bounds)
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __len__(self):
+        return len(self.ar_segments)
+
+    def __getitem__(self, idx):
+        return (
+            self.transform(self.ar_segments[idx])
+            if self.transform
+            else self.ar_segments[idx],
+            self.target_transform(self.targets[idx])
+            if self.target_transform
+            else self.targets[idx],
+        )
 
     @staticmethod
-    def _load_debug_dataset() -> FRDCDataset:
+    def _load_debug_dataset(resize: int = 299) -> FRDCDataset:
         """Loads a debug dataset from Google Cloud Storage.
 
         Returns:
             A dictionary of the dataset, with keys as the filenames and values
             as the images.
         """
-        return FRDCDataset(site="DEBUG", date="0", version=None)
+        return FRDCDataset(
+            site="DEBUG",
+            date="0",
+            version=None,
+            transform=Compose(
+                [
+                    ToImage(),
+                    ToDtype(torch.float32),
+                    Resize((resize, resize)),
+                ]
+            ),
+            target_transform=None,
+        )
 
     @property
     def dataset_dir(self):
@@ -287,5 +343,36 @@ def _load_image(path: Path | str) -> np.ndarray:
         """
 
         im = Image.open(Path(path).as_posix())
-        ar = np.array(im)
+        ar = np.asarray(im)
         return np.expand_dims(ar, axis=-1) if ar.ndim == 2 else ar
+
+
+# TODO: Kind of hacky, the unlabelled dataset should somehow come from the
+#       labelled dataset by filtering out the unknown labels. But we'll
+#       figure out this later when we do get unlabelled data.
+#       I'm thinking some API that's like
+#       FRDCDataset.filter_labels(...) -> FRDCSubset, FRDCSubset
+#       It could be more intuitive if it returns FRDCDataset, so we don't have
+#       to implement another class.
+class FRDCUnlabelledDataset(FRDCDataset):
+    def __getitem__(self, item):
+        return (
+            self.transform(self.ar_segments[item])
+            if self.transform
+            else self.ar_segments[item]
+        )
+
+
+# This is not yet used much as we don't have sufficient training data.
+class FRDCConcatDataset(ConcatDataset):
+    def __init__(self, datasets: list[FRDCDataset]):
+        super().__init__(datasets)
+        self.datasets = datasets
+
+    def __getitem__(self, idx):
+        x, y = super().__getitem__(idx)
+        return x, y
+
+    @property
+    def targets(self):
+        return [t for ds in self.datasets for t in ds.targets]
diff --git a/src/frdc/models/__init__.py b/src/frdc/models/__init__.py
index 5420b607..e69de29b 100644
--- a/src/frdc/models/__init__.py
+++ b/src/frdc/models/__init__.py
@@ -1,5 +0,0 @@
-from .facenet import FaceNet
-
-__all__ = [
-    "FaceNet",
-]
diff --git a/src/frdc/models/facenet.py b/src/frdc/models/inceptionv3.py
similarity index 56%
rename from src/frdc/models/facenet.py
rename to src/frdc/models/inceptionv3.py
index 3d875e45..c2dce7f6 100644
--- a/src/frdc/models/facenet.py
+++ b/src/frdc/models/inceptionv3.py
@@ -1,19 +1,33 @@
+from copy import deepcopy
+
 import torch
+from sklearn.preprocessing import OrdinalEncoder, StandardScaler
 from torch import nn
 from torchvision.models import Inception_V3_Weights, inception_v3
 
+from frdc.train.mixmatch_module import MixMatchModule
+from frdc.utils.ema import EMA
+
 
-class FaceNet(nn.Module):
+class InceptionV3MixMatchModule(MixMatchModule):
     INCEPTION_OUT_DIMS = 2048
     INCEPTION_AUX_DIMS = 1000
     INCEPTION_IN_CHANNELS = 3
     MIN_SIZE = 299
 
-    def __init__(self, n_out_classes: int = 10):
-        """Initialize the FaceNet model.
+    def __init__(
+        self,
+        *,
+        n_classes: int,
+        lr: float,
+        x_scaler: StandardScaler,
+        y_encoder: OrdinalEncoder,
+        ema_lr: float = 0.001,
+    ):
+        """Initialize the InceptionV3 model.
 
         Args:
-            n_out_classes: The number of output classes
+            n_classes: The number of output classes
 
         Notes:
             - Min input size: 299 x 299.
@@ -21,7 +35,15 @@ def __init__(self, n_out_classes: int = 10):
 
             Retrieve these constants in class attributes MIN_SIZE and CHANNELS.
         """
-        super().__init__()
+        self.lr = lr
+
+        super().__init__(
+            n_classes=n_classes,
+            x_scaler=x_scaler,
+            y_encoder=y_encoder,
+            sharpen_temp=0.5,
+            mix_beta_alpha=0.75,
+        )
 
         self.inception = inception_v3(
             weights=Inception_V3_Weights.IMAGENET1K_V1,
@@ -32,14 +54,28 @@ def __init__(self, n_out_classes: int = 10):
         for param in self.inception.parameters():
             param.requires_grad = False
 
-        # self.fc = nn.Linear(self.INCEPTION_OUT_DIMS, n_out_classes)
         self.fc = nn.Sequential(
             nn.BatchNorm1d(self.INCEPTION_OUT_DIMS),
             nn.Linear(self.INCEPTION_OUT_DIMS, self.INCEPTION_OUT_DIMS // 2),
             nn.BatchNorm1d(self.INCEPTION_OUT_DIMS // 2),
-            nn.Linear(self.INCEPTION_OUT_DIMS // 2, n_out_classes),
+            nn.Linear(self.INCEPTION_OUT_DIMS // 2, n_classes),
             nn.Softmax(dim=1),
         )
+        # The problem is that the deep copy runs even before the module is
+        # initialized, which means ema_model is empty.
+        ema_model = deepcopy(self)
+        for param in ema_model.parameters():
+            param.detach_()
+        self._ema_model = ema_model
+        self.ema_updater = EMA(model=self, ema_model=self.ema_model)
+        self.ema_lr = ema_lr
+
+    @property
+    def ema_model(self):
+        return self._ema_model
+
+    def update_ema(self):
+        self.ema_updater.update(self.ema_lr)
 
     def forward(self, x: torch.Tensor):
         """Forward pass.
@@ -62,7 +98,6 @@ def forward(self, x: torch.Tensor):
                 f" - No singleton dimensions\n"
                 f" - Size >= {self.MIN_SIZE}\n"
             )
-        # x = self.feature_extraction(x)
 
         # During training, the auxiliary outputs are used for auxiliary loss,
         # but during testing, only the main output is used.
@@ -72,3 +107,9 @@ def forward(self, x: torch.Tensor):
             logits = self.inception(x)
 
         return self.fc(logits)
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(
+            self.parameters(),
+            lr=self.lr,
+        )
diff --git a/src/frdc/train/__init__.py b/src/frdc/train/__init__.py
index 8942e255..8b137891 100644
--- a/src/frdc/train/__init__.py
+++ b/src/frdc/train/__init__.py
@@ -1,5 +1 @@
-from .frdc_datamodule import FRDCDataModule
-from .frdc_module import FRDCModule
-from .train import dummy_train
 
-__all__ = ["dummy_train", "FRDCDataModule", "FRDCModule"]
diff --git a/src/frdc/train/frdc_datamodule.py b/src/frdc/train/frdc_datamodule.py
index 28e79665..6138c7e5 100644
--- a/src/frdc/train/frdc_datamodule.py
+++ b/src/frdc/train/frdc_datamodule.py
@@ -1,139 +1,105 @@
 from __future__ import annotations
 
-from dataclasses import dataclass, field
-from typing import Callable, Collection
+from dataclasses import dataclass
 
-import numpy as np
-import torch
 from lightning import LightningDataModule
-from sklearn.preprocessing import LabelEncoder
-from torch.utils.data import DataLoader, TensorDataset, Dataset
+from torch.utils.data import DataLoader, RandomSampler
 
+from frdc.load import FRDCDataset
 
-@dataclass  # (kw_only=True) # only available when we use Py3.10
+
+@dataclass
 class FRDCDataModule(LightningDataModule):
-    """FRDC Data Module.
+    """Lightning DataModule for FRDC Dataset.
 
