diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d079751..ea8ac2e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -22,6 +22,8 @@ jobs:
       - name: Install pipenv
         run: pip install pipenv
       - name: Install dependencies
-        run: pipenv install --dev
+        run: |
+          pipenv install --dev
+          pipenv run pip install torch torchvision
       - name: Check formatting and types
         run: pipenv run qa
diff --git a/Pipfile b/Pipfile
index 91c3400..ccd54f2 100644
--- a/Pipfile
+++ b/Pipfile
@@ -4,15 +4,14 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-torch = "*"
 numpy = "*"
-torchvision = "*"
 pillow = "*"
 tqdm = "*"
 opencv-python = "*"
 shapely = "*"
 wandb = "*"
 pylev = "*"
+onnx = "*"
 
 [dev-packages]
 black = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index 7872ba4..5a5a717 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "ad9f9b9aca8bffa1d11ecd669d3a9ddd24242484c2c86bd671cb89a3b6a4b3f8"
+            "sha256": "bb8b04faefb42750b15d4d65406827b6f73d584b81c2e104e463bcac9242ff58"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -25,100 +25,115 @@
         },
         "certifi": {
             "hashes": [
-                "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3",
-                "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"
+                "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1",
+                "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==2022.12.7"
+            "version": "==2023.11.17"
         },
         "charset-normalizer": {
             "hashes": [
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-                "sha256:0be65ccf618c1e7ac9b849c315cc2e8a8751d9cfdaa43027d4f6624bd587ab7e",
-                "sha256:0c95f12b74681e9ae127728f7e5409cbbef9cd914d5896ef238cc779b8152373",
-                "sha256:0ca564606d2caafb0abe6d1b5311c2649e8071eb241b2d64e75a0d0065107e62",
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@@ -127,279 +142,232 @@
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@@ -610,146 +609,76 @@
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-                "sha256:b9fd45787ba8aa3f5e0a0a98920c1012c884622c6c920dbe98dbd05bc7c70fbf",
-                "sha256:c85928b9d5f83b23cee7d0efcb310172412fbf7cb9d9ce963bd67fd141781def",
-                "sha256:c9a3ac16efe9ec7d7381ddebcc022119794872abce99475345c5a61aa18c45ad",
-                "sha256:cfaf3895a9634e882bf9d2363fed5af8888802d670f58b279b0bece00e9a872d",
-                "sha256:e439798f819d49ba1c0bd9664427a05aab79bfba777a6db94fd4e56fae0cb849",
-                "sha256:f586c26118bc6e714ec58c09df0157fe2d9ee195c764f630eb0d8e7ccce72e69",
-                "sha256:f6fe02afde060bbeef044af7996f335fbe90b039ccf3f5eb8f16df8b20f77666"
+                "sha256:0269dfdea12442022e88043d2910429bed717b2d04523867a85dacce535916b8",
+                "sha256:07204d078e25327aad9ed2c64790d681238686bce254c910de640c7cc4fc3aa6",
+                "sha256:08b34e85170d368c37ca7bf81cf67ac863c9d1963b2c1780c39102187ec8dd62",
+                "sha256:1a95915c98d6e32ca43809d46d932e2abc5f1f7d582ffbe65a5b4d1588af7445",
+                "sha256:2588021038bd5ada078de606f2a804cadd0a3cc6a79cb3e9bb3a8bf581325a4c",
+                "sha256:2fa6a0e965779c8f2afb286f9ef798df770ba2b6cee063c650b96adec22c056a",
+                "sha256:34afe9da5056aa123b8bfda1664bfe6fb4e9c6f311d8e4a6eb089da9a9173bf9",
+                "sha256:3897ae5a21ca132efa219c029cce5e6bfc9c3d34ed7e892113d199c0b1b444a2",
+                "sha256:40657e1b78212d582a0edecafef133cf1dd02e6677f539b669db4746150d38f6",
+                "sha256:48b5760dcbfe5cf97fd4fba23946681f3a81514c6ab8a45b50da67ac8fbc6c7b",
+                "sha256:5242ecd9e990aeb995b6d03dc3b2d112d4a78f2083e5a8e86d566340ae80fec4",
+                "sha256:5cdc2e2195212208fbcae579b931407c1fa9997584f0a415421748aeafff1168",
+                "sha256:5d7b06ea8816cbd4becfe5f70accae953c53c0e53aa98730ceccb0395520ee5d",
+                "sha256:7258c27115c1e3b5de9ac6c4f9957e3ee2c02c0b39222a24dc7aa03ba0e986f5",
+                "sha256:854c06fb86fd854140f37fb24dbf10621f5dab9e3b0c29a690ba595e3d543024",
+                "sha256:a21725862d0e855ae05da1dd25e3825ed712eaaccef6b03017fe0853a01aa45e",
+                "sha256:a83fe522d9698d8f9a101b860b1ee154c1d25f8a82ceb807d319f085b2627c5b",
+                "sha256:b3d64db762eae4a5ce04b6e3dd745dcca0fb9560eb931a5be97472e38652a161",
+                "sha256:e298d588744efda02379521a19639ebcd314fba7a49be22136204d7ed1782717",
+                "sha256:e2c8dfa14677f90d976f68e0c923947ae68fa3961d61ee30976c388adc0b02c8",
+                "sha256:ecba2a15dfb2d97105be74bbfe5128bc5e9fa8477d8c46766505c1dda5883aac",
+                "sha256:fc1ec9aa6f4d98d022101e015261c056ddebe3da6a8ccfc2c792cbe0349d48b7"
             ],
             "index": "pypi",
-            "version": "==22.6.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==24.1.1"
         },
         "click": {
             "hashes": [
-                "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e",
-                "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48"
+                "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
+                "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==8.1.3"
+            "version": "==8.1.7"
         },
         "mypy": {
             "hashes": [
@@ -795,26 +724,27 @@
         },
         "packaging": {
             "hashes": [
-                "sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61",
-                "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"
+                "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5",
+                "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==23.1"
+            "version": "==23.2"
         },
         "pathspec": {
             "hashes": [
-                "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a",
-                "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"
+                "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08",
+                "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"
             ],
-            "version": "==0.9.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.12.1"
         },
         "platformdirs": {
             "hashes": [
-                "sha256:027d8e83a2d7de06bbac4e5ef7e023c02b863d7ea5d079477e722bb41ab25788",
-                "sha256:58c8abb07dcb441e6ee4b11d8df0ac856038f944ab98b7be6b27b2a3c7feef19"
+                "sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380",
+                "sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==2.5.2"
+            "markers": "python_version >= '3.8'",
+            "version": "==4.1.0"
         },
         "tomli": {
             "hashes": [
@@ -826,11 +756,12 @@
         },
         "types-pillow": {
             "hashes": [
-                "sha256:9781104ee2176f680576523fa2a2b83b134957aec6f4d62582cc9e74c93a60b4",
-                "sha256:d63743ef631e47f8d8669590ea976162321a9a7604588b424b6306533453fb63"
+                "sha256:322dbae32b4b7918da5e8a47c50ac0f24b0aa72a804a23857620f2722b03c858",
+                "sha256:c449b2c43b9fdbe0494a7b950e6b39a4e50516091213fec24ef3f33c1d017717"
             ],
             "index": "pypi",
-            "version": "==9.2.1"
+            "markers": "python_version >= '3.8'",
+            "version": "==10.2.0.20240125"
         },
         "typing-extensions": {
             "hashes": [
diff --git a/README.md b/README.md
index b571e60..ed65a16 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,8 @@
 This project contains tools for training PyTorch models for use with the
 [**Ocrs**](https://github.com/robertknight/ocrs/) OCR engine.
 
