diff --git a/README.md b/README.md
index b584ae3..403a57b 100644
--- a/README.md
+++ b/README.md
@@ -18,9 +18,6 @@ The following code snippets show how to train and evaluate a model on classifyin
 ```console
 python -m phenocam_snow.train \
    canadaojp \
-   --model resnet18 \
-   --learning_rate 5e-4 \
-   --weight_decay 0.01 \
    --new \
    --n_train 120 \
    --n_test 30 \
@@ -31,10 +28,9 @@ This will print out the file path of the best model, which can be substituted in
 ```console
 python -m phenocam_snow.predict \
    canadaojp \
-   [path/to/best_model.ckpt] \
-   resnet18 \
-   --categories snow no_snow too_dark
-   --url https://phenocam.nau.edu/data/latest/canadaojp.jpg
+   canadaojp_lightning_logs/version_0/checkpoints/epoch=12-step=78.ckpt \
+   --categories snow no_snow too_dark \
+   --directory 'canadaojp/test'
 ```
 
 Advanced usage details can be found in the [documentation](http://phenocamsnow.readthedocs.io/).
diff --git a/docs/source/user-guide/predict.rst b/docs/source/user-guide/predict.rst
index 9b7467b..74cbde3 100644
--- a/docs/source/user-guide/predict.rst
+++ b/docs/source/user-guide/predict.rst
@@ -1,8 +1,8 @@
 Classifying New Images
 ======================
 Models produced by PhenoCamSnow can be used to generate predictions for all
-images in a local directory, or to generate a prediction for a single image as
-pointed to by URL. 
+images in a local directory, or to generate a prediction for images pointed to
+by their URLs. 
 
 Local Prediction
 ----------------
@@ -16,15 +16,13 @@ following command.
     python -m phenocam_snow.predict \
         canadaojp \
         canadaojp_lightning_logs/version_0/checkpoints/epoch=12-step=78.ckpt \
-        resnet18 \
-        --categories snow no_snow too_dark
-        --directory canadaojp_test
+        --categories snow no_snow too_dark \
+        --directory 'canadaojp/test'
 
 The file path provided in the third line is printed by the model training
 script. It is also saved in a file called ``best_model_paths.csv``. Ensure that
 the categories provided are the same name and in the same order as provided
-during training. Similarly, ensure that the ResNet variant specified is the
-same as in training.
+during training.
 
 The program will then print out its predictions to a CSV file that looks like
 this:
@@ -37,43 +35,44 @@ this:
     # 0. snow
     # 1. no_snow
     # 2. too_dark
-    timestamp,label
-    2016-04-01 17:29:59,snow
-    2017-11-13 13:59:59,snow
-    2019-01-24 16:59:59,snow
-    2016-04-09 20:29:59,snow
-    2020-02-26 09:59:59,snow
-    2016-12-21 11:29:59,snow
-    2017-03-19 11:59:59,snow
-    2020-10-29 13:29:59,snow
-    2018-03-01 19:00:00,snow
-    2016-04-24 07:29:59,no_snow
-    2016-05-14 04:00:00,no_snow
-    2019-09-25 09:30:00,no_snow
-    2018-07-20 17:00:00,no_snow
-    2017-05-27 16:29:59,no_snow
-    2016-08-28 20:29:59,no_snow
-    2017-06-06 09:30:00,no_snow
-    2017-09-02 08:29:59,no_snow
-    2019-09-09 16:00:00,no_snow
-    2019-01-07 01:29:59,too_dark
-    2020-01-07 23:30:00,too_dark
-    2017-09-05 01:59:59,too_dark
-    2018-07-24 01:30:00,too_dark
-    2017-11-21 18:59:59,too_dark
-    2020-01-01 03:29:59,too_dark
-    2019-02-01 05:29:59,too_dark
-    2020-10-14 06:30:00,too_dark
-    2020-05-31 02:59:59,too_dark
-    2018-12-22 02:29:59,too_dark
-    2019-05-01 04:30:00,too_dark
-    2020-07-22 04:00:00,too_dark
+    filename,label
+    canadaojp_2020_04_25_052959.jpg,snow
+    canadaojp_2019_11_14_073000.jpg,snow
+    canadaojp_2020_01_08_155959.jpg,snow
+    canadaojp_2018_10_06_182959.jpg,no_snow
+    canadaojp_2019_05_30_040000.jpg,no_snow