     Notes:
-        We separate the segment input and transform as we expect the input
-        to be as "raw" as possible, which is usually in the np.ndarray
-        format.
-
-        This makes it easy if we have custom images with alternative
-        segmentations, such as through an auto-segmentation.
+        This is a special datamodule for semi-supervised learning, which
+        requires two dataloaders for the labelled and unlabelled datasets.
+        It can also be used for supervised learning, by passing in None for
+        the unlabelled dataset.
+
+        If you're using our MixMatch Module, using None for the unlabelled
+        dataset will skip the MixMatch. However, note that this is not
+        equivalent to passing the Labelled set as unlabelled as well.
+
+        For example:
+        >>> FRDCSSLDataModule(
+        ...     train_lab_ds=train_lab_ds,
+        ...     train_unl_ds=train_lab_ds,
+        ...     ...
+        ... )
+
+        Does not have the same performance as:
+        >>> FRDCSSLDataModule(
+        ...     train_lab_ds=train_lab_ds,
+        ...     train_unl_ds=None,
+        ...     ...
+        ... )
+
+        As partially, some samples in MixMatch uses the unlabelled loss.
 
     Args:
-        segments: A list of segments, each ND-Array.
-            Note that PyTorch expects input shapes of (B, C, H, W),
-             while common image libs expect shapes (H, W, C).
-        labels: A list of labels as strings. If None, then the datamodule
-            will not have train, val, test datasets.
-        preprocess: Transform applied to the segments.
-            It takes a list of segments, returning a batched tensor:
-             (batch, height, width, channels).
-            In FRDC, each segment is the tree crown segment, the output
-             should be a batched image of all the crown segments.
-            See Examples for more details.
-        train_val_test_split: This is a function that takes a TensorDataset
-            and splits it into train, val, test Datasets.
-            See Examples for more details.
+        train_lab_ds: The labelled training dataset.
+        train_unl_ds: The unlabelled training dataset. Can be None, which will
+            default to a DataModule suitable for supervised learning.
+        val_ds: The validation dataset.
         batch_size: The batch size to use for the dataloaders.
-
-    Examples:
-        The fn_segment_tf could be a function that resizes the segments to
-        299x299 and then stacks them into a batched image. The output is
-        then permuted to be (batch, channels, height, width).:
-
-        >>> from skimage.transform import resize
-        >>> from frdc.models import FaceNet
-        >>>
-        >>> fn_segment_tf=lambda x: torch.stack([
-        >>>     torch.from_numpy(resize(s, FaceNet.MIN_SIZE)) for s in x
-        >>> ]).permute(0, 3, 1, 2)
-
-        The fn_split could be a function that splits the dataset into
-        train, val, test subsets.:
-
-        >>> from torch.utils.data import random_split
-        >>>
-        >>> fn_split=lambda x: random_split(x, lengths=[len(x) - 6, 3, 3])
+        train_iters: The number of iterations to run for the labelled training
+            dataset.
+        val_iters: The number of iterations to run for the validation dataset.
 
     """
 
-    segments: list[np.ndarray]
-    preprocess: Callable[[list[np.ndarray]], torch.Tensor]
-    augmentation: Callable[[torch.Tensor], torch.Tensor] = lambda x: x
-    labels: list[str] | None = None
-    train_val_test_split: (
-        Callable[[TensorDataset], Collection[Dataset, Dataset, Dataset]] | None
-    ) = (None,)
+    train_lab_ds: FRDCDataset
+    val_ds: FRDCDataset
+    train_unl_ds: FRDCDataset | None = None
     batch_size: int = 4
-    le: LabelEncoder = LabelEncoder()
-
-    train_ds: Dataset = field(init=False, default=None)
-    val_ds: Dataset = field(init=False, default=None)
-    test_ds: Dataset = field(init=False, default=None)
-    predict_ds: Dataset = field(init=False, default=None)
+    train_iters: int = 100
+    val_iters: int = 100
 
     def __post_init__(self):
         super().__init__()
 
-    def setup(self, stage=None):
-        x = self.preprocess(self.segments)
-
-        assert torch.isnan(x).sum() == 0, "Found NaN values in the segments."
-        assert x.ndim == 4, (
-            f"Expected 4 dimensions, got {x.ndim} dimensions of shape"
-            f" {x.shape}."
-        )
-
-        if stage in ["fit", "validate", "test"]:
-            if self.labels is None or self.train_val_test_split is None:
-                raise ValueError(
-                    "Labels and fn_split must be provided for"
-                    " train, val, test datasets."
-                )
-
-            y = torch.from_numpy(self.le.fit_transform(self.labels))
-            assert x.shape[0] == y.shape[0], (
-                f"Expected same number of samples for x and y, got"
-                f" {x.shape[0]} for x and {y.shape[0]} for y."
-            )
-
-            tds = TensorDataset(x, y)
-            (
-                self.train_ds,
-                self.val_ds,
-                self.test_ds,
-            ) = self.train_val_test_split(tds)
-
-        elif stage == "predict":
-            tds = TensorDataset(x)
-            self.predict_ds = tds
-
-    def on_before_batch_transfer(self, batch, dataloader_idx: int):
-        if self.trainer.training:
-            x, y = batch
-            x = self.augmentation(x)
-            batch = x, y
-        return batch
-
     def train_dataloader(self):
-        return DataLoader(
-            self.train_ds, batch_size=self.batch_size, shuffle=True
+        num_samples = self.batch_size * self.train_iters
+        lab_dl = DataLoader(
+            self.train_lab_ds,
+            batch_size=self.batch_size,
+            sampler=RandomSampler(
+                self.train_lab_ds,
+                num_samples=num_samples,
+                replacement=False,
+            ),
         )
-
-    def val_dataloader(self):
-        return DataLoader(
-            self.val_ds, batch_size=self.batch_size, shuffle=False
+        unl_dl = (
+            DataLoader(
+                self.train_unl_ds,
+                batch_size=self.batch_size,
+                sampler=RandomSampler(
+                    self.train_unl_ds,
+                    num_samples=self.batch_size * self.train_iters,
+                    replacement=False,
+                ),
+            )
+            if self.train_unl_ds is not None
+            # This is a hacky way to create an empty dataloader.
+            # The size should be the same as the labelled dataloader so that
+            #  the iterator doesn't prematurely stop.
+            else DataLoader(
+                empty := [[] for _ in range(len(self.train_lab_ds))],
+                batch_size=self.batch_size,
+                sampler=RandomSampler(
+                    empty,
+                    num_samples=num_samples,
+                    replacement=False,
+                ),
+            )
         )
 
