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Pets-Face-Recognition

(★ 128)

Animal identification using face recognition based methods

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최종 버전 다운로드 (.zip)

- - body_cat_fe.py
  - cat_fe_head.py
  - masked_head_cat.py
  - no_align_head_cat.py
  - simple_fe_cat.py
- - body_dog_fe.py
  - fe_dogs_config.py
  - masked_head_dog.py
  - no_align_head_dog.py
  - simple_fe_dog.py
- - keypoints_config.py
- - mask_rcnn_config.py
- __init__.py
- dataset.py
- human.py
- lmd_dataset.py
- oxford.py
- pairs.py
- - __init__.py
  - eval_loop.py
  - predict_loop.py
  - train_loop.py
- __init__.py
- controller.py
- detector_controller.py
- keypoints_controller.py
- trainer.py
- __init__.py
- large_margin.py
- losses.py
- - __init__.py
  - rcnn.py
- __init__.py
- swin.py
- __init__.py
- align.py
- __init__.py
- collate_fn.py
- preprocs.py
.gitignore
data_25_anno.pickle
download_datasets.py
download_models.py
eval_detection.py
eval_fe_cat_head_sgd.py
eval_fe_dog_head_sgd.py
eval_landmark.py
generate_tsv.py
generate_tsv_to_reproduce1.py
generate_tsv_to_reproduce2.py
LICENSE
main.py
main_detection.py
main_keypoints.py
others.pickle
others2.pickle
paths.pickle
paths2.pickle
preds.tsv
prepare_tables.py
README.md
requirements.txt
score_detection.py
score_landmark.py
transform_dataset.py
transform_reproduce.py

// repository documentation

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# Pets-Face-Recognition

## Download the datasets
List of the datasets:
1. Oxford IIIT Pet (https://www.robots.ox.ac.uk/~vgg/data/pets/)
2. Cat Dataset (https://www.microsoft.com/en-us/research/wp-content/uploads/2008/10/ECCV_CAT_PROC.pdf)
3. Petfinder cats (https://zenodo.org/record/6656292#.Yq66DHZBwuU)
4. Petfinder dogs (https://zenodo.org/record/6660349#.Yq8TJHZBwuU)
5. Labelled data from Kashtanka.pet (https://zenodo.org/record/6664769#.Yq8GuXZBwuU)

`python download_datasets.py`

The script downloads all the needed datasets to the directory ../pets_datasets

## Checkpoints and our configs

To download the checkpoints and configs to use them run:

`python download_models.py`

## Detectors

Body detection and segmentation

| Dataset | AP50 | AP70 | IoU detection | IoU segmentation | 
| ------- | ---------- | ---------- | ---------- | ---------- |
| Oxford IIIT Pets | 0.999 | 0.999 | 0.975 | 0.946 |
| Labelled kashtanka.pet dogs | 0.966 | 0.916 | 0.866 | N/A | 
| Labelled kashtanka.pet cats | 0.979 | 0.952 | 0.836| N/A |

Head and landmarks detection

| Dataset | AP50 | AP70 | IoU | NME | NME (Median) | NME percentile 0.25 | NME percentile 0.75|
| ------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- | ---------- |
| Cat Dataset | 0.999 | 0.988 | 0.909 | 0.044 | - | - | - |
| Labelled kashtanka.pet dogs | 0.999 | 0.715 | 0.774 | 0.141 | 0.057 | 0.036 | 0.088 | 
| Labelled kashtanka.pet cats | 0.975 | 0.869 | 0.866 | 0.277 | 0.061 | 0.037 | 0.094 |

### Training body detection (Mask R-CNN)

`python main_detection.py --config configs/to_reproduce/mask/mask_rcnn_config.py`

### Training Head and Landmark Detection (Keypoint R-CNN)

`python main_keypoints.py --config configs/to_reproduce/keypoint/keypoints_config