changed README

README.md

@@ -13,6 +13,76 @@ app_file: app.py
 pinned: false
 ---
 
-# Metric Card for MOT Metrics
+# How to Use
 
-***Module Card Instructions:*** *Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.*
+The MOT metrics takes two numeric arrays as input corresponding to the predictions and references bounding boxes:
+```python
+>>> import numpy as np
+>>> module = evaluate.load("SEA-AI/mot-metrics")
+>>> predicted =[[1,1,10,20,30,40,0.85],[2,1,15,25,35,45,0.78],[2,2,55,65,75,85,0.95]]
+>>> ground_truth = [[1, 1, 10, 20, 30, 40],[2, 1, 15, 25, 35, 45]]
+            
+>>> results = module._compute(predictions=predicted, references=ground_truth, max_iou=0.5)
+>>> results
+{'idf1': 0.8421052631578947, 'idp': 0.8888888888888888,
+'idr': 0.8, 'recall': 0.8, 'precision': 0.8888888888888888,
+'num_unique_objects': 3,'mostly_tracked': 2,
+'partially_tracked': 1, 'mostly_lost': 0,
+'num_false_positives': 1, 'num_misses': 2,
+'num_switches': 0, 'num_fragmentations': 0,
+'mota': 0.7, 'motp': 0.02981870229007634,
+'num_transfer': 0, 'num_ascend': 0,
+'num_migrate': 0}
+```
+
+## Input
+Each line of the **predictions** array is a list with the following format:
+```
+[frame ID, object ID, x, y, width, height, confidence]
+```
+
+Each line of the **references** array is a list with the following format:
+```
+[frame ID, object ID, x, y, width, height]
+```
+
+The `max_iou` parameter is used to filter out the bounding boxes with IOU less than the threshold. The default value is 0.5. This means that if a ground truth and a predicted bounding boxes IoU value is less than 0.5, then the predicted bounding box is not considered for association.
+
+## Output
+The output is a dictionary containing the following metrics:
+
+| Name                 | Description                                                                        |
+| :------------------- | :--------------------------------------------------------------------------------- |
+| idf1                 | ID measures: global min-cost F1 score.                                             |
+| idp                  | ID measures: global min-cost precision.                                            |
+| idr                  | ID measures: global min-cost recall.                                               |
+| recall               | Number of detections over number of objects.                                       |
+| precision            | Number of detected objects over sum of detected and false positives.               |
+| num_unique_objects   | Total number of unique object ids encountered.                                     |
+| mostly_tracked       | Number of objects tracked for at least 80 percent of lifespan.                     |
+| partially_tracked    | Number of objects tracked between 20 and 80 percent of lifespan.                   |
+| mostly_lost          | Number of objects tracked less than 20 percent of lifespan.                        |
+| num_false_positives  | Total number of false positives (false-alarms).                                    |
+| num_misses           | Total number of misses.                                                            |
+| num_switches         | Total number of track switches.                                                    |
+| num_fragmentations   | Total number of switches from tracked to not tracked.                              |
+| mota                 | Multiple object tracker accuracy.                                                  |
+| motp                 | Multiple object tracker precision.                                                 |
+
+
+## Citations
+```bibtex
+@InProceedings{huggingface:module,
+title = {A great new module},
+authors={huggingface, Inc.},
+year={2020}}
+
+@article{milan2016mot16,
+title={MOT16: A benchmark for multi-object tracking},
+author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
+journal={arXiv preprint arXiv:1603.00831},
+year={2016}}
+```
+
+## Further References
+- [Github Repository - py-motmetrics](https://github.com/cheind/py-motmetrics/tree/develop)