+    canadaojp_2018_10_23_142959.jpg,no_snow
+    canadaojp_2018_04_27_083000.jpg,no_snow
+    canadaojp_2020_08_05_100000.jpg,no_snow
+    canadaojp_2020_07_13_215959.jpg,no_snow
+    canadaojp_2020_04_26_045959.jpg,no_snow
+    canadaojp_2020_07_01_205959.jpg,no_snow
+    canadaojp_2018_06_01_082959.jpg,no_snow
+    canadaojp_2020_05_30_065959.jpg,no_snow
+    canadaojp_2020_06_11_033000.jpg,no_snow
+    canadaojp_2019_05_10_112959.jpg,no_snow
+    canadaojp_2020_04_09_232959.jpg,too_dark
+    canadaojp_2018_10_31_182959.jpg,too_dark
+    canadaojp_2019_08_31_000000.jpg,too_dark
+    canadaojp_2020_02_01_075959.jpg,too_dark
+    canadaojp_2018_12_18_185959.jpg,too_dark
+    canadaojp_2020_03_05_012959.jpg,too_dark
+    canadaojp_2020_04_22_035959.jpg,too_dark
+    canadaojp_2020_05_02_032959.jpg,too_dark
+    canadaojp_2018_12_29_052959.jpg,too_dark
+    canadaojp_2020_04_24_025959.jpg,too_dark
+    canadaojp_2020_04_11_232959.jpg,too_dark
+    canadaojp_2018_11_20_065959.jpg,too_dark
+    canadaojp_2019_02_25_192959.jpg,too_dark
+    canadaojp_2020_03_30_232959.jpg,too_dark
+    canadaojp_2018_06_15_003000.jpg,too_dark
+
 
 Online Prediction
 -----------------
 
-PhenoCamSnow is also capable of generating a prediction for a single online
-image, as pointed to by a URL. For example, using canadaojp again:
+PhenoCamSnow is also capable of generating a prediction for online images as
+as pointed to by their URLs. For example, using canadaojp again:
 
 .. code-block:: bash
     :linenos:
@@ -83,9 +82,22 @@ image, as pointed to by a URL. For example, using canadaojp again:
         canadaojp_lightning_logs/version_0/checkpoints/epoch=12-step=78.ckpt \
         resnet18 \
         --categories snow no_snow too_dark
-        --url https://phenocam.nau.edu/data/latest/canadaojp.jpg
+        --urls urls.txt
+
+Here, ``urls.txt`` is a file containing one image URL per line. The program
+will write its predictions to a CSV file, formatted similarly to the one shown
+for offline prediction.
+
+Getting Help
+------------
+
+You can see a help message displayed by running:
+
+.. code-block:: bash
+    :linenos:
+
+    python -m phenocam_snow.predict --help
 
-The program will print its prediction to the console. E.g., ``no_snow``.
 
 If you have further questions, please raise an
 `issue on the GitHub repository <https://github.com/jasonjewik/PhenoCamSnow/issues/new/choose>`_.
\ No newline at end of file
diff --git a/docs/source/user-guide/train.rst b/docs/source/user-guide/train.rst
index e6b6802..7c73e3e 100644
--- a/docs/source/user-guide/train.rst
+++ b/docs/source/user-guide/train.rst
@@ -13,9 +13,6 @@ spaces: use underscores instead.
 
     python -m phenocam_snow.train \
         canadaojp \
-        --model resnet18 \
-        --learning_rate 5e-4 \
-        --weight_decay 0.01 \
         --new \
         --n_train 120 \
         --n_test 30 \
@@ -28,19 +25,9 @@ evaluating the model's performance. After each set of images is downloaded,
 the program will prompt you to label the images. To do so, it will create
 three sub-directories inside of the current directory, each named after one of
 the classes. Move the images into their corresponding directory (e.g., all the
-pictures with snow should go into the "snow" directory) to label them.
-
-You can also change the model. Currently, PhenocamSnow only supports training 
-`ResNet <https://arxiv.org/abs/1512.03385/>`_ variants: ResNet-18, ResNet-34,
-ResNet-50, ResNet-101, and ResNet-152. The expected format is "resnet{number}".
-The larger the number, the more expressive the model, but the slower it might
-be to train.
