finished better documentation

README.md

@@ -48,7 +48,7 @@ references = [torch.tensor[n,5], torch.tensor[n,5]]
 #refernces box format: label, x1, y1, x2, y2 (torch metrics format)
 
 # Load SEA-AI/det-metrics and evaluate
-module = evaluate.load("SEA-AI/det-metrics")
+module = evaluate.load("SEA-AI/box-metrics")
 module.add_batch(prediction=predictions, reference=references, sequence_name="sequence")
 results = module.compute()
 
@@ -77,15 +77,15 @@ logging.basicConfig(level=logging.WARNING)
 
 # Configure your dataset and model details
 processor = PayloadProcessor(
-    dataset_name="SAILING_DATASET_QA",
+    dataset_name="SENTRY_VIDEOS_DATASET_QA",
     gt_field="ground_truth_det",
-    models=["yolov5n6_RGB_D2304-v1_9C"],
-    sequence_list=["Trip_14_Seq_1"],
-    data_type="rgb",
+    models=["ahoy-IR-b2-whales__XAVIER-AGX-JP46_CNN"],
+    sequence_list=["Sentry_2022_11_PROACT_CELADON_7.5M_MOB_2022_11_25_12_12_39"],
+    data_type="thermal",
 )
 
 # Evaluate using SEA-AI/det-metrics
-module = evaluate.load("SEA-AI/det-metrics")
+module = evaluate.load("SEA-AI/box-metrics")
 module.add_payload(processor.payload)
 results = module.compute()
 
@@ -93,63 +93,16 @@ print(results)
 ```
 
 ```console
-{'all': {'range': [0, 10000000000.0],
-  'iouThr': '0.00',
-  'maxDets': 100,
-  'tp': 89,
-  'fp': 13,
-  'fn': 15,
-  'duplicates': 1,
-  'precision': 0.8725490196078431,
-  'recall': 0.8557692307692307,
-  'f1': 0.8640776699029126,
-  'support': 104,
-  'fpi': 0,
-  'nImgs': 22}}
-```
-
-## Metric Settings
-
-Customize your evaluation by specifying various parameters when loading SEA-AI/det-metrics:
-
-- **area_ranges_tuples**: Define different area ranges for metrics calculation.
-- **bbox_format**: Set the bounding box format (e.g., `"xywh"`).
-- **iou_threshold**: Choose the IOU threshold for determining correct detections.
-- **class_agnostic**: Specify whether to calculate metrics disregarding class labels.
-
-```python
-area_ranges_tuples = [
-    ("all", [0, 1e5**2]),
-    ("small", [0**2, 6**2]),
-    ("medium", [6**2, 12**2]),
-    ("large", [12**2, 1e5**2]),
-]
-
-module = evaluate.load(
-    "SEA-AI/det-metrics",
-    iou_threshold=[0.00001],
-    area_ranges_tuples=area_ranges_tuples,
-)
+{'Sentry_2022_11_PROACT_CELADON_7.5M_MOB_2022_11_25_12_12_39': {'ahoy-IR-b2-whales__XAVIER-AGX-JP46_CNN':
+  {'iou': '0.6',
+  'bep': 0.5,
+  ...
+  }}}
 ```
 
-## Output Values
-
-SEA-AI/det-metrics provides a detailed breakdown of performance metrics for each specified area range:
-
-- **range**: The area range considered.
-- **iouThr**: The IOU threshold applied.
-- **maxDets**: The maximum number of detections evaluated.
-- **tp/fp/fn**: Counts of true positives, false positives, and false negatives.
-- **duplicates**: Number of duplicate detections.
-- **precision/recall/f1**: Calculated precision, recall, and F1 score.
-- **support**: Number of ground truth boxes considered.
-- **fpi**: Number of images with predictions but no ground truths.
-- **nImgs**: Total number of images evaluated.
-
 ## Further References
 
 - **seametrics Library**: Explore the [seametrics GitHub repository](https://github.com/SEA-AI/seametrics/tree/main) for more details on the underlying library.
-- **Pycoco Tools**: SEA-AI/det-metrics calculations are based on [pycoco tools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools), a widely used library for COCO dataset evaluation.
 - **Understanding Metrics**: For a deeper understanding of precision, recall, and other metrics, read [this comprehensive guide](https://www.analyticsvidhya.com/blog/2020/09/precision-recall-machine-learning/).
 
 ## Contribution