+## About the models
+
 The ocrs engine splits text detection and recognition into three phases, each
 of which corresponds to a different model in this repository:
 
@@ -22,3 +24,14 @@ All models can be exported to ONNX for downstream use.
 
 The models are trained exclusively on datasets which are a) open and b) have non-restrictive licenses. This currently includes:
 - [HierText](https://github.com/google-research-datasets/hiertext) (CC-BY-SA 4.0)
+
+## Pre-trained models
+
+Pre-trained models are available from [Hugging
+Face](https://huggingface.co/robertknight/ocrs) as PyTorch checkpoints,
+[ONNX](https://onnx.ai) and [RTen](https://github.com/robertknight/rten) models.
+
+## Training custom models
+
+See the [Training guide](docs/training.md) for a walk-through of the process to
+train models from scratch or fine-tune existing models.
diff --git a/docs/training.md b/docs/training.md
new file mode 100644
index 0000000..2248f7a
--- /dev/null
+++ b/docs/training.md
@@ -0,0 +1,190 @@
+# Training Ocrs models
+
+This document describes how to train models for use with
+[ocrs](https://github.com/robertknight/ocrs).
+
+## Prerequisites
+
+To train the models you will need:
+
+- Python 3.10 or later
+- A GPU. The initial training was done on NVidia A10G GPUs with 24 GB RAM, via
+  [AWS EC2 G5 instances](https://aws.amazon.com/ec2/instance-types/g5/) (the
+  smallest `g5.xlarge` size will work).
+- Optional: A Weights and Biases account (https://wandb.ai/) to track training progress
+
+## About Ocrs models
+
+Ocrs splits the OCR process into three stages:
+
+ 1. Text detection
+ 2. Layout analysis
+ 3. Text recognition
+
+Each of these stages corresponds to a separate PyTorch model. The layout
+analysis model is incomplete and is not currently used in Ocrs.
+
+You can mix and match default/pre-trained and custom models for the different
+stages. For example you may wish to use a pre-trained detection model but a
+custom recognition model.
+
+## Download the dataset
+
+Following the instructions in
+https://github.com/google-research-datasets/hiertext#getting-started, clone the
+HierText repository and download the training data.
+
+Note that you do **not** need to follow the step about decompressing the
+`.jsonl.gz` files. The training tools will do this for you.
+
+The compressed dataset is ~3.6 GB in total size.
+
+```
+# Clone the HierText repository. This contains the ground truth data.
+mkdir -p datasets/
+cd datasets
+git clone https://github.com/google-research-datasets/hiertext.git
+cd hiertext
+
+# Download the training, validation and test images.
+aws s3 --no-sign-request cp s3://open-images-dataset/ocr/train.tgz .
+aws s3 --no-sign-request cp s3://open-images-dataset/ocr/validation.tgz .
+aws s3 --no-sign-request cp s3://open-images-dataset/ocr/test.tgz .
+
+# Decompress the datasets.
+tar -xf train.tgz
+tar -xf validation.tgz
+tar -xf test.tgz
+```
+
+## Set up the training environment
+
+1. Install [Pipenv](https://pipenv.pypa.io/en/latest/)
+2. Install dependencies, except for PyTorch:
+
+	 ```
+	 pipenv install --dev
+	 ```
+
+3. Install the appropriate version of PyTorch for your system, in the virtualenv
+   created by pipenv:
+
+   ```
+   pipenv run pip install torch torchvision
+   ```
+
+   See https://pytorch.org/get-started/locally/ for an appropriate pip command
+   depending on your platform and GPU.
+
+4. Start a dummy training run of text detection training to verify everything is working:
+
+	 ```
+	 pipenv run python -m ocrs_models.train_detection hiertext datasets/hiertext/ --max-images 100
+	 ```
+	
+   Wait for one successful epoch of training and validation to complete and then
+   exit the process with Ctrl+C.
+
+## Set up Weights and Biases integration (optional)
+
+The ocrs-models training scripts support tracking training progress using