-
-The learning rate and weight decay arguments are hyperparameters for the model.
-The "new" flag indicates that you need to download the training and testing
-images. In this case, we will be getting 120 training images and 30 testing
-images. Empirically, I found this to work well for canadaojp (93% accuracy
-on the testing images).
+pictures with snow should go into the "snow" directory) to label them. After
+the images are labeled, they will be moved out of the sub-directories back into
+the original top-level directory.
 
 When the program has finished training the model, it will print out the model's
 performance on the testing images and the file path of the saved best model.
@@ -53,8 +40,75 @@ which will look something like this:
     timestamp,site_name,best_model
     2023-05-24T15:45:59.642605,canadaojp,/home/jason/Development/test/canadaojp_lightning_logs/version_0/checkpoints/epoch=12-step=78.ckpt
 
-To get the model's predictions for a set of images, see the next section of the
-documentation.
+To get the model's predictions for a set of images, see the User Guide page
+called "Classifying New Images".
+
+Advanced Usage
+--------------
+
+Overriding Model and Hyperparameter Defaults
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Currently, PhenoCamSnow only supports training models which use `ResNet <https://arxiv.org/abs/1512.03385/>`_
+variants as the feature extraction backbone: ResNet-18, ResNet-34, ResNet-50,
+ResNet-101, and ResNet-152. The expected format is "resnet{number}". The larger
+the number, the more expressive the model, but the slower it might be to train.
+PhenoCamSnow also allows you to specify learning rate, weight decay, and max
+epochs hyperparameters. I have picked what I determined to be sensible
+defaults, but these all should be tuned for your specific use-case. In short:
+
+* The learning rate affects the speed at which the model learns (too small and the model will learn slowly, too high and the model might overshoot its optimal parameters).
+
+* The weight decay affects the model's generalization abilities (too low and the model might "memorize" the training set and perform poorly on images it has not seen in training).
+
+* The max epochs determines how many training "sessions" the model will run, at most (too few and the model might not have sufficient time to learn well, too many and the model might simply "memorize" the training set). 
+
+Previous example with the ResNet-50 model and some hyperparameter overrides:
+
+.. code-block:: bash
+    :linenos:
+    
+    python -m phenocam_snow.train \
+        canadaojp \
+        --new \
+        --n_train 120 \
+        --n_test 30 \
+        --classes snow no_snow too_dark
+        --model resnet50
+        --learning_rate 1e-3
+        --weight_decay 1e-3
+        --max_epochs 25
+
+Training a Model with Already-Downloaded Images
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The "new" flag in the given examples indicates that you need to download the
+training and testing images. In this case, we will be getting 120 training
+images and 30 testing images.
+
+However, if you already have downloaded and labeled images, then you can train
+a model like this (assuming the directories provided for ``train_dir`` and
+``test_dir`` actually exist and contain the required images and labels files).
+
+.. code-block:: bash
+    :linenos:
+
+    python -m phenocam_snow.train \
+        canadaojp \
+        --existing
+        --train_dir 'canadaojp/train'
+        --test_dir 'canadaojp/test'
+        --classes snow no_snow too_dark
+
+Getting Help
+^^^^^^^^^^^^
+
+You can see a help message displayed by running:
+
+.. code-block:: bash
+    :linenos:
+
+    python -m phenocam_snow.train --help
 
 If you have further questions, please raise an
 `issue on the GitHub repository <https://github.com/jasonjewik/PhenoCamSnow/issues/new/choose>`_.
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 13248f9..d13fddf 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -13,98 +13,98 @@ files = [
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.4.3"
+version = "2.4.4"
 description = "Happy Eyeballs for asyncio"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
-    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+    {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
+    {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.11.8"
+version = "3.11.10"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 [package.extras]
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 [[package]]
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 version = "2024.8.30"
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+pyspellchecker = "*"
+sphinx = "*"
+
+[package.extras]
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+typecheck = ["mypy", "pytest-lsp (>=0.3.1)", "types-appdirs", "types-docutils", "types-pygments"]
+
 [[package]]
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 version = "1.2.2"
@@ -683,13 +731,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
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 [package.extras]
-protobuf = ["grpcio-tools (>=1.68.0)"]
+protobuf = ["grpcio-tools (>=1.68.1)"]
 
 [[package]]
 name = "identify"
@@ -1169,6 +1217,21 @@ cli = ["fire"]
 docs = ["requests (>=2.0.0)"]
 typing = ["mypy (>=1.0.0)", "types-setuptools"]
 
+[[package]]
+name = "lsprotocol"
+version = "2023.0.1"
+description = "Python implementation of the Language Server Protocol."
