feat: Add PPG model.

docs/assets/zoo/seg-ppg-2-tcn-sm/confusion_matrix_test.html

@@ -0,0 +1,2 @@
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docs/assets/zoo/seg-ppg-2-tcn-sm/results.md

@@ -0,0 +1,3 @@
+| NAME                 | DATASET                  | FS    | DURATION | # CLASSES | MODEL         | PARAMS | FLOPS   | METRIC    |
+| -------------------- | ------------------------ | ----- | -------- | --------- | ------------- | ------ | ------- | --------- |
+| __SEG-PPG-2-TCN-SM__ | Synthetic                | 100Hz | 2.5s     | 2         | TCN           | 4K     | 1.43M   | 98.6% F1  |

docs/guides/byot.ipynb

@@ -430,7 +430,7 @@
     "        if params.architecture is None:\n",
     "            raise ValueError(\"Model architecture must be specified\")\n",
     "        model = hk.ModelFactory.get(params.architecture.name)(\n",
-    "            x=inputs,\n",
+    "            inputs=inputs,\n",
     "            params=params.architecture.params,\n",
     "            num_classes=params.num_classes,\n",
     "        )\n",

docs/models/byom.md

@@ -39,7 +39,7 @@ The model factory can be extended to include custom models. This is useful when
     ```py linenums="1"
     inputs = keras.Input(shape=(100,))
     model = hk.ModelFactory.get("custom-model")(
-        x=inputs,
+        inputs=inputs,
         params={
             "layers": [
                 {"units": 64, "activation": "relu"},

docs/models/index.md

@@ -65,14 +65,14 @@ Given the following configuration file `configuration.json`:
 }
 ```
 
-### Defining a model in code
-
-The model can be created using the following command:
+The model can be then be created using the following command:
 
 ```bash
 heartkit --mode train --task rhythm --config config.json
 ```
 
+### Defining a model in code
+
 Alternatively, the model can be created directly in code using the following snippet:
 
 ```py linenums="1"
@@ -103,7 +103,7 @@ inputs = keras.Input(shape=(256,1), dtype="float32")
 num_classes = 5
 
 model = hk.ModelFactory.get(architecture["name"])(
-    x=inputs,
+    inputs=inputs,
     params=architecture["params"],
     num_classes=num_classes,
 )

docs/modes/configuration.md

@@ -1,4 +1,4 @@
-# Configuration Parameters
+# :material-code-json: Configuration Parameters
 
 For each mode, common configuration parameters, [HKTaskParams](#hktaskparams), are required to run the task. These parameters are used to define the task, datasets, model, and other settings. Rather than defining separate configuration files for each mode, a single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
 

docs/modes/demo.md

@@ -1,4 +1,4 @@
-# Task-Level Demo
+# :material-hexagon-multiple: Task-Level Demo
 