+[Weights and Biases](https://wandb.ai). To enable this you will need to create
+an account and then set the `WANDB_API_KEY` environment variable before running
+training scripts:
+
+```
+export WANDB_API_KEY=<your_api_key>
+```
+
+## Train the text detection model
+
+To launch a training run for the text detection model, run:
+
+```
+pipenv run python -m ocrs_models.train_detection hiertext datasets/hiertext/ \
+  --max-epochs 50 \
+  --batch-size 28
+```
+
+The `--batch-size` flag will need to be varied according to the amount of GPU
+memory you have available. One way to do this is to start with a small value,
+and then increase it until the training process is using most of the available
+GPU memory. The above value was used with a GPU that has 24 GB of memory. When
+training with an NVidia GPU, you can use the `nvidia-smi` tool to get memory
+usage statistics.
+
+To fine-tune an existing model, pass the `--checkpoint` flag to specify the
+pre-trained model to start with.
+
+### Export the text detection model
+
+As training progresses, the latest checkpoint will be saved to
+`text-detection-checkpoint.pt`. Once training completes, you can export the
+model to ONNX via:
+
+```
+pipenv run python -m ocrs_models.train_detection hiertext datasets/hiertext/ \
+  --checkpoint text-detection-checkpoint.pt \
+  --export text-detection.onnx
+```
+
+### Convert the text detection model
+
+To use the exported ONNX model with Ocrs, you will need to convert it to
+the `.rten` format used by [RTen][rten].
+
+See the [RTen README](https://github.com/robertknight/rten#getting-started)
+for current instructions on how to do this.
+
+To use the converted model with the `ocrs` CLI tool, you can either pass the
+model path via CLI arguments, or replace the default models in the cache
+directory (`~/.cache/ocrs`). Example using CLI arguments:
+
+```sh
+ocrs --detect-model custom-detection-model.rten image.jpg
+```
+
+[rten]: https://github.com/robertknight/rten
+
+## Train the text recognition model
+
+To launch a training run for the text recognition model, run:
+
+```
+pipenv run python -m ocrs_models.train_rec hiertext datasets/hiertext/ \
+  --max-epochs 50 \
+  --batch-size 250
+```
+
+The `--batch-size` flag will need to be varied according to the amount of GPU
+memory you have available. One way to do this is to start with a small value,
+and then increase it until the training process is using most of the available
+GPU memory. The above value was used with a GPU that has 24 GB of memory.
+
+To fine-tune an existing model, pass the `--checkpoint` flag to specify the
+pre-trained model to start with.
+
+### Export the text recognition model
+
+As training progresses, the latest checkpoint will be saved to
+`text-rec-checkpoint.pt`. Once training completes, you can export the model to
+ONNX via:
+
+```
+pipenv run python -m ocrs_models.train_rec hiertext datasets/hiertext/ \
+  --checkpoint text-rec.pt \
+  --export text-recognition.onnx
+```
+
+### Convert the text recognition model
+
+To use the exported ONNX models with Ocrs, convert it to `.rten` format using
+the same process as for the detection model.
+
+To use the converted model with the `ocrs` CLI tool, you can either pass the
+model path via CLI arguments, or replace the default models in the cache
+directory (`~/.cache/ocrs`). Example using CLI arguments:
+
+```sh
+ocrs --rec-model custom-recognition-model.rten image.jpg
+```
diff --git a/ocrs_models/eval_detection.py b/ocrs_models/eval_detection.py
index 8037819..1cfd458 100644
--- a/ocrs_models/eval_detection.py
+++ b/ocrs_models/eval_detection.py
@@ -22,9 +22,6 @@ def main():
     parser.add_argument("model")
     parser.add_argument("image")
     parser.add_argument("out_basename")
-    parser.add_argument(
-        "--export", type=str, help="Export model as ONNX after evaluation"
-    )
     args = parser.parse_args()
 