+optional = false
+python-versions = ">=3.7"
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+
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+cattrs = "!=23.2.1"
+
 [[package]]
 name = "markdown"
 version = "3.7"
@@ -1463,13 +1526,13 @@ files = [
 
 [[package]]
 name = "nbclient"
-version = "0.10.0"
+version = "0.10.1"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 optional = false
 python-versions = ">=3.8.0"
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 [package.dependencies]
@@ -1480,7 +1543,7 @@ traitlets = ">=5.4"
 
 [package.extras]
 dev = ["pre-commit"]
-docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+docs = ["autodoc-traits", "flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "mock", "moto", "myst-parser", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling", "testpath", "xmltodict"]
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 [[package]]
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-    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
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 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
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+
+[package.extras]
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+
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@@ -2245,22 +2310,22 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
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 spelling = ["pyenchant (>=3.2,<4.0)"]
 testutils = ["gitpython (>3)"]
 
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 [[package]]
@@ -3299,93 +3418,93 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "yarl"
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 optional = false
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+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
 [package.dependencies]
@@ -3396,4 +3515,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "4641cc33090fbf1009246bd296bbb62b5a0dbb52b5c016c36c40b9eba93f9649"
+content-hash = "2fd2c8270b68459d3efa0472b874d041c52cb30818b3c6c948c47d8e74aa1963"
diff --git a/pyproject.toml b/pyproject.toml
index 5b82ce0..3073ab4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,6 @@ torchvision = "^0.20.1"
 tensorboard = "^2.18.0"
 pandas = "^2.2.3"
 pillow = "^11.0.0"
-tqdm = "^4.67.1"
 
 [tool.poetry.group.dev.dependencies]
 black = "^24.10.0"
@@ -47,6 +46,7 @@ mypy = "^1.13.0"
 pytest-cov = "^6.0.0"
 isort = "^5.13.2"
 autoflake = "^2.3.1"
+esbonio = "^0.16.5"
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/src/phenocam_snow/model.py b/src/phenocam_snow/model.py
index 2559964..adb7b03 100644
--- a/src/phenocam_snow/model.py
+++ b/src/phenocam_snow/model.py
@@ -5,7 +5,7 @@
 import torch.nn.functional as F
 import torchvision.models as models
 from lightning import LightningModule
-from torchmetrics.classification import MulticlassAccuracy
+from torchmetrics.classification import MulticlassAccuracy, MulticlassF1Score
 
 
 class PhenoCamResNet(LightningModule):  # pragma: no cover
@@ -52,7 +52,10 @@ def __init__(
             param.requires_grad = False
         n_filters = backbone.fc.in_features
         self.classifier = nn.Linear(n_filters, n_classes)
-        self.metric = MulticlassAccuracy(num_classes=n_classes)
+        self.metrics = {
+            "acc": MulticlassAccuracy(num_classes=n_classes),
+            "f1": MulticlassF1Score(num_classes=n_classes),
+        }
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.feature_extractor(x)
@@ -65,9 +68,11 @@ def training_step(self, batch: torch.Tensor, batch_idx: int) -> torch.Tensor:
         yhat = self(x)
         loss = F.cross_entropy(yhat, y)
         preds = torch.argmax(yhat, dim=1)
-        acc = self.metric(preds, y)
+        output_dict = {"train_loss": loss}
+        for name, fn in self.metrics.items():
+            output_dict[f"train_{name}"] = fn.to(preds.device)(preds, y)
         self.log_dict(
-            {"train_loss": loss, "train_acc": acc},
+            output_dict,
             prog_bar=True,
             on_step=True,
             on_epoch=False,
@@ -75,16 +80,18 @@ def training_step(self, batch: torch.Tensor, batch_idx: int) -> torch.Tensor:
         return loss
 
     def evaluate(
-        self, batch: torch.Tensor, stage: Literal["fit", "test"] | None = None
+        self, batch: torch.Tensor, stage: Literal["val", "test"] | None = None
     ) -> None:
         x, y = batch
         yhat = self(x)
         loss = F.cross_entropy(yhat, y)
         preds = torch.argmax(yhat, dim=1)
-        acc = self.metric(preds, y)
+        output_dict = {f"{stage}_loss": loss}
+        for name, fn in self.metrics.items():
+            output_dict[f"{stage}_{name}"] = fn.to(preds.device)(preds, y)
         if stage:
             self.log_dict(
-                {f"{stage}_loss": loss, f"{stage}_acc": acc},
+                output_dict,
                 prog_bar=True,
                 on_step=False,
                 on_epoch=True,
diff --git a/src/phenocam_snow/predict.py b/src/phenocam_snow/predict.py
index db1c70a..da37cbb 100644
--- a/src/phenocam_snow/predict.py
+++ b/src/phenocam_snow/predict.py
@@ -20,11 +20,6 @@ def main():
     )
     parser.add_argument("model_path", help="The path of the model to use.")
