update add_batch documention

README.md

@@ -30,31 +30,26 @@ pip install evaluate git+https://github.com/SEA-AI/seametrics@develop
 
 ### Basic Usage
 
-Here's how to quickly evaluate your object detection models using SEA-AI/det-metrics:
+Here's how to quickly evaluate your object detection models using SEA-AI/box-metrics:
 
 ```python
 import evaluate
 
 # Define your predictions and references (dict values can also by numpy arrays)
-predictions = [
-    {
-        "boxes": [[449.3, 197.75390625, 6.25, 7.03125], [334.3, 181.58203125, 11.5625, 6.85546875]],
-        "labels": [0, 0],
-        "scores": [0.153076171875, 0.72314453125],
-    }
-]
+predictions = {
+              "model1": [torch.tensor[n,6], torch.tensor[n,6]],
+              "model2": [torch.tensor[n,6], torch.tensor[n,6]]
+              }
 
-references = [
-    {
-        "boxes": [[449.3, 197.75390625, 6.25, 7.03125], [334.3, 181.58203125, 11.5625, 6.85546875]],
-        "labels": [0, 0],
-        "area": [132.2, 83.8],
-    }
-]
+#predictions box format: x1, y1, x2, y2, conf, label  (torch metrics format)
+
+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.add(prediction=predictions, reference=references)
+module.add_batch(prediction=predictions, reference=references, sequence_name="sequence")
 results = module.compute()
 
 print(results)
@@ -62,19 +57,11 @@ print(results)
 
 This will output the evaluation metrics for your detection model.
 ```
-{'all': {'range': [0, 10000000000.0],
-  'iouThr': '0.00',
-  'maxDets': 100,
-  'tp': 2,
-  'fp': 0,
-  'fn': 0,
-  'duplicates': 0,
-  'precision': 1.0,
-  'recall': 1.0,
-  'f1': 1.0,
-  'support': 2,
-  'fpi': 0,
-  'nImgs': 1}
+{'sequence': {'model1':
+  {'iou': '0.6',
+  'bep': 0.5,
+  ...
+  }}}
 ```
 
 ## FiftyOne Integration