-    def test_dataloader(self):
-        return DataLoader(
-            self.test_ds, batch_size=self.batch_size, shuffle=False
-        )
+        return [lab_dl, unl_dl]
 
-    def predict_dataloader(self):
+    def val_dataloader(self):
         return DataLoader(
-            self.predict_ds, batch_size=self.batch_size, shuffle=False
+            self.val_ds,
+            batch_size=self.batch_size,
         )
diff --git a/src/frdc/train/frdc_module.py b/src/frdc/train/frdc_module.py
deleted file mode 100644
index e6d7c2a5..00000000
--- a/src/frdc/train/frdc_module.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-from lightning import LightningModule
-from torch import nn
-
-
-class FRDCModule(LightningModule):
-    def __init__(
-        self,
-        *,
-        model_cls: type[nn.Module],
-        model_kwargs: dict,
-        optim_cls: type[torch.optim.Optimizer],
-        optim_kwargs: dict,
-    ):
-        super().__init__()
-        self.save_hyperparameters()
-        self.model = model_cls(**model_kwargs)
-        self.optim = optim_cls
-        self.optim_kwargs = optim_kwargs
-
-    def forward(self, x):
-        return self.model(x)
-
-    def training_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = nn.CrossEntropyLoss()(y_hat, y)
-        self.log("loss", loss, prog_bar=True)
-        self.log(
-            "acc", (y_hat.argmax(dim=1) == y).float().mean(), prog_bar=True
-        )
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = nn.CrossEntropyLoss()(y_hat, y)
-        self.log("val_loss", loss)
-        self.log(
-            "val_acc", (y_hat.argmax(dim=1) == y).float().mean(), prog_bar=True
-        )
-        return loss
-
-    def test_step(self, batch, batch_idx):
-        x, y = batch
-        y_hat = self(x)
-        loss = nn.CrossEntropyLoss()(y_hat, y)
-        self.log("test_loss", loss)
-        self.log("test_acc", (y_hat.argmax(dim=1) == y).float().mean())
-        return loss
-
-    def predict_step(self, batch, batch_idx, dataloader_idx=None):
-        x = batch[0]
-        y_hat = self(x)
-        return y_hat
-
-    def configure_optimizers(self):
-        optim = self.optim(self.parameters(), **self.optim_kwargs)
-        scheduler = torch.optim.lr_scheduler.ExponentialLR(
-            optimizer=optim,
-            gamma=0.99,
-        )
-        return [optim], [scheduler]
diff --git a/src/frdc/train/mixmatch_module.py b/src/frdc/train/mixmatch_module.py
new file mode 100644
index 00000000..194928ad
--- /dev/null
+++ b/src/frdc/train/mixmatch_module.py
@@ -0,0 +1,312 @@
+from __future__ import annotations
+
+from abc import abstractmethod
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.nn.parallel
+import torch.nn.parallel
+from lightning import LightningModule
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder
+from torch.nn.functional import one_hot
+from torchmetrics.functional import accuracy
+
+
+class MixMatchModule(LightningModule):
+    def __init__(
+        self,
+        *,
+        x_scaler: StandardScaler,
+        y_encoder: OrdinalEncoder,
+        n_classes: int = 10,
+        sharpen_temp: float = 0.5,
+        mix_beta_alpha: float = 0.75,
+    ):
+        """PyTorch Lightning Module for MixMatch
+
+        Notes:
+            This performs MixMatch as described in the paper.
+            https://arxiv.org/abs/1905.02249
+
+            This module is designed to be used with any model, not only
+            the WideResNet model.
+
+            Furthermore, while it's possible to switch datasets, take a look
+            at how we implement the CIFAR10DataModule's DataLoaders to see
+            how to implement a new dataset.
+
+        Args:
+            n_classes: The number of classes in the dataset.
+            sharpen_temp: The temperature to use for sharpening.
+            mix_beta_alpha: The alpha to use for the beta distribution
+                when mixing.
+        """
+
+        super().__init__()
+
+        self.x_scaler = x_scaler
+        self.y_encoder = y_encoder
+        self.n_classes = n_classes
+        self.sharpen_temp = sharpen_temp
+        self.mix_beta_alpha = mix_beta_alpha
+        self.save_hyperparameters()
+
+    @property
+    @abstractmethod
+    def ema_model(self):
+        """The inherited class should return the EMA model, which it should
+        retroactively create through `deepcopy(self)`. Furthermore, the
+        training loop will automatically call `update_ema` after each batch.
+        Thus, the inherited class should implement `update_ema` to update the
+        EMA model.
+        """
+        ...
+
+    @abstractmethod
+    def update_ema(self):
+        """This method should update the EMA model, which is handled by the
+        inherited class.
+        """
+        ...
+
+    @abstractmethod
+    def forward(self, x):
+        ...
+
+    @staticmethod
+    def loss_unl_scaler(progress: float) -> float:
+        return progress * 75
+
+    @staticmethod
+    def loss_lbl(lbl_pred: torch.Tensor, lbl: torch.Tensor):
+        return F.cross_entropy(lbl_pred, lbl)
+
+    @staticmethod
+    def loss_unl(unl_pred: torch.Tensor, unl: torch.Tensor):
+        return torch.mean((torch.softmax(unl_pred, dim=1) - unl) ** 2)
+
+    @staticmethod
+    def mix_up(
+        x: torch.Tensor,
+        y: torch.Tensor,
+        alpha: float,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Mix up the data
+
+        Args:
+            x: The data to mix up.
+            y: The labels to mix up.
+            alpha: The alpha to use for the beta distribution.
+
+        Returns:
+            The mixed up data and labels.
+        """
+        ratio = np.random.beta(alpha, alpha)
+        ratio = max(ratio, 1 - ratio)
+
+        shuf_idx = torch.randperm(x.size(0))
+
+        x_mix = ratio * x + (1 - ratio) * x[shuf_idx]
+        y_mix = ratio * y + (1 - ratio) * y[shuf_idx]
+        return x_mix, y_mix
+
+    @staticmethod
+    def sharpen(y: torch.Tensor, temp: float) -> torch.Tensor:
+        """Sharpen the predictions by raising them to the power of 1 / temp
+
+        Args:
+            y: The predictions to sharpen.
+            temp: The temperature to use.
+
+        Returns:
+            The probability-normalized sharpened predictions
+        """
+        y_sharp = y ** (1 / temp)
+        # Sharpening will change the sum of the predictions.
+        y_sharp /= y_sharp.sum(dim=1, keepdim=True)
+        return y_sharp
+
+    def guess_labels(
+        self,
+        x_unls: list[torch.Tensor],
+    ) -> torch.Tensor:
+        """Guess labels from the unlabelled data"""
+        y_unls: list[torch.Tensor] = [
+            torch.softmax(self.ema_model(u), dim=1) for u in x_unls
+        ]
+        # The sum will sum the tensors in the list,
+        # it doesn't reduce the tensors
+        y_unl = sum(y_unls) / len(y_unls)
+        # noinspection PyTypeChecker
+        return y_unl
+
+    @property
+    def progress(self):
+        # Progress is a linear ramp from 0 to 1 over the course of training.
+        return (
+            self.global_step / self.trainer.num_training_batches
+        ) / self.trainer.max_epochs
+
+    def training_step(self, batch, batch_idx):
+        # Progress is a linear ramp from 0 to 1 over the course of training.
+        (x_lbl, y_lbl), x_unls = batch
+
+        y_lbl = one_hot(y_lbl.long(), num_classes=self.n_classes)
+
+        # If x_unls is Truthy, then we are using MixMatch.
+        # Otherwise, we are just using supervised learning.
+        if x_unls:
+            # This route implies that we are using SSL
+            with torch.no_grad():
+                y_unl = self.guess_labels(x_unls=x_unls)
+                y_unl = self.sharpen(y_unl, self.sharpen_temp)
+
+            x = torch.cat([x_lbl, *x_unls], dim=0)
+            y = torch.cat([y_lbl, *(y_unl,) * len(x_unls)], dim=0)
+            x_mix, y_mix = self.mix_up(x, y, self.mix_beta_alpha)
+
+            # This had interleaving, but it was removed as it's not