     parser.add_argument("--categories", nargs="+", help="The image categories to use.")
-    parser.add_argument(
-        "--url",
-        default=None,
-        help="Provide this if you want to get a prediction for a single online image.",
-    )
     parser.add_argument(
         "--directory",
         default=None,
@@ -36,9 +31,7 @@ def main():
     args = parser.parse_args()
 
     model = load_model_from_file(args.model_path)
-    if args.url:
-        run_model_online(model, args.site_name, args.categories, args.url)
-    elif args.urls:
+    if args.urls:
         run_model_online(model, args.site_name, args.categories, args.urls)
     elif args.directory:
         run_model_offline(model, args.site_name, args.categories, args.directory)
@@ -74,7 +67,7 @@ def classify_online(
         np_img = np.array(img).T
         x = torch.from_numpy(np_img)
         x = data_module.preprocess(x.unsqueeze(0))
-        yhat = model(x)
+        yhat = model(x.to(model.device))
         pred = categories[torch.argmax(yhat, dim=1)]
         return (np_img, pred)
     else:
@@ -103,7 +96,7 @@ def classify_offline(
     :rtype: tuple[np.array, int]
     """
     x = data_module.preprocess(decode_image(img_path).unsqueeze(0))
-    yhat = model(x)
+    yhat = model(x.to(model.device))
     pred = categories[torch.argmax(yhat, dim=1)]
     return pred
 
@@ -146,21 +139,20 @@ def run_model_offline(
     data_module = PhenoCamDataModule(
         "site_name", "train_dir", "train_labels", "test_dir", "test_labels"
     )
-    image_paths = img_dir.glob("*.jpg")
+    image_paths = list(img_dir.glob("*.jpg"))
     predictions = [
         classify_offline(data_module, model, categories, path) for path in image_paths
     ]
 
-    df = pd.DataFrame(
-        zip(image_paths, predictions), columns=["filename", "predicted_label"]
-    )
+    data = [(path, pred) for path, pred in zip(image_paths, predictions)]
+    df = pd.DataFrame(data, columns=["filename", "predicted_label"])
     save_to = img_dir.joinpath("predictions.csv")
     with open(save_to, "w+") as f:
         f.write(f"# Site: {site_name}\n")
         f.write("# Categories:\n")
         for i, cat in enumerate(categories):
             f.write(f"# {i}. {cat}\n")
-    df.to_csv(save_to, mode="a", line_terminator="\n", index=False)
+    df.to_csv(save_to, mode="a", lineterminator="\n", index=False)
     print(f"Results written to {save_to}")
 
     return df
@@ -191,14 +183,15 @@ def run_model_online(
         classify_online(data_module, model, categories, link)[1] for link in links
     ]
 
-    df = pd.DataFrame(zip(links, predictions), columns=["url", "predicted_label"])
+    data = [(link, prediction) for link, prediction in zip(links, predictions)]
+    df = pd.DataFrame(data, columns=["url", "predicted_label"])
     save_to = "predictions.csv"
     with open(save_to, "w+") as f:
         f.write(f"# Site: {site_name}\n")
         f.write("# Categories:\n")
         for i, cat in enumerate(categories):
             f.write(f"# {i}. {cat}\n")
-    df.to_csv(save_to, mode="a", line_terminator="\n", index=False)
+    df.to_csv(save_to, mode="a", lineterminator="\n", index=False)
     print(f"Results written to {save_to}")
 
     return df
diff --git a/src/phenocam_snow/train.py b/src/phenocam_snow/train.py
index e58ae37..1354552 100644
--- a/src/phenocam_snow/train.py
+++ b/src/phenocam_snow/train.py
@@ -1,10 +1,10 @@
+import os
 from argparse import ArgumentParser
 from datetime import datetime
 from pathlib import Path
 from typing import Literal
 
 import lightning as L
-import torch
 from lightning.pytorch.callbacks.early_stopping import EarlyStopping
 from lightning.pytorch.loggers import TensorBoardLogger
 
@@ -24,6 +24,7 @@ def main():
     )