 ## <span class="sk-h2-span">Introduction </span>
 
@@ -48,7 +48,6 @@ end
 F ==> G
 ```
 
-
 ---
 
 ## <span class="sk-h2-span">Backend Inference Engines</span>

docs/modes/download.md

@@ -1,4 +1,4 @@
-# Download Datasets
+# :material-download: Download Datasets
 
 ## <span class="sk-h2-span">Introduction</span>
 

docs/modes/evaluate.md

@@ -1,12 +1,11 @@
-# Model Evaluation
+# :material-ab-testing: Model Evaluation
 
 ## <span class="sk-h2-span">Introduction </span>
 
 Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized via CLI configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score. A number of results and metrics will be generated and saved to the `job_dir`.
 
 <div class="annotate" markdown>
 
-
 1. Load the configuration data (e.g. `configuration.json` (1))
 1. Load the desired datasets (e.g. `PtbxlDataset`)
 1. Load the corresponding task dataloaders (e.g. `PtbxlDataLoader`)

docs/modes/export.md

@@ -1,4 +1,4 @@
-# Model Exporting
+# :material-export: Model Exporting
 
 ## <span class="sk-h2-span">Introduction </span>
 

docs/modes/train.md

@@ -1,4 +1,4 @@
-# Model Training
+# :material-chart-ppf: Model Training
 
 ## <span class="sk-h2-span">Introduction </span>
 

docs/tasks/index.md

@@ -1,4 +1,4 @@
-# Tasks
+# :simple-task: Tasks
 
 ## <span class="sk-h2-span">Introduction</span>
 
@@ -8,6 +8,7 @@ HeartKit provides several built-in __heart-monitoring__ tasks. Each task is desi
   ![Task Diagram](../assets/tasks/heartkit-task-diagram.svg){ width="600" }
 </figure>
 
+---
 
 ## <span class="sk-h2-span">Available Tasks</span>
 
@@ -35,6 +36,8 @@ Multi-label diagnostic classification is the process of assigning diagnostic lab
 
 [Bring-Your-Own-Task (BYOT)](./byot.md) is a feature that allows users to create custom tasks by extending the [HKTask](/heartkit/api/heartkit/tasks/task) base class and registering it with the `TaskFactory`. This feature is useful for addressing specific use cases that are not covered by the built-in tasks.
 
+---
+
 ## <span class="sk-h2-span">Task Factory</span>
 
 The task factory, `TaskFactory`, provides a convenient way to access the built-in tasks. The factory is a thread-safe singleton class that provides a single point of access to the tasks via the tasks' slug names. The benefit of using the factory is it allows registering custom tasks that can then be used just like built-in tasks.

docs/usage/cli.md

@@ -1,4 +1,4 @@
-# CLI Usage
+# :octicons-terminal-24: HeartKit CLI
 
 <div class="termy">
 
@@ -102,7 +102,7 @@ The [train](../modes/train.md) command is used to train a HeartKit model for the
 
 ## [Evaluate](../modes/evaluate.md)
 
-The [evaluate](../modes/evaluate.md) command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file.Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) to see supported options.
+The [evaluate](../modes/evaluate.md) command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file. Please refer to [HKTaskParams](../modes/configuration.md#hktaskparams) to see supported options.
 
 !!! example "CLI"
 

docs/usage/python.md

@@ -1,4 +1,4 @@
-# Python Usage
+# :simple-python: Python Usage
 
 __HeartKit__ python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in taks and custom tasks. In addition, custom datasets and model architectures can be created and registered with corresponding factories.
 
@@ -39,7 +39,7 @@ num_classes = 4
 model_params = dict(...)
 
 model = hk.ModelFactory.get('tcn')(
-    x=inputs,
+    inputs=inputs,
     params=model_params,
     num_classes=num_classes
 )

docs/zoo/arr-2-eff-sm.md

@@ -1,8 +1,9 @@
-# ARR-2-EFF-SM
+# 2-Class Arrhythmia Classification (ARR-2-EFF-SM)
+
 
 ## <span class="sk-h2-span">Overview</span>
 
-The following table provides the latest pre-trained models for 2-class arrhythmia classification. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+The following table provides the latest pre-trained model for 2-class arrhythmia classification. Below we also provide additional details including training configuration, performance metrics, and downloads.
 
 --8<-- "assets/zoo/arr-2-eff-sm/results.md"
 
@@ -17,7 +18,7 @@ The following table provides the latest pre-trained models for 2-class arrhythmi
 
 ## <span class="sk-h2-span">Class Mapping</span>
 
-The model is trained on raw ECG data and is able to discern normal sinus rhythm (NSR) from atrial fibrillation (AFIB) and atrial flutter (AFL).
+The model is trained on raw ECG data and is able to discern normal sinus rhythm (NSR) from atrial fibrillation (AFIB) and atrial flutter (AFL). The class mapping is as follows:
 
 
 | Base Class    | Target Class | Label     |
@@ -30,15 +31,14 @@ The model is trained on raw ECG data and is able to discern normal sinus rhythm
 
 The model is trained on the following datasets:
 
-- **[Icentia11k](../datasets/icentia11k.md)**
 - **[PTB-XL](../datasets/ptbxl.md)**
 - **[LSAD](../datasets/lsad.md)**
 
 ---
 
 ## <span class="sk-h2-span">Model Performance</span>
 
-The confusion matrix for the model is depicted below.
+The confusion matrix on the test set for the model is depicted below.
 
 <div class="sk-plotly-graph-div">
 --8<-- "assets/zoo/arr-2-eff-sm/confusion_matrix_test.html"

docs/zoo/arr-4-eff-sm.md

@@ -1,15 +1,17 @@
-# ARR-4-EFF-SM
+# 4-Class Arrhythmia Classification (ARR-4-EFF-SM)
 
 ## <span class="sk-h2-span">Overview</span>
 
-The following table provides the latest pre-trained models for 4-class arrhythmia classification. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+The following table provides the latest pre-trained model for 4-class arrhythmia classification. Below we also provide additional details including training configuration, performance metrics, and downloads.
 
 --8<-- "assets/zoo/rhythm/rhythm-model-zoo-table.md"
 
 ---
 
 ## <span class="sk-h2-span">Input</span>
 
+The model is trained on 5-second, raw ECG frames sampled at 100 Hz.
+
 - **Sensor**: ECG
 - **Location**: Wrist
 - **Sampling Rate**: 100 Hz
@@ -19,7 +21,7 @@ The following table provides the latest pre-trained models for 4-class arrhythmi
 
 ## <span class="sk-h2-span">Class Mapping</span>
 
-Identify rhythm into one of four categories: SR, SBRAD, AFIB, GSVT.
+The model is trained on raw ECG data and is able to discern normal sinus rhythm (SR) from sinus bradycardia (SBRAD), atrial fibrillation (AFIB), atrial flutter (AFL), supraventricular tachycardia (STACH), and general supraventricular tachycardia (GSVT). The class mapping is as follows:
 
 | Base Class     | Target Class | Label                     |
 | -------------- | ------------ | ------------------------- |

docs/zoo/beat-2-eff-sm.md

@@ -1,15 +1,17 @@
-# BEAT-2-EFF-SM
+# 2-Class Beat Classification (BEAT-2-EFF-SM)
 
 ## <span class="sk-h2-span">Overview</span>
 
-The following table provides the latest pre-trained models for 2-class beat classification task. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+The following table provides the latest pre-trained model for 2-class beat classification task. Below we also provide additional details including training configuration, performance metrics, and downloads.
 
 --8<-- "assets/zoo/beat-2-eff-sm/results.md"
 
 ---
 
 ## <span class="sk-h2-span">Input</span>
 
+The model is trained on 5-second, raw ECG frames sampled at 100 Hz.
+
 - **Sensor**: ECG
 - **Location**: Wrist
 - **Sampling Rate**: 100 Hz
@@ -19,7 +21,8 @@ The following table provides the latest pre-trained models for 2-class beat clas
 
 ## <span class="sk-h2-span">Class Mapping</span>
 
-The model is trained on raw ECG data and is able to classify normal sinus rhythm (NSR) and premature atrial/ventricular contractions (PAC/PVC).
+The model is able to classify normal sinus rhythm (NSR) and premature atrial/ventricular contractions (PAC/PVC).
+The class mapping is as follows:
 
 | Base Class    | Target Class | Label     |
 | ------------- | ------------ | --------- |