+            # significantly better
+            batch_size = x_lbl.shape[0]
+            y_mix_pred = self(x_mix)
+            y_mix_lbl_pred = y_mix_pred[:batch_size]
+            y_mix_unl_pred = y_mix_pred[batch_size:]
+            y_mix_lbl = y_mix[:batch_size]
+            y_mix_unl = y_mix[batch_size:]
+
+            loss_lbl = self.loss_lbl(y_mix_lbl_pred, y_mix_lbl)
+            loss_unl = self.loss_unl(y_mix_unl_pred, y_mix_unl)
+            loss_unl_scale = self.loss_unl_scaler(progress=self.progress)
+
+            loss = loss_lbl + loss_unl * loss_unl_scale
+
+            self.log("loss_unl_scale", loss_unl_scale, prog_bar=True)
+            self.log("train_loss_lbl", loss_lbl)
+            self.log("train_loss_unl", loss_unl)
+        else:
+            # This route implies that we are just using supervised learning
+            y_pred = self(x_lbl)
+            loss = self.loss_lbl(y_pred, y_lbl.float())
+
+        self.log("train_loss", loss)
+        return loss
+
+    # PyTorch Lightning doesn't automatically no_grads the EMA step.
+    # It's important to keep this to avoid a memory leak.
+    @torch.no_grad()
+    def on_after_backward(self) -> None:
+        self.update_ema()
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        y_pred = self.ema_model(x)
+        loss = F.cross_entropy(y_pred, y.long())
+
+        acc = accuracy(
+            y_pred, y, task="multiclass", num_classes=y_pred.shape[1]
+        )
+        self.log("val_loss", loss)
+        self.log("val_acc", acc, prog_bar=True)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        x, y = batch
+        y_pred = self.ema_model(x)
+        loss = F.cross_entropy(y_pred, y.long())
+
+        acc = accuracy(
+            y_pred, y, task="multiclass", num_classes=y_pred.shape[1]
+        )
+        self.log("test_loss", loss)
+        self.log("test_acc", acc, prog_bar=True)
+        return loss
+
+    def predict_step(self, batch, *args, **kwargs) -> Any:
+        x, y = batch
+        y_pred = self.ema_model(x)
+        y_true_str = self.y_encoder.inverse_transform(
+            y.cpu().numpy().reshape(-1, 1)
+        )
+        y_pred_str = self.y_encoder.inverse_transform(
+            y_pred.argmax(dim=1).cpu().numpy().reshape(-1, 1)
+        )
+        return y_true_str, y_pred_str
+
+    @torch.no_grad()
+    def on_before_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
+        """This method is called before any data transfer to the device.
+
+        We leverage this to do some preprocessing on the data.
+        Namely, we use the StandardScaler and OrdinalEncoder to transform the
+        data.
+        """
+
+        # TODO: ngl, this is pretty chunky.
+        #       It works, but it's not very pretty.
+        if self.training:
+            (x_lab, y), x_unl = batch
+            xs = [x_lab, *x_unl]
+
+            b, c, h, w = x_lab.shape
+
+            # Move Channel to the last dimension then transform
+            xs_ss: list[np.ndarray] = [
+                self.x_scaler.transform(x.permute(0, 2, 3, 1).reshape(-1, c))
+                for x in xs
+            ]
+
+            # Move Channel back to the second dimension
+            xs_: list[torch.Tensor] = [
+                torch.from_numpy(x_ss.reshape(b, h, w, c))
+                .permute(0, 3, 1, 2)
+                .float()
+                for x_ss in xs_ss
+            ]
+
+            y: tuple[str]
+            y_: torch.Tensor = torch.from_numpy(
+                self.y_encoder.transform(np.array(y).reshape(-1, 1)).squeeze()
+            )
+
+            # Ordinal Encoders can return a np.nan if the value is not in the
+            # categories. We will remove that from the batch.
+            x_ = xs_[0][~torch.isnan(y_)]
+            y_ = y_[~torch.isnan(y_)]
+
+            return (x_, y_.long()), xs_[1:]
+
+        else:
+            x, y = batch
+
+            x: torch.Tensor
+            b, c, h, w = x.shape
+
+            # Standard Scaler only accepts (n_samples, n_features),
+            # so we need to do some fancy reshaping.
+            # Note that moving dimensions then reshaping is different from just
+            # reshaping!
+            # Move Channel to the last dimension then transform
+            x_ss: np.ndarray = self.x_scaler.transform(
+                x.permute(0, 2, 3, 1).reshape(-1, c)
+            )
+
+            # Move Channel back to the second dimension
+            x_: torch.Tensor = (
+                torch.from_numpy(x_ss.reshape(b, h, w, c))
+                .permute(0, 3, 1, 2)
+                .float()
+            )
+
+            y: tuple[str]
+            y_: torch.Tensor = torch.from_numpy(
+                self.y_encoder.transform(np.array(y).reshape(-1, 1)).squeeze()
+            )
+
+            # Ordinal Encoders can return a np.nan if the value is not in the
+            # categories. We will remove that from the batch.
+            x_ = x_[~torch.isnan(y_)]
+            y_ = y_[~torch.isnan(y_)]
+
+            return x_, y_.long()
diff --git a/src/frdc/train/train.py b/src/frdc/train/train.py
deleted file mode 100644
index c3d49049..00000000
--- a/src/frdc/train/train.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from typing import Callable
-
-import numpy as np
-from sklearn.base import ClassifierMixin
-from sklearn.ensemble import RandomForestClassifier
-
-
-# We force keyword arguments to make it easier to read the function signature.
-def dummy_train(
-    *,
-    X_train: np.ndarray,
-    y_train: np.ndarray,
-    X_val: np.ndarray,
-    y_val: np.ndarray,
-) -> tuple[Callable[[np.ndarray], np.ndarray], ClassifierMixin, float]:
-    """Dummy Training function.
-
-    Notes:
-        This is obviously not final. This is just a placeholder to get the
-        pipeline working.
-
-    Args:
-        X_train: X_train is the train image numpy array of shape (N, H, W, C).
-        y_train: y_train is the train class label a numpy array of shape (N,).
-        X_val: X_val is the validation for X_train
-        y_val: y_val is the validation for y_train
-
-    Returns:
-        The feature extraction function, the classifier, and validation score.
-
-    """
-
-    # TODO: Placeholder feature extraction for an image.
-    #  We'll probably sub with a CNN later.
-    def feature_extraction(X):
-        return np.stack(
-            [
-                np.nanmean(X, axis=(1, 2, 3)),
-                np.nanstd(X, axis=(1, 2, 3)),
-            ],
-            axis=-1,
-        )
-
-    X_train = feature_extraction(X_train)
-    X_val = feature_extraction(X_val)
-
-    # TODO: This is likely the last layer of the CNN.
-    classifier = RandomForestClassifier()
-
-    # TODO: "Train" the model.
-    classifier.fit(X_train, y_train)
-
-    # TODO: "Evaluate" the model.
-    score = classifier.score(X_val, y_val)
-
-    return feature_extraction, classifier, score
diff --git a/src/frdc/utils/ema.py b/src/frdc/utils/ema.py
new file mode 100644
index 00000000..297b0b11
--- /dev/null
+++ b/src/frdc/utils/ema.py
@@ -0,0 +1,26 @@
+import torch
+import torch.nn as nn
+
+
+class EMA:
+    def __init__(
+        self,
+        model: nn.Module,
+        ema_model: nn.Module,
+    ):
+        self.model = model
+        self.ema_model = ema_model
+
+    def update(self, lr: float):
+        """Update the EMA model with the current model's parameters.
+
+        Args:
+            lr: A fraction controlling how much should the EMA learn from the
+                current model.
+        """
+        for param, ema_param in zip(
+            self.model.parameters(), self.ema_model.parameters()
+        ):
+            if ema_param.dtype == torch.float32:
+                ema_param.mul_(1 - lr)
+                ema_param.add_(param * lr)
diff --git a/tests/integration_tests/test_pipeline.py b/tests/integration_tests/test_pipeline.py
index 41bfbbc6..cc86cb3c 100644
--- a/tests/integration_tests/test_pipeline.py
+++ b/tests/integration_tests/test_pipeline.py
@@ -1,96 +1,45 @@
 import logging
 
