Usage of COCO Object Detection Metrics #186
Description
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System information
- Have I written custom code (as opposed to using a stock example script
provided in TensorFlow Model Analysis): No - OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Fedora 40
- TensorFlow Model Analysis installed from (source or binary): binary (pypi)
- TensorFlow Model Analysis version (use command below):'0.46.0'
- Python version: 3.10.14
- Jupyter Notebook version:jupyterlab==3.6.7
- Exact command to reproduce:
You can obtain the TensorFlow Model Analysis version with
python -c "import tensorflow_model_analysis as tfma; print(tfma.version.VERSION)"
Describe the problem
Describe the problem clearly here. Be sure to convey here why it's a bug in
TensorFlow Model Analysis or a feature request.
This might be an issue with documentation. I'm using yolov8 for an object detection task. Relevant technical details below. I would like to use COCO Object Detection Metrics in TFMA, but I'm unable to find any examples that show how to do this. I am able to visualize the distribution of input examples across classes using this:
eval_config = text_format.Parse(
"""
model_specs {
signature_name: "serving_default"
prediction_key: "predictions" # placeholder
label_key: "labels" # placeholder
}
slicing_specs {}
slicing_specs {
feature_keys: ["label"]
}
metrics_specs {
metrics {
class_name: "ExampleCount"
# config:'"iou_thresholds":[0.5], "class_id":0,'
# '"max_num_detections":100, "name":"iou0.5", "labels_to_stack":["bbox", "label"]'
}
}
metrics_specs {
output_names: ["label"]
}
""", tfma.EvalConfig())
Input:
[ins] In [21]: X.shape
Out[21]: TensorShape([4, 640, 640, 3])
Label:
[ins] In [23]: y.keys()
Out[23]: dict_keys(['boxes', 'classes'])
[ins] In [24]: y
Out[24]:
{'boxes': <tf.Tensor: shape=(4, 1, 4), dtype=float32, numpy=
array([[[270.22223, 421.30017, 715.66223, 600.74664]],
[[554.8021 , 578.6413 , 175.54286, 193.96402]],
[[612.69336, 459.9846 , 224.14223, 133.12 ]],
[[560.3555 , 471.36237, 251.44888, 65.99111]]], dtype=float32)>,
'classes': <tf.Tensor: shape=(4, 1), dtype=int64, numpy=
array([[6],
[6],
[3],
[2]])>}
Model compilation:
model.compile(
optimizer=optimizer, classification_loss="binary_crossentropy", box_loss="ciou"
)
Model prediction:
[ins] In [26]: model.predict(X).keys()
1/1 [==============================] - 0s 84ms/step
Out[26]: dict_keys(['boxes', 'confidence', 'classes', 'num_detections'])
[ins] In [28]: model.predict(X)['boxes']
1/1 [==============================] - 0s 92ms/step
Out[28]:
array([[[-431.99997 , -310.47818 , 544. , 393.79114 ],
[-400. , -426.98306 , 544. , 506.9677 ],
[-304. , -431.9941 , 544. , 511.95975 ],
...,
[ 594.22614 , 199.95639 , 148.6546 , 283.17026 ],