     parser.add_argument("--learning_rate", type=float, default=5e-4)
     parser.add_argument("--weight_decay", type=float, default=0.01)
+    parser.add_argument("--max_epochs", type=int, default=10)
     parser.add_argument(
         "--new",
         action="store_true",
@@ -54,6 +55,7 @@ def main():
             args.model,
             args.learning_rate,
             args.weight_decay,
+            args.max_epochs,
             args.site_name,
             args.n_train,
             args.n_test,
@@ -64,6 +66,7 @@ def main():
             args.model,
             args.learning_rate,
             args.weight_decay,
+            args.max_epochs,
             args.site_name,
             args.train_dir,
             args.test_dir,
@@ -79,6 +82,7 @@ def train_model_with_new_data(
     ],
     learning_rate: float,
     weight_decay: float,
+    max_epochs: int,
     site_name: str,
     n_train: int,
     n_test: int,
@@ -92,6 +96,8 @@ def train_model_with_new_data(
     :type learning_rate: float
     :param weight_decay: The weight decay to use.
     :type weight_decay: float
+    :param max_epochs: The max number of training epochs.
+    :type max_epochs: int
     :param site_name: The name of the PhenoCam site you want.
     :type site_name: str
     :param n_train: The number of training images to use.
@@ -144,6 +150,7 @@ def train_model_with_new_data(
         len(classes),
         learning_rate,
         weight_decay,
+        max_epochs,
     )
 
 
@@ -153,6 +160,7 @@ def train_model_with_existing_data(
     ],
     learning_rate: float,
     weight_decay: float,
+    max_epochs: int,
     site_name: str,
     train_dir: str | Path,
     test_dir: str | Path,
@@ -166,6 +174,8 @@ def train_model_with_existing_data(
     :type learning_rate: float
     :param weight_decay: The weight decay to use.
     :type weight_decay: float
+    :param max_epochs: The max number of training epochs.
+    :type max_epochs: int
     :param site_name: The name of the PhenoCam site you want.
     :type site_name: str
     :param train_dir: The path to the training images directory.
@@ -198,6 +208,7 @@ def train_model_with_existing_data(
         len(classes),
         learning_rate,
         weight_decay,
+        max_epochs,
     )
 
 
@@ -210,19 +221,18 @@ def _fit_and_eval_model(
     n_classes: int,
     learning_rate: float,
     weight_decay: float,
+    max_epochs: int,
 ) -> PhenoCamResNet:
     """Helper function for above public methods. Returns the best model."""
     data_module.setup(stage="fit")
     model = PhenoCamResNet(resnet_variant, n_classes, learning_rate, weight_decay)
     logger = TensorBoardLogger(save_dir=os.getcwd(), name=f"{site_name}_lightning_logs")
     callbacks = [EarlyStopping(monitor="val_loss", mode="min", min_delta=1e-8)]
-    accelerator = "gpu" if torch.cuda.is_available() else None
     trainer = L.Trainer(
         logger=logger,
         callbacks=callbacks,
-        max_epochs=50,
+        max_epochs=max_epochs,
         log_every_n_steps=3,
-        accelerator=accelerator,
         precision=16,
     )
     trainer.fit(model, data_module)
diff --git a/src/phenocam_snow/utils.py b/src/phenocam_snow/utils.py
index 2f44ef4..7fe73f2 100644
--- a/src/phenocam_snow/utils.py
+++ b/src/phenocam_snow/utils.py
@@ -1,14 +1,15 @@
+import multiprocessing
 import os
 import random
 import re
 from datetime import datetime
 from io import BytesIO
+from multiprocessing.managers import ListProxy
 from pathlib import Path
 
 import pandas as pd
 import requests
 from PIL import Image
-from tqdm import tqdm
 
 
 def get_site_names() -> list[str]:
@@ -108,7 +109,7 @@ def download(
     n_photos: int | None = None,
     log_filename: str | Path | None = None,
 ) -> bool:
-    """Downloads photos taken in some time range at a given site.
