update readme

README.md

@@ -84,6 +84,8 @@ See [Output Values](#output-values) for more detailed information about the retu
 
 Integrate SEA-AI/det-metrics with FiftyOne datasets for enhanced analysis and visualization:
 
+### Class-agnostic Example
+
 ```python
 import evaluate
 import logging
@@ -129,6 +131,54 @@ This will output the following dictionary containing metrics for the detection m
 
 See [Output Values](#output-values) for more detailed information about the returned results structure, which includes metrics, eval, and params fields for each model passed as input.
 
+### Class-specific example
+```python
+import evaluate
+import logging
+from seametrics.payload.processor import PayloadProcessor
+
+logging.basicConfig(level=logging.WARNING)
+
+# Configure your dataset and model details
+processor = PayloadProcessor(
+    dataset_name="SAILING_DATASET_QA",
+    gt_field="ground_truth_det",
+    models=["yolov5n6_RGB_D2304-v1_9C", "tf1zoo_ssd-mobilenet-v2_agnostic_D2207"],
+    sequence_list=["Trip_14_Seq_1"],
+    data_type="rgb",
+    slices=["rgb"]
+)
+
+# Evaluate using SEA-AI/det-metrics
+module = evaluate.load("SEA-AI/det-metrics", payload=processor.payload, class_agnostic=False)
+print("Used labels: \n", module.label_mapping)
+results = module.compute()
+
+print("Results: \n", results)
+```
+
+```json
+Used labels:
+{
+    "SHIP": 0,
+    "FISHING_SHIP": 0,
+    "BOAT_WITHOUT_SAILS": 1,
+    ... 
+}
+Results:
+{
+    "yolov5n6_RGB_D2304-v1_9C": {
+        "metrics": ..., # metrics are arrays instead of single numbers, where the indices represent class 0, 1, etc. from the label mapping
+        "eval": ...,
+        "params": ...
+    },
+    "tf1zoo_ssd-mobilenet-v2_agnostic_D2207": {
+        "metrics": ...,
+        "eval": ...,
+        "params": ...
+    }
+}
+```
 
 ## Metric Settings