[ 502.5973 , 375.44495 , 205.77087 , 177.3197 ],
[ 491.90588 , 219.66116 , 257.7141 , 221.53989 ]],
[[-431.99713 , -337.42957 , 543.99713 , 423.6395 ],
[-368. , -431.72815 , 544. , 508.2921 ],
[-304. , -368.69293 , 544. , 448.2409 ],
...,
[-163.98816 , 172.93243 , 371.98816 , 457.06378 ],
[ -48.008606, 30.734238, 352.0086 , 241.25737 ],
[-422.85272 , 295.79327 , 534.8527 , 410.34576 ]],
[[-431.99936 , -312.31406 , 543.9994 , 406.8303 ],
[-368. , -354.40826 , 544. , 404.39636 ],
[-176. , -431.99796 , 544. , 488.12885 ],
...,
[ -1. , -1. , -1. , -1. ],
[ -1. , -1. , -1. , -1. ],
[ -1. , -1. , -1. , -1. ]],
[[-399.76962 , -394.83652 , 543.76965 , 482.42383 ],
[-303.99673 , -371.62524 , 543.9967 , 450.7735 ],
[-240. , -431.732 , 544. , 500.4899 ],
...,
[ 555.43005 , 307.50546 , 178.5935 , 307.58475 ],
[ 500.02817 , 306.5144 , 212.86801 , 227.49939 ],
[ -1. , -1. , -1. , -1. ]]],
dtype=float32)
[ins] In [29]: model.predict(X)['classes']
1/1 [==============================] - 0s 99ms/step
Out[29]:
array([[ 1, 1, 1, 1, 1, 2, 2, 7, 1, 1, 7, 2, 2, 1, 1, 7,
1, 7, 2, 1, 1, 1, 1, 1, 7, 1, 1, 1, 1, 1, 7, 7,
1, 1, 1, 1, 1, 1, 1, 7, 7, 1, 7, 1, 1, 1, 1, 7,
7, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1,
1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 7, 7,
7, 7, 7, 7, 7, 7, 4, 4, 7, 7, 4, 4, 2, 4, 2, 2,
2, 4, 4, 4],
[ 1, 1, 2, 2, 2, 7, 2, 1, 1, 7, 1, 1, 1, 1, 2, 1,
1, 1, 1, 7, 1, 1, 7, 1, 1, 2, 2, 1, 7, 1, 1, 1,
1, 1, 7, 7, 1, 2, 7, 2, 1, 1, 7, 2, 7, 1, 1, 1,
1, 2, 7, 7, 7, 1, 1, 1, 7, 1, 2, 1, 2, 2, 2, 2,
1, 7, 1, 2, 2, 2, 7, 7, 7, 1, 1, 7, 2, 7, 2, 2,
7, 7, 7, 7, 7, 2, 7, 7, 7, 2, 2, 7, 2, 7, 7, 7,
7, 7, 7, 1],
[ 1, 7, 2, 1, 7, 7, 1, 2, 7, 1, 1, 2, 1, 1, 2, 1,
7, 1, 1, 1, 1, 7, 1, 2, 1, 7, 7, 1, 1, 1, 1, 1,
1, 1, 1, 2, 7, 7, 7, 2, 1, 2, 1, 1, 1, 1, 2, 1,
1, 1, 1, 7, 1, 2, 2, 1, 7, 2, 2, 2, 2, 1, 1, 7,
1, 7, 2, 7, 1, 7, 7, 2, 2, 1, 7, 7, 2, 7, 7, 2,
1, 7, 1, 7, 7, 7, 7, 7, 2, 2, 7, 7, 2, -1, -1, -1,
-1, -1, -1, -1],
[ 7, 1, 2, 1, 7, 7, 7, 2, 7, 7, 2, 1, 7, 2, 7, 7,
1, 1, 1, 7, 1, 1, 1, 7, 2, 1, 1, 2, 7, 2, 2, 2,
2, 7, 1, 1, 2, 7, 2, 2, 7, 1, 7, 7, 1, 7, 2, 1,
2, 2, 7, 7, 2, 2, 7, 2, 2, 1, 7, 7, 7, 7, 1, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 7, 7, 2, 2, 7, 7,
2, 7, 2, 7, 2, 2, 2, 2, 2, 7, 2, 7, 2, 2, 2, 2,
7, 2, 2, -1]])
ins] In [32]: model.predict(X)['confidence'].shape
1/1 [==============================] - 0s 98ms/step
Out[32]: (4, 100)
[ins] In [34]: model.predict(X)['num_detections']
1/1 [==============================] - 0s 109ms/step
Out[34]: array([100, 100, 93, 99], dtype=int32)
Source code / logs
Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached. Try to provide a reproducible test case that is the bare
minimum necessary to generate the problem.