+    """Downloads photos at a given site.
 
     :param site_name: The name of the site to download from.
     :type site_name: str
@@ -154,30 +155,31 @@ def download(
 
     write_info(f"Retrieved {site_name}'s per-month URLs")
 
-    image_urls = []
-    year_month_str = rf"https:\/\/phenocam.nau.edu\/webcam\/browse\/{site_name}\/(?P<year>.+)\/(?P<month>.+)"
-    year_month_pattern = re.compile(year_month_str)
-    date_pattern = re.compile(year_month_str + r"\/(?P<date>.+)")
+    total_processes = 8
+    process_manager = multiprocessing.Manager()
+    list_proxy: ListProxy[str] = process_manager.list()
+    print(f"Process manager started {total_processes} processes.")
+
+    processes = []
+    for process_id in range(total_processes):
+        months_for_this_process = site_month_urls[process_id::total_processes]
+        process = multiprocessing.Process(
+            target=_download_worker,
+            args=(process_id, site_name, months_for_this_process, list_proxy),
+        )
+        processes.append(process)
+        process.start()
 
-    # TODO:implement multiprocessing to hasten downloads
-    for month_url in tqdm(site_month_urls, desc="months"):
-        match = year_month_pattern.match(month_url)
-        year, month = match.group("year"), match.group("month")
-        try:
-            site_date_urls = get_site_dates(site_name, year, month)
-        except Exception as e:
-            write_error(f"Call to get_site_dates failed with {e.__class__.__name__}")
+    for current_process in processes:
+        current_process.join()
+
+    image_urls = []
+    for item in list(list_proxy):
+        if item.endswith("jpg"):
+            image_urls.append(item)
+        else:
+            write_error(item)
             return False
-        for date_url in tqdm(site_date_urls, desc="dates", leave=False):
-            match = date_pattern.match(date_url)
-            date = match.group("date")
-            try:
-                image_urls.extend(get_site_images(site_name, year, month, date))
-            except Exception as e:
-                write_error(
-                    f"Call to get_site_images failed with {e.__class__.__name__}"
-                )
-                return False
 
     random.shuffle(image_urls)
     n_downloaded = 0
@@ -208,6 +210,26 @@ def download(
     return True
 
 
+def _download_worker(
+    process_id: int, site_name: str, my_month_urls: list[str], list_proxy: ListProxy
+) -> None:
+    print(f"Process #{process_id} assigned {len(my_month_urls)} months to download")
+    year_month_str = rf"https:\/\/phenocam.nau.edu\/webcam\/browse\/{site_name}\/(?P<year>.+)\/(?P<month>.+)"
+    year_month_pattern = re.compile(year_month_str)
+    date_pattern = re.compile(year_month_str + r"\/(?P<date>.+)")
+    try:
+        for month_url in my_month_urls:
+            match = year_month_pattern.match(month_url)
+            year, month = match.group("year"), match.group("month")
+            site_date_urls = get_site_dates(site_name, year, month)
+            for date_url in site_date_urls:
+                match = date_pattern.match(date_url)
+                date = match.group("date")
+                list_proxy.extend(get_site_images(site_name, year, month, date))
+    except Exception as e:
+        list_proxy.extend(str(e))
+
+
 def download_from_log(
     source_log: str | Path, save_to: str | Path, log_filename: str | Path | None = None
 ) -> None:
@@ -313,12 +335,12 @@ def label_images(
         for fpath in dircat.glob("*.jpg"):
             data.append((os.path.basename(fpath), os.path.basename(dircat)))
     df = pd.DataFrame(data, columns=["filename", "label"])
-    with open(img_dir / save_to, "w+") as f:
+    with open(save_to, "w+") as f:
         f.write(f"# Site: {site_name}\n")
         f.write("# Categories:\n")
         for i, cat in enumerate(categories):
             f.write(f"# {i}. {cat}\n")
-    df.to_csv(img_dir / save_to, mode="a", index=False)
+    df.to_csv(save_to, mode="a", index=False)
 
     for item in img_dir.glob("*"):
         if item.is_dir():