     model = DetectionModel()
@@ -51,9 +48,6 @@ def main():
         pred_masks = model(img)
     end = time.time()
 
-    if args.export:
-        torch.onnx.export(model, img, args.export)
-
     print(f"Predicted text in {end - start:.2f}s", file=sys.stderr)
 
     pred_masks = pred_masks[0]  # Remove dummy batch dimension
diff --git a/ocrs_models/postprocess.py b/ocrs_models/postprocess.py
index 4755702..6893def 100644
--- a/ocrs_models/postprocess.py
+++ b/ocrs_models/postprocess.py
@@ -79,6 +79,29 @@ def expand_quads(quads: torch.Tensor, dist: float) -> torch.Tensor:
     return torch.stack([expand_quad(quad, dist) for quad in quads])
 
 
+def lines_intersect(a_start: float, a_end: float, b_start: float, b_end: float) -> bool:
+    """
+    Return true if the lines (a_start, a_end) and (b_start, b_end) intersect.
+    """
+    if a_start <= b_start:
+        return a_end > b_start
+    else:
+        return b_end > a_start
+
+
+def bounds_intersect(
+    a: tuple[float, float, float, float], b: tuple[float, float, float, float]
+) -> bool:
+    """
+    Return true if the rects defined by two (min_x, min_y, max_x, max_y) tuples intersect.
+    """
+    a_min_x, a_min_y, a_max_x, a_max_y = a
+    b_min_x, b_min_y, b_max_x, b_max_y = b
+    return lines_intersect(a_min_x, a_max_x, b_min_x, b_max_x) and lines_intersect(
+        a_min_y, a_max_y, b_min_y, b_max_y
+    )
+
+
 def box_match_metrics(pred: torch.Tensor, target: torch.Tensor) -> dict[str, float]:
     """
     Compute metrics for quality of matches between two sets of rotated rects.
@@ -99,12 +122,22 @@ def box_match_metrics(pred: torch.Tensor, target: torch.Tensor) -> dict[str, flo
     # Areas of unions of predictions and targets
     union = torch.zeros((len(pred), len(target)))
 