 import lightning as pl
-import torch
-from skimage.transform import resize
-from torch.utils.data import random_split
+import numpy as np
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder
 
-from frdc.models import FaceNet
-from frdc.preprocess.extract_segments import (
-    extract_segments_from_bounds,
-    extract_segments_from_labels,
-)
-from frdc.train import FRDCDataModule, FRDCModule
-from utils import get_labels
-
-
-def fn_segment_tf(x):
-    x = [resize(s, [FaceNet.MIN_SIZE, FaceNet.MIN_SIZE]) for s in x]
-    x = [torch.from_numpy(s) for s in x]
-    x = torch.stack(x)
-    x = torch.nan_to_num(x)
-    x = x.permute(0, 3, 1, 2)
-    return x
-
-
-fn_split = lambda x: random_split(x, lengths=[len(x) - 6, 3, 3])
+from frdc.models.inceptionv3 import InceptionV3MixMatchModule
+from frdc.train.frdc_datamodule import FRDCDataModule
 
 BATCH_SIZE = 3
 
 
-def test_manual_segmentation_pipeline(ds) -> tuple[FRDCModule, FRDCDataModule]:
+def test_manual_segmentation_pipeline(ds):
     """Manually segment the image according to bounds.csv,
     then train a model on it."""
 
-    ar, order = ds.get_ar_bands()
-    bounds, labels = ds.get_bounds_and_labels()
-    segments = extract_segments_from_bounds(ar, bounds, cropped=True)
-
     dm = FRDCDataModule(
-        segments=segments,
-        labels=labels,
-        preprocess=fn_segment_tf,
-        train_val_test_split=fn_split,
-        augmentation=lambda x: x,
+        train_lab_ds=ds,
+        train_unl_ds=None,
+        val_ds=ds,
         batch_size=BATCH_SIZE,
     )
-    m = FRDCModule(
-        model_cls=FaceNet,
-        model_kwargs={"n_out_classes": 10},
-        optim_cls=torch.optim.Adam,
-        optim_kwargs=dict(lr=1e-3),
-    )
-
-    trainer = pl.Trainer(fast_dev_run=True)
-    trainer.fit(m, datamodule=dm)
-
-    val_loss = trainer.validate(m, datamodule=dm)[0]["val_loss"]
-    test_loss = trainer.test(m, datamodule=dm)[0]["test_loss"]
 
-    logging.debug(f"Validation score: {val_loss:.2%}")
-    logging.debug(f"Test score: {test_loss:.2%}")
-
-    return m, dm
-
-
-def test_auto_segmentation_pipeline(ds):
-    """Automatically segment the image, then use a model to predict."""
-
-    # Auto segmentation
-    ar, order = ds.get_ar_bands()
-    ar_labels = get_labels(ar, order)
-    segments_auto = extract_segments_from_labels(ar, ar_labels)
+    oe = OrdinalEncoder(
+        handle_unknown="use_encoded_value",
+        unknown_value=np.nan,
+    )
+    oe.fit(np.array(ds.targets).reshape(-1, 1))
+    n_classes = len(oe.categories_[0])
 
-    # Get our model trained on the bounds.csv
-    m, _ = test_manual_segmentation_pipeline(ds)
+    ss = StandardScaler()
+    ss.fit(ds.ar.reshape(-1, ds.ar.shape[-1]))
 
-    # Construct our datamodule for prediction
-    dm_auto = FRDCDataModule(
-        segments=segments_auto,
-        labels=None,  # Labels can be none if we just want predictions.
-        preprocess=fn_segment_tf,
-        train_val_test_split=fn_split,
-        batch_size=BATCH_SIZE,
+    m = InceptionV3MixMatchModule(
+        n_classes=n_classes,
+        lr=1e-3,
+        x_scaler=ss,
+        y_encoder=oe,
     )
 
     trainer = pl.Trainer(fast_dev_run=True)
-    # The predictions have a shape of (N, C), where N is the number of
-    # segments, and C is the number of classes.
-    predictions = torch.concat(trainer.predict(m, datamodule=dm_auto))
-
-    assert predictions.shape[0] == len(
-        segments_auto
-    ), "Expected the same number of predictions as segments."
+    trainer.fit(m, datamodule=dm)
 
