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# Accuracy evaluation of models in OpenCV Zoo
Make sure you have the following packages installed:
```shell
pip install tqdm
pip install scikit-learn
pip install scipy
```
Generally speaking, evaluation can be done with the following command:
```shell
python eval.py -m model_name -d dataset_name -dr dataset_root_dir
```
Supported datasets:
- [ImageNet](#imagenet)
- [WIDERFace](#widerface)
- [LFW](#lfw)
## ImageNet
### Prepare data
Please visit https://image-net.org/ to download the ImageNet dataset and [the labels from caffe](http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz). Organize files as follow:
```shell
$ tree -L 2 /path/to/imagenet
.
├── caffe_ilsvrc12
│ ├── det_synset_words.txt
│ ├── imagenet.bet.pickle
│ ├── imagenet_mean.binaryproto
│ ├── synsets.txt
│ ├── synset_words.txt
│ ├── test.txt
│ ├── train.txt
│ └── val.txt
├── caffe_ilsvrc12.tar.gz
├── ILSVRC
│ ├── Annotations
│ ├── Data
│ └── ImageSets
├── imagenet_object_localization_patched2019.tar.gz
├── LOC_sample_submission.csv
├── LOC_synset_mapping.txt
├── LOC_train_solution.csv
└── LOC_val_solution.csv
```
### Evaluation
Run evaluation with the following command:
```shell
python eval.py -m mobilenet -d imagenet -dr /path/to/imagenet
```
## WIDERFace
The script is modified based on [WiderFace-Evaluation](https://github.com/wondervictor/WiderFace-Evaluation).
### Prepare data
Please visit http://shuoyang1213.me/WIDERFACE to download the WIDERFace dataset [Validation Images](https://huggingface.co/datasets/wider_face/resolve/main/data/WIDER_val.zip), [Face annotations](http://shuoyang1213.me/WIDERFACE/support/bbx_annotation/wider_face_split.zip) and [eval_tools](http://shuoyang1213.me/WIDERFACE/support/eval_script/eval_tools.zip). Organize files as follow:
```shell
$ tree -L 2 /path/to/widerface
.
├── eval_tools
│ ├── boxoverlap.m
│ ├── evaluation.m
│ ├── ground_truth
│ ├── nms.m
│ ├── norm_score.m
│ ├── plot
│ ├── read_pred.m
│ └── wider_eval.m
├── wider_face_split
│ ├── readme.txt
│ ├── wider_face_test_filelist.txt
│ ├── wider_face_test.mat
│ ├── wider_face_train_bbx_gt.txt
│ ├── wider_face_train.mat
│ ├── wider_face_val_bbx_gt.txt
│ └── wider_face_val.mat
└── WIDER_val
└── images
```
### Evaluation
Run evaluation with the following command:
```shell
python eval.py -m yunet -d widerface -dr /path/to/widerface
```
## LFW
The script is modified based on [evaluation of InsightFace](https://github.com/deepinsight/insightface/blob/f92bf1e48470fdd567e003f196f8ff70461f7a20/src/eval/lfw.py).
This evaluation uses [YuNet](../../models/face_detection_yunet) as face detector. The structure of the face bounding boxes saved in [lfw_face_bboxes.npy](../eval/datasets/lfw_face_bboxes.npy) is shown below.
Each row represents the bounding box of the main face that will be used in each image.
```shell
[
[x, y, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm],
...
[x, y, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm]
]
```
`x1, y1, w, h` are the top-left coordinates, width and height of the face bounding box, `{x, y}_{re, le, nt, rcm, lcm}` stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively. Data type of this numpy array is `np.float32`.
### Prepare data
Please visit http://vis-www.cs.umass.edu/lfw to download the LFW [all images](http://vis-www.cs.umass.edu/lfw/lfw.tgz)(needs to be decompressed) and [pairs.txt](http://vis-www.cs.umass.edu/lfw/pairs.txt)(needs to be placed in the `view2` folder). Organize files as follow:
```shell
$ tree -L 2 /path/to/lfw
.
├── lfw
│ ├── Aaron_Eckhart
│ ├── ...
│ └── Zydrunas_Ilgauskas
└── view2
└── pairs.txt
```
### Evaluation
Run evaluation with the following command:
```shell
python eval.py -m sface -d lfw -dr /path/to/lfw
``` |