Spaces:
Running
Running
File size: 7,849 Bytes
58ff7c0 f12b919 bd044ed 58ff7c0 f12b919 bd044ed f12b919 58ff7c0 9019b36 f12b919 3fb84f6 58ff7c0 8e5798a 71a8d2b f12b919 9528b50 f12b919 8455f0e f12b919 5facf9a 8e5798a f12b919 8e5798a f12b919 8e5798a 5facf9a 8e5798a f12b919 8e5798a 69e13f0 f12b919 8e5798a f12b919 8e5798a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
---
app_file: app.py
colorFrom: yellow
colorTo: green
description: 'TODO: add a description here'
emoji: "🤑"
pinned: false
runme:
id: 01HPS3ASFJXVQR88985QNSXVN1
version: v3
sdk: gradio
sdk_version: 4.36.0
tags:
- evaluate
- metric
title: user-friendly-metrics
---
# How to Use
```python {"id":"01HPS3ASFHPCECERTYN7Z4Z7MN"}
>>> import evaluate
>>> from seametrics.fo_utils.utils import fo_to_payload
>>> b = fo_to_payload(
>>> dataset="SENTRY_VIDEOS_DATASET_QA",
>>> gt_field="ground_truth_det",
>>> models=['volcanic-sweep-3_02_2023_N_LN1_ep288_TRACKER'],
>>> sequence_list=["Sentry_2022_11_PROACT_CELADON_7.5M_MOB_2022_11_25_12_12_39"],
>>> tracking_mode=True
>>> )
>>> module = evaluate.load("SEA-AI/user-friendly-metrics")
>>> res = module._calculate(b, max_iou=0.99, recognition_thresholds=[0.3, 0.5, 0.8])
>>> print(res)
```
```
global:
ahoy-IR-b2-whales__XAVIER-AGX-JP46_TRACKER:
all:
f1: 0.8262651742077881
fn: 2045.0
fp: 159.0
num_gt_ids: 13
precision: 0.9705555555555555
recall: 0.7193247323634367
recognition_0.3: 0.9230769230769231
recognition_0.5: 0.8461538461538461
recognition_0.8: 0.46153846153846156
recognized_0.3: 12
recognized_0.5: 11
recognized_0.8: 6
tp: 5241.0
area:
large:
f1: 0.4053050397877984
fn: 612.0
fp: 3872.0
num_gt_ids: 6
precision: 0.28296296296296297
recall: 0.7140186915887851
recognition_0.3: 0.8333333333333334
recognition_0.5: 0.8333333333333334
recognition_0.8: 0.3333333333333333
recognized_0.3: 5
recognized_0.5: 5
recognized_0.8: 2
tp: 1528.0
medium:
f1: 0.7398209644816635
fn: 1146.0
fp: 1557.0
num_gt_ids: 10
precision: 0.7116666666666667
recall: 0.7702946482260974
recognition_0.3: 1.0
recognition_0.5: 0.8
recognition_0.8: 0.6
recognized_0.3: 10
recognized_0.5: 8
recognized_0.8: 6
tp: 3843.0
small:
f1: 0.10373582388258838
fn: 285.0
fp: 5089.0
num_gt_ids: 6
precision: 0.05759259259259259
recall: 0.5218120805369127
recognition_0.3: 0.3333333333333333
recognition_0.5: 0.3333333333333333
recognition_0.8: 0.16666666666666666
recognized_0.3: 2
recognized_0.5: 2
recognized_0.8: 1
tp: 311.0
per_sequence:
Sentry_2022_12_19_Romania_2022_12_19_17_09_34:
ahoy-IR-b2-whales__XAVIER-AGX-JP46_TRACKER:
all:
f1: 0.8262651742077881
fn: 2045.0
fp: 159.0
num_gt_ids: 13
precision: 0.9705555555555555
recall: 0.7193247323634367
recognition_0.3: 0.9230769230769231
recognition_0.5: 0.8461538461538461
recognition_0.8: 0.46153846153846156
recognized_0.3: 12
recognized_0.5: 11
recognized_0.8: 6
tp: 5241.0
area:
large:
f1: 0.4053050397877984
fn: 612.0
fp: 3872.0
num_gt_ids: 6
precision: 0.28296296296296297
recall: 0.7140186915887851
recognition_0.3: 0.8333333333333334
recognition_0.5: 0.8333333333333334
recognition_0.8: 0.3333333333333333
recognized_0.3: 5
recognized_0.5: 5
recognized_0.8: 2
tp: 1528.0
medium:
f1: 0.7398209644816635
fn: 1146.0
fp: 1557.0
num_gt_ids: 10
precision: 0.7116666666666667
recall: 0.7702946482260974
recognition_0.3: 1.0
recognition_0.5: 0.8
recognition_0.8: 0.6
recognized_0.3: 10
recognized_0.5: 8
recognized_0.8: 6
tp: 3843.0
small:
f1: 0.10373582388258838
fn: 285.0
fp: 5089.0
num_gt_ids: 6
precision: 0.05759259259259259
recall: 0.5218120805369127
recognition_0.3: 0.3333333333333333
recognition_0.5: 0.3333333333333333
recognition_0.8: 0.16666666666666666
recognized_0.3: 2
recognized_0.5: 2
recognized_0.8: 1
tp: 311.0
```
## Metric Settings
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. So, the higher the `max_iou` value, the more the predicted bounding boxes are considered for association.
## Output
The output is a dictionary containing the following metrics:
| Name | Description |
| :------------------- | :--------------------------------------------------------------------------------- |
| recall | Number of detections over number of objects. |
| precision | Number of detected objects over sum of detected and false positives. |
| f1 | F1 score |
| num_gt_ids | Number of unique objects on the ground truth |
| fn | Number of false negatives |
| fp | Number of of false postives |
| tp | number of true positives |
| recognized_th | Total number of unique objects on the ground truth that were seen more then th% of the times |
| recognition_th | Total number of unique objects on the ground truth that were seen more then th% of the times over the number of unique objects on the ground truth|
## How it Works
We levereage one of the internal variables of motmetrics ```MOTAccumulator``` class, ```events```, which keeps track of the detections hits and misses. These values are then processed via the ```track_ratios``` function which counts the ratio of assigned to total appearance count per unique object id. We then define the ```recognition``` function that counts how many objects have been seen more times then the desired threshold.
## Citations
```bibtex {"id":"01HPS3ASFJXVQR88985GKHAQRE"}
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}}
```
```bibtex {"id":"01HPS3ASFJXVQR88985KRT478N"}
@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)
|