+    # Get bounding boxes of polys for a cheap intersection test.
+    pred_polys_bounds = [poly.bounds for poly in pred_polys]
+    target_polys_bounds = [poly.bounds for poly in target_polys]
+
     pred_areas = torch.zeros((len(pred),))
     for pred_index, pred_poly in enumerate(pred_polys):
         pred_areas[pred_index] = pred_poly.area
+        pred_bounds = pred_polys_bounds[pred_index]
+
         for target_index, target_poly in enumerate(target_polys):
-            if not pred_poly.intersects(target_poly):
+            # Do a cheap intersection test and skip computing the actual
+            # union/intersection if that fails.
+            target_bounds = target_polys_bounds[target_index]
+            if not bounds_intersect(pred_bounds, target_bounds):
                 continue
+
             pt_intersection = pred_poly.intersection(target_poly)
             intersection[pred_index, target_index] = pt_intersection.area
 
diff --git a/ocrs_models/train_detection.py b/ocrs_models/train_detection.py
index 33b08bd..d4e7b36 100644
--- a/ocrs_models/train_detection.py
+++ b/ocrs_models/train_detection.py
@@ -12,6 +12,7 @@
 from torchvision.transforms.functional import resize, to_pil_image
 import torchvision.transforms as transforms
 from tqdm import tqdm
+import wandb
 
 from .datasets import DDI100, HierText
 from .models import DetectionModel
@@ -84,6 +85,7 @@ def train(
 
         img = img.to(device)
         masks = masks.to(device)
+
         start = time.time()
 
         pred_masks = model(img)
@@ -172,9 +174,12 @@ def test(
 
             test_loss += loss_fn(pred_masks, masks).item()
 
-            for item_index, pred_mask in enumerate(pred_masks):
-                pred_quads = extract_cc_quads(binarize_mask(pred_mask).cpu())
-                target_quads = extract_cc_quads(binarize_mask(masks[item_index]).cpu())
+            bin_pred_masks = binarize_mask(pred_masks).cpu()
+            bin_masks = binarize_mask(masks).cpu()
+
+            for item_index, bin_pred_mask in enumerate(bin_pred_masks):
+                pred_quads = extract_cc_quads(bin_pred_mask)
+                target_quads = extract_cc_quads(bin_masks[item_index])
                 metrics.append(box_match_metrics(pred_quads, target_quads))
 
             if save_debug_images:
@@ -233,6 +238,15 @@ def balanced_cross_entropy_loss(
     pos_mask = target > 0.5
     neg_mask = target < 0.5
 
+    # Clamp target values to ensure they are valid for use with BCE loss.
+    #
+    # The PyTorch transforms used for data augmentation can sometimes result in
+    # values slightly outside the [0, 1] range.
+    #
+    # We assume the predictions have been generated via a sigmoid or similar
+    # that guarantees values in [0, 1].
+    target = target.clamp(0.0, 1.0)
+
     pixel_loss = F.binary_cross_entropy(pred, target, reduction="none")
 
     pos_loss = pos_mask * pixel_loss
@@ -255,7 +269,7 @@ def prepare_transform(mask_size: tuple[int, int], augment) -> nn.Module:
     :param mask_size: HxW output image size
     :param augment: Whether to apply randomized data augmentation
     """
-    resize_transform = transforms.Resize(mask_size)
+    resize_transform = transforms.Resize(mask_size, antialias=False)
     if not augment:
         return resize_transform
 
@@ -288,6 +302,10 @@ def main():
         action="store_true",
         help="Save debugging images during training",
     )
+    parser.add_argument("--export", type=str, help="Export model to ONNX format")
+    parser.add_argument(
+        "--max-epochs", type=int, help="Maximum number of epochs to train for"
+    )
     parser.add_argument(
         "--max-images", type=int, help="Maximum number of images to load"
     )
@@ -314,6 +332,10 @@ def main():
     batch_size = args.batch_size
     max_images = args.max_images
 