-    logging.debug(f"Predictions: {predictions}")
-    logging.debug(f"Class Predictions: {torch.argmax(predictions, dim=1)}")
+    val_loss = trainer.validate(m, datamodule=dm)[0]["val_loss"]
+    logging.debug(f"Validation score: {val_loss:.2%}")
diff --git a/tests/model_tests/chestnut_dec_may/augmentation.py b/tests/model_tests/chestnut_dec_may/augmentation.py
deleted file mode 100644
index f182b36d..00000000
--- a/tests/model_tests/chestnut_dec_may/augmentation.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import torch
-from torchvision.transforms.v2 import RandomHorizontalFlip, RandomVerticalFlip
-
-
-def augmentation(t: torch.Tensor) -> torch.Tensor:
-    """Runs out augmentation on a tensor."""
-    t = RandomHorizontalFlip()(t)
-    t = RandomVerticalFlip()(t)
-    return t
diff --git a/tests/model_tests/chestnut_dec_may/confusion_matrix.png b/tests/model_tests/chestnut_dec_may/confusion_matrix.png
index 7761f8d3..ec81ced0 100644
Binary files a/tests/model_tests/chestnut_dec_may/confusion_matrix.png and b/tests/model_tests/chestnut_dec_may/confusion_matrix.png differ
diff --git a/tests/model_tests/chestnut_dec_may/evaluate.py b/tests/model_tests/chestnut_dec_may/evaluate.py
deleted file mode 100644
index 0cc245cd..00000000
--- a/tests/model_tests/chestnut_dec_may/evaluate.py
+++ /dev/null
@@ -1,53 +0,0 @@
-import lightning as pl
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-from seaborn import heatmap
-from sklearn.metrics import confusion_matrix
-
-from frdc.train import FRDCDataModule
-from frdc.train import FRDCModule
-from .preprocess import preprocess
-from tests.model_tests.utils import get_dataset
-
-# Get our Test
-# TODO: Ideally, we should have a separate dataset for testing.
-segments, labels = get_dataset(
-    "chestnut_nature_park", "20210510", "90deg43m85pct255deg/map"
-)
-
-# Prepare the datamodule and trainer
-dm = FRDCDataModule(segments=segments, preprocess=preprocess, batch_size=5)
-
-# TODO: Hacky way to load our LabelEncoder
-dm.le.classes_ = np.load("le.npy", allow_pickle=True)
-
-# Load the model
-m = FRDCModule.load_from_checkpoint(
-    "lightning_logs/version_88/checkpoints/epoch=99-step=700.ckpt"
-)
-
-# Make predictions
-trainer = pl.Trainer(logger=False)
-pred = trainer.predict(m, datamodule=dm)
-y_pred = torch.concat(pred, dim=0).argmax(dim=1)
-y_true = dm.le.transform(labels)
-
-# Plot the confusion matrix
-cm = confusion_matrix(y_true, y_pred)
-
-plt.figure(figsize=(10, 10))
-
-heatmap(
-    cm,
-    annot=True,
-    xticklabels=dm.le.classes_,
-    yticklabels=dm.le.classes_,
-    cbar=False,
-)
-
-plt.tight_layout(pad=3)
-plt.title("Confusion Matrix")
-plt.xlabel("Predicted Label")
-plt.ylabel("True Label")
-plt.savefig("confusion_matrix.png")
diff --git a/tests/model_tests/chestnut_dec_may/le.npy b/tests/model_tests/chestnut_dec_may/le.npy
deleted file mode 100644
index cb1aa764..00000000
Binary files a/tests/model_tests/chestnut_dec_may/le.npy and /dev/null differ
diff --git a/tests/model_tests/chestnut_dec_may/main.py b/tests/model_tests/chestnut_dec_may/main.py
deleted file mode 100644
index 3e2fda79..00000000
--- a/tests/model_tests/chestnut_dec_may/main.py
+++ /dev/null
@@ -1,126 +0,0 @@
-""" Tests for the FaceNet model.
-
-This test is done by training a model on the 20201218 dataset, then testing on
-the 20210510 dataset.
-"""
-from pathlib import Path
-
-import lightning as pl
-import numpy as np
-import torch
-from lightning.pytorch.callbacks import (
-    LearningRateMonitor,
-    ModelCheckpoint,
-    EarlyStopping,
-)
-from torch.utils.data import TensorDataset, Dataset, Subset
-
-from frdc.models import FaceNet
-from frdc.train import FRDCDataModule, FRDCModule
-from tests.model_tests.chestnut_dec_may.augmentation import augmentation
-from tests.model_tests.chestnut_dec_may.preprocess import preprocess
-from tests.model_tests.utils import get_dataset
-from lightning.pytorch.loggers import WandbLogger
-import wandb
-
-assert wandb.run is None
-
-wandb.setup(wandb.Settings(program=__name__, program_relpath=__name__))
-run = wandb.init()
-logger = WandbLogger(name="chestnut_dec_may", project="frdc")
-
-
-def train_val_test_split(
-    x: TensorDataset,
-) -> list[Dataset, Dataset, Dataset]:
-    # Defines how to split the dataset into train, val, test subsets.
-    # TODO: Quite ugly as it uses the global variables segments_0 and
-    #  segments_1. Will need to refactor this.
-    return [
-        Subset(x, list(range(len(segments_0)))),
-        Subset(
-            x,
-            list(range(len(segments_0), len(segments_0) + len(segments_1))),
-        ),
-        [],
-    ]
-
-
-# Prepare the dataset
-segments_0, labels_0 = get_dataset("chestnut_nature_park", "20201218", None)
-segments_1, labels_1 = get_dataset(
-    "chestnut_nature_park", "20210510", "90deg43m85pct255deg/map"
-)
-
-
-# Concatenate the datasets
-segments = [*segments_0, *segments_1]
-labels = [*labels_0, *labels_1]
-
-BATCH_SIZE = 5
-EPOCHS = 50
-LR = 1e-3
-
-# Prepare the datamodule and trainer
-dm = FRDCDataModule(
-    # Input to the model
-    segments=segments,
-    # Output of the model
-    labels=labels,
-    # Preprocessing function
-    preprocess=preprocess,
-    # Augmentation function (Only on train)
-    augmentation=augmentation,
-    # Splitting function
-    train_val_test_split=train_val_test_split,
-    # Batch size
-    batch_size=BATCH_SIZE,
-)
-
-trainer = pl.Trainer(
-    max_epochs=EPOCHS,
-    # fast_dev_run=True,
-    # Set the seed for reproducibility
-    # TODO: Though this is set, the results are still not reproducible.
-    deterministic=True,
-    # fast_dev_run=True,
-    accelerator="cpu",
-    log_every_n_steps=4,
-    callbacks=[
-        # Stop training if the validation loss doesn't improve for 4 epochs
-        EarlyStopping(monitor="val_loss", patience=4, mode="min"),
-        # Log the learning rate on TensorBoard
-        LearningRateMonitor(logging_interval="epoch"),
-        # Save the best model
-        ModelCheckpoint(monitor="val_loss", mode="min", save_top_k=1),
-    ],
-    logger=logger,
-)
-
-m = FRDCModule(
-    # Our model is the "FaceNet" model
-    # TODO: It's not really the FaceNet model,
-    #  but a modified version of it.
-    model_cls=FaceNet,
-    model_kwargs=dict(n_out_classes=len(set(labels))),
-    # We use the Adam optimizer
-    optim_cls=torch.optim.Adam,
-    # TODO: This is not fine-tuned.
-    optim_kwargs=dict(lr=LR, weight_decay=1e-4, amsgrad=True),
-)
-
-trainer.fit(m, datamodule=dm)
-# TODO: Quite hacky, but we need to save the label encoder for prediction.
-np.save("le.npy", dm.le.classes_)
-
-report = f"""
-# Chestnut Nature Park (Dec 2020 vs May 2021)
-[WandB Report]({run.get_url()})
-TODO: Authentication for researchers
-"""
-
-with open(Path(__file__).parent / "report.md", "w") as f:
-    f.write(report)
-
-
-wandb.finish()
diff --git a/tests/model_tests/chestnut_dec_may/preprocess.py b/tests/model_tests/chestnut_dec_may/preprocess.py
deleted file mode 100644
index 090b7b9d..00000000
--- a/tests/model_tests/chestnut_dec_may/preprocess.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import torch
-
-# from glcm_cupy import Features
-from torchvision.transforms.v2 import Resize
-
-from frdc.models import FaceNet
-
-# from frdc.preprocess.glcm_padded import append_glcm_padded_cached
-from frdc.preprocess.scale import scale_normal_per_band, scale_0_1_per_band
-
-
-# TODO: Eventually, we will have multiple tests, and we should try to make
-#   this function test agnostic.
-
-
-def channel_preprocess(ar: np.ndarray) -> np.ndarray:
-    # Preprocesses a channel array of shape: (H, W)
-    shape = ar.shape
-    ar_flt = ar.flatten()
-    return ar_flt.reshape(*shape)
-
-
-def segment_preprocess(ar: np.ndarray) -> torch.Tensor:
-    # Preprocesses a segment array of shape: (H, W, C)
-
-    # Add a small epsilon to avoid upper bound of 1.0
-    ar = scale_0_1_per_band(ar, epsilon=0.001)
-    # ar = append_glcm_padded_cached(
-    #     ar,
-    #     step_size=7,
-    #     bin_from=1,
-    #     bin_to=128,
-    #     radius=3,
-    #     features=(Features.MEAN,),
-    # )
-    # # We can then scale normal for better neural network convergence
-    ar = scale_normal_per_band(ar)
-    ar = np.rollaxis(ar, axis=2)
-
-    # TODO: Doesn't seem like we have any channel preprocessing here.
-    # ar = np.stack([
-    #     channel_preprocess(ar[..., ch]) for ch in range(ar.shape[-1])
-    # ])
-
-    t = torch.from_numpy(ar)
-    t = Resize([FaceNet.MIN_SIZE, FaceNet.MIN_SIZE], antialias=True)(t)
-    return t
-
-
-def preprocess(l_ar: list[np.ndarray]) -> torch.Tensor:
-    """Preprocesses a list of segments.
-
-    Notes:
-        We structure the transformations into 3 levels.
-        1. Segments transformation (This function)
-        2. Per segment transformation (segment_preprocess)
-        3. Per channel transformation (channel_preprocess)
-
-    Returns:
-        A preprocessed tensor of shape: (batch, channels, height, width)
-    """
-
-    l_t: list[torch.Tensor] = [segment_preprocess(ar) for ar in l_ar]
-    t: torch.Tensor = torch.stack(l_t)
-    t = torch.nan_to_num(t)
-    return t
