a

my_metricv2.py

@@ -11,23 +11,26 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""TODO: Add a description here."""
 
 import evaluate
 import datasets
 import motmetrics as mm
 import numpy as np
 
-# TODO: Add BibTeX citation
 _CITATION = """\
 @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}
 }
 """
 
-# TODO: Add description of the module here
 _DESCRIPTION = """\
 The MOT Metrics module is designed to evaluate multi-object tracking (MOT) 
 algorithms by computing various metrics based on predicted and ground truth bounding 
@@ -35,7 +38,6 @@ boxes. It serves as a crucial tool in assessing the performance of MOT systems,
 aiding in the iterative improvement of tracking algorithms."""
 
 
-# TODO: Add description of the arguments of the module here
 _KWARGS_DESCRIPTION = """
 
 Calculates how good are predictions given some references, using certain scores
@@ -101,11 +103,10 @@ Examples:
 
     >>> results = module._compute(predictions=predicted, references=ground_truth, max_iou=0.5)
     >>> print(results)
-    {'idf1': {0: 0.8421052631578947}, 'idp': {0: 0.8888888888888888}, 'idr': {0: 0.8}, 'recall': {0: 0.8}, 
-    'precision': {0: 0.8888888888888888}, 'num_unique_objects': {0: 3}, 'mostly_tracked': {0: 2}, 
-    'partially_tracked': {0: 1}, 'mostly_lost': {0: 0}, 'num_false_positives': {0: 1}, 'num_misses': {0: 2}, 
-    'num_switches': {0: 0}, 'num_fragmentations': {0: 0}, 'mota': {0: 0.7}, 'motp': {0: 0.02981870229007634}, 
-    'num_transfer': {0: 0}, 'num_ascend': {0: 0}, 'num_migrate': {0: 0}}
+    {'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}
 """
 
 
@@ -130,8 +131,6 @@ class MyMetricv2(evaluate.Metric):
                                 datasets.Sequence(datasets.Value("float"))
                             )
             }),
-            # Homepage of the module for documentation
-            homepage="http://module.homepage",
             # Additional links to the codebase or references
             codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
             reference_urls=["http://path.to.reference.url/new_module"]

requirements.txt

@@ -1,4 +1,3 @@
 git+https://github.com/huggingface/evaluate@main
 numpy
-motmetrics
-scikit-learn
+motmetrics

tests.py

@@ -24,14 +24,14 @@ test_cases = [
                             [5, 1, 30, 40, 50, 60],
                             [5, 2, 70, 80, 90, 100]
                         ]],
-        "result": {'idf1': {0: 0.8421052631578947}, 'idp': {0: 0.8888888888888888},
-                    'idr': {0: 0.8}, 'recall': {0: 0.8}, 'precision': {0: 0.8888888888888888},
-                    'num_unique_objects': {0: 3}, 'mostly_tracked': {0: 2},
-                    'partially_tracked': {0: 1}, 'mostly_lost': {0: 0},
-                    'num_false_positives': {0: 1}, 'num_misses': {0: 2},
-                    'num_switches': {0: 0}, 'num_fragmentations': {0: 0},
-                    'mota': {0: 0.7}, 'motp': {0: 0.02981870229007634},
-                    'num_transfer': {0: 0}, 'num_ascend': {0: 0},
-                    'num_migrate': {0: 0}}
+        "result": {'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}
     },   
 ]