+    # Set to aid debugging of initial text detection model
+    pytorch_seed = 1234
+    torch.manual_seed(pytorch_seed)
+
     if max_images:
         validation_max_images = max(10, int(max_images * 0.1))
     else:
@@ -325,7 +347,11 @@ def main():
         args.data_dir, transform=transform, train=True, max_images=max_images
     )
     train_dataloader = DataLoader(
-        train_dataset, batch_size=batch_size, shuffle=True, num_workers=2
+        train_dataset,
+        batch_size=batch_size,
+        shuffle=True,
+        pin_memory=True,
+        num_workers=2,
     )
 
     val_dataset = load_dataset(
@@ -334,10 +360,16 @@ def main():
         train=False,
         max_images=validation_max_images,
     )
-    val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
+    val_dataloader = DataLoader(
+        val_dataset, batch_size=batch_size, pin_memory=True, num_workers=2
+    )
 
-    print(f"Train images {len(train_dataset)} in {len(train_dataloader)} batches")
-    print(f"Validation images {len(val_dataset)} in {len(val_dataloader)} batches")
+    print(
+        f"Training dataset: images {len(train_dataset)} in {len(train_dataloader)} batches"
+    )
+    print(
+        f"Validation dataset: images {len(val_dataset)} in {len(val_dataloader)} batches"
+    )
 
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model = DetectionModel().to(device)
@@ -345,7 +377,7 @@ def main():
     optimizer = torch.optim.Adam(model.parameters())
 
     total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print(f"Model param count {total_params}")
+    print(f"Model param count: {total_params}")
 
     epochs_without_improvement = 0
     min_train_loss = 1.0
@@ -356,6 +388,23 @@ def main():
         checkpoint = load_checkpoint(args.checkpoint, model, optimizer, device)
         epoch = checkpoint["epoch"]
 
+    if args.export:
+        if not args.checkpoint:
+            raise Exception("ONNX export requires a checkpoint to load")
+
+        test_batch = next(iter(val_dataloader))
+        test_image = test_batch["image"][0:1]
+
+        torch.onnx.export(
+            model,
+            test_image,
+            args.export,
+            input_names=["image"],
+            output_names=["mask"],
+            dynamic_axes={"image": {0: "batch"}, "mask": {0: "batch"}},
+        )
+        return
+
     if args.validate_only:
         if not args.checkpoint:
             parser.exit(
@@ -374,7 +423,21 @@ def main():
         print("Validation metrics:", format_metrics(val_metrics))
         return
 
-    while True:
+    # Enable experiment tracking via Weights and Biases if API key set.
+    enable_wandb = bool(os.environ.get("WANDB_API_KEY"))
+    if enable_wandb:
+        wandb.init(
+            project="text-detection",
+            config={
+                "batch_size": args.batch_size,
+                "dataset_size": len(train_dataset),
+                "model_params": total_params,
+                "pytorch_seed": pytorch_seed,
+            },
+        )
+        wandb.watch(model)
+
+    while args.max_epochs is None or epoch < args.max_epochs:
         train_loss = train(
             epoch,
             device,
@@ -396,10 +459,21 @@ def main():
         )
         print(f"Epoch {epoch} validation metrics:", format_metrics(val_metrics))
 
+        if enable_wandb:
+            wandb.log(
+                {
+                    "train_loss": train_loss,
+                    "val_loss": val_loss,
+                    "val_metrics": val_metrics,
+                }
+            )
+
         if train_loss < min_train_loss:
             min_loss = train_loss
             epochs_without_improvement = 0
-            save_checkpoint("checkpoint.pt", model, optimizer, epoch=epoch)
+            save_checkpoint(
+                "text-detection-checkpoint.pt", model, optimizer, epoch=epoch
+            )
         else:
             epochs_without_improvement += 1
 
diff --git a/ocrs_models/train_rec.py b/ocrs_models/train_rec.py
index d097133..1a63181 100644
--- a/ocrs_models/train_rec.py
+++ b/ocrs_models/train_rec.py
@@ -352,7 +352,7 @@ def main():
     )
 
     total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    print(f"Modal param count {total_params}")
+    print(f"Model param count {total_params}")
 
     epoch = 0