diff --git a/tests/model_tests/chestnut_dec_may/train.py b/tests/model_tests/chestnut_dec_may/train.py
new file mode 100644
index 00000000..6fb383c3
--- /dev/null
+++ b/tests/model_tests/chestnut_dec_may/train.py
@@ -0,0 +1,276 @@
+""" Tests for the InceptionV3 model on the Chestnut Nature Park dataset.
+
+This test is done by training a model on the 20201218 dataset, then testing on
+the 20210510 dataset.
+"""
+
+import os
+from pathlib import Path
+
+import lightning as pl
+import numpy as np
+import torch
+import wandb
+from lightning.pytorch.callbacks import (
+    LearningRateMonitor,
+    ModelCheckpoint,
+    EarlyStopping,
+)
+from lightning.pytorch.loggers import WandbLogger
+from matplotlib import pyplot as plt
+from seaborn import heatmap
+from sklearn.metrics import confusion_matrix
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder
+from torch.utils.data import DataLoader
+from torchvision.transforms import RandomVerticalFlip
+from torchvision.transforms.v2 import (
+    Compose,
+    ToImage,
+    ToDtype,
+    RandomVerticalFlip,
+    RandomCrop,
+    CenterCrop,
+)
+from torchvision.transforms.v2 import RandomHorizontalFlip
+
+from frdc.load import FRDCDataset
+from frdc.load.dataset import FRDCUnlabelledDataset
+from frdc.models.inceptionv3 import InceptionV3MixMatchModule
+from frdc.train.frdc_datamodule import FRDCDataModule
+
+THIS_DIR = Path(__file__).parent
+
+
+# TODO: Ideally, we should have a separate dataset for testing.
+
+
+# TODO: This is pretty hacky, I'm not sure if there's a better way to do this.
+#       Note that initializing datasets separately then concatenating them
+#       together is 4x slower than initializing a dataset then hacking into
+#       the __getitem__ method.
+class FRDCDatasetFlipped(FRDCDataset):
+    def __len__(self):
+        """Assume that the dataset is 4x larger than it actually is.
+
+        For example, for index 0, we return the original image. For index 1, we
+        return the horizontally flipped image and so on, until index 3.
+        Then, return the next image for index 4, and so on.
+        """
+        return super().__len__() * 4
+
+    def __getitem__(self, idx):
+        """Alter the getitem method to implement the logic above."""
+        x, y = super().__getitem__(int(idx // 4))
+        if idx % 4 == 0:
+            return x, y
+        elif idx % 4 == 1:
+            return RandomHorizontalFlip(p=1)(x), y
+        elif idx % 4 == 2:
+            return RandomVerticalFlip(p=1)(x), y
+        elif idx % 4 == 3:
+            return RandomHorizontalFlip(p=1)(RandomVerticalFlip(p=1)(x)), y
+
+
+def evaluate(ckpt_pth: Path | str | None = None) -> tuple[plt.Figure, float]:
+    ds = FRDCDatasetFlipped(
+        "chestnut_nature_park",
+        "20210510",
+        "90deg43m85pct255deg/map",
+        transform=preprocess,
+    )
+
+    if ckpt_pth is None:
+        # This fetches all possible checkpoints and gets the latest one
+        ckpt_pth = sorted(
+            THIS_DIR.glob("**/*.ckpt"), key=lambda x: x.stat().st_mtime_ns
+        )[-1]
+
+    m = InceptionV3MixMatchModule.load_from_checkpoint(ckpt_pth)
+    # Make predictions
+    trainer = pl.Trainer(logger=False)
+    pred = trainer.predict(m, dataloaders=DataLoader(ds, batch_size=32))
+
+    y_trues = []
+    y_preds = []
+    for y_true, y_pred in pred:
+        y_trues.append(y_true)
+        y_preds.append(y_pred)
+    y_trues = np.concatenate(y_trues)
+    y_preds = np.concatenate(y_preds)
+    acc = (y_trues == y_preds).mean()
+
+    # Plot the confusion matrix
+    cm = confusion_matrix(y_trues, y_preds)
+
+    plt.figure(figsize=(10, 10))
+
+    heatmap(
+        cm,
+        annot=True,
+        xticklabels=m.y_encoder.categories_[0],
+        yticklabels=m.y_encoder.categories_[0],
+        cbar=False,
+    )
+    plt.title(f"Accuracy: {acc:.2%}")
+    plt.tight_layout(pad=3)
+    plt.xlabel("Predicted Label")
+    plt.ylabel("True Label")
+    return plt.gcf(), acc
+
+
+def preprocess(x):
+    return Compose(
+        [
+            ToImage(),
+            ToDtype(torch.float32, scale=True),
+            CenterCrop(
+                [
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                ],
+            ),
+        ]
+    )(x)
+
+
+def train_preprocess(x):
+    return Compose(
+        [
+            ToImage(),
+            ToDtype(torch.float32, scale=True),
+            RandomCrop(
+                [
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                    InceptionV3MixMatchModule.MIN_SIZE,
+                ],
+                pad_if_needed=True,
+                padding_mode="constant",
+                fill=0,
+            ),
+            RandomHorizontalFlip(),
+            RandomVerticalFlip(),
+        ]
+    )(x)
+
+
+def train_unl_preprocess(n_aug: int = 2):
+    def f(x):
+        # This simulates the n_aug of MixMatch
+        return (
+            [train_preprocess(x) for _ in range(n_aug)] if n_aug > 0 else None
+        )
+
+    return f
+
+
+def main(
+    batch_size=32,
+    epochs=10,
+    train_iters=25,
+    val_iters=15,
+    lr=1e-3,
+):
+    run = wandb.init()
+    logger = WandbLogger(name="chestnut_dec_may", project="frdc")
+    # Prepare the dataset
+    train_lab_ds = FRDCDataset(
+        "chestnut_nature_park",
+        "20201218",
+        None,
+        transform=train_preprocess,
+    )
+
+    # TODO: This is a hacky impl of the unlabelled dataset, see the docstring
+    #       for future work.
+    train_unl_ds = FRDCUnlabelledDataset(
+        "chestnut_nature_park",
+        "20201218",
+        None,
+        transform=train_unl_preprocess(2),
+    )
+
+    # Subset(train_ds, np.argwhere(train_ds.targets == 0).reshape(-1))
+    val_ds = FRDCDataset(
+        "chestnut_nature_park",
+        "20210510",
+        "90deg43m85pct255deg/map",
+        transform=preprocess,
+    )
+
+    oe = OrdinalEncoder(
+        handle_unknown="use_encoded_value",
+        unknown_value=np.nan,
+    )
+    oe.fit(np.array(train_lab_ds.targets).reshape(-1, 1))
+    n_classes = len(oe.categories_[0])
+
+    ss = StandardScaler()
+    ss.fit(train_lab_ds.ar.reshape(-1, train_lab_ds.ar.shape[-1]))
+
+    # Prepare the datamodule and trainer
+    dm = FRDCDataModule(
+        train_lab_ds=train_lab_ds,
+        # Pass in None to use the default supervised DM
+        train_unl_ds=train_unl_ds,
+        val_ds=val_ds,
+        batch_size=batch_size,
+        train_iters=train_iters,
+        val_iters=val_iters,
+    )
+
+    trainer = pl.Trainer(
+        max_epochs=epochs,
+        deterministic=True,
+        accelerator="gpu",
+        log_every_n_steps=4,
+        callbacks=[
+            # Stop training if the validation loss doesn't improve for 4 epochs
+            EarlyStopping(monitor="val_loss", patience=4, mode="min"),
+            # Log the learning rate on TensorBoard
+            LearningRateMonitor(logging_interval="epoch"),
+            # Save the best model
+            ckpt := ModelCheckpoint(
+                monitor="val_loss", mode="min", save_top_k=1
+            ),
+        ],
+        logger=logger,
+    )
+    m = InceptionV3MixMatchModule(
+        n_classes=n_classes,
+        lr=lr,
+        x_scaler=ss,
+        y_encoder=oe,
+    )
+
+    trainer.fit(m, datamodule=dm)
+
+    with open(Path(__file__).parent / "report.md", "w") as f:
+        f.write(
+            f"# Chestnut Nature Park (Dec 2020 vs May 2021)"
+            f"[WandB Report]({run.get_url()})"
+        )
+
+    fig, acc = evaluate(Path(ckpt.best_model_path))
+    wandb.log({"confusion_matrix": wandb.Image(fig)})
+    wandb.log({"eval_accuracy": acc})
+
+    wandb.finish()
+
+
+if __name__ == "__main__":
+    BATCH_SIZE = 32
+    EPOCHS = 10
+    TRAIN_ITERS = 25
+    VAL_ITERS = 15
+    LR = 1e-3
+    os.environ["GOOGLE_CLOUD_PROJECT"] = "frmodel"
+
+    assert wandb.run is None
+    wandb.setup(wandb.Settings(program=__name__, program_relpath=__name__))
+    main(
+        batch_size=BATCH_SIZE,
+        epochs=EPOCHS,
+        train_iters=TRAIN_ITERS,
+        val_iters=VAL_ITERS,
+        lr=LR,
+    )
diff --git a/tests/model_tests/utils.py b/tests/model_tests/utils.py
index 1fd09a7d..22640115 100644
--- a/tests/model_tests/utils.py
+++ b/tests/model_tests/utils.py
@@ -1,12 +1 @@
-from frdc.load import FRDCDataset
-from frdc.preprocess.extract_segments import extract_segments_from_bounds
-
 BANDS = ["NB", "NG", "NR", "RE", "NIR"]
-
-
-def get_dataset(site, date, version):
-    ds = FRDCDataset(site=site, date=date, version=version)
-    ar, order = ds.get_ar_bands(BANDS)
-    bounds, labels = ds.get_bounds_and_labels()
-    segments = extract_segments_from_bounds(ar, bounds, cropped=True)
-    return segments, labels
diff --git a/tests/unit_tests/models/test_facenet.py b/tests/unit_tests/models/test_inceptionv3.py
similarity index 60%
rename from tests/unit_tests/models/test_facenet.py
rename to tests/unit_tests/models/test_inceptionv3.py
index 269dfaa9..28932d45 100644
--- a/tests/unit_tests/models/test_facenet.py
+++ b/tests/unit_tests/models/test_inceptionv3.py
@@ -1,17 +1,23 @@
 import pytest
 import torch
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder
 
-from frdc.models import FaceNet
+from frdc.models.inceptionv3 import InceptionV3MixMatchModule
 
 N_CLASSES = 42
 N_CHANNELS = 3
 BATCH_SIZE = 2
-MIN_SIZE = FaceNet.MIN_SIZE
+MIN_SIZE = InceptionV3MixMatchModule.MIN_SIZE
 
 
 @pytest.fixture(scope="module")
-def facenet():
-    return FaceNet(n_out_classes=N_CLASSES)
+def inceptionv3():
+    return InceptionV3MixMatchModule(
+        n_classes=N_CLASSES,
+        lr=1e-3,
+        x_scaler=StandardScaler(),
+        y_encoder=OrdinalEncoder(),
+    )
 
 
 @pytest.mark.parametrize(
@@ -31,7 +37,7 @@ def facenet():
         [BATCH_SIZE, 1, MIN_SIZE, False],
     ],
 )
-def test_facenet_io(facenet, batch_size, channels, size, ok):
+def test_inceptionv3_io(inceptionv3, batch_size, channels, size, ok):
     def check(net, x):
         if ok:
             assert net(x).shape == (BATCH_SIZE, N_CLASSES)
@@ -41,19 +47,21 @@ def check(net, x):
 
     x = torch.rand((batch_size, channels, size, size))
 
-    facenet.train()
-    check(facenet, x)
-    facenet.eval()
-    check(facenet, x)
+    inceptionv3.train()
+    check(inceptionv3, x)
+    inceptionv3.eval()
+    check(inceptionv3, x)
 
 
-def test_facenet_frozen(facenet):
+def test_inception_frozen(inceptionv3):
     """Assert that the base model is frozen, and the rest is trainable."""
-    assert sum(p.numel() for p in facenet.parameters() if p.requires_grad) > 0
+    assert (
+        sum(p.numel() for p in inceptionv3.parameters() if p.requires_grad) > 0
+    )
     assert (
         sum(
             p.numel()
-            for p in facenet.inception.parameters()
+            for p in inceptionv3.inception.parameters()
             if p.requires_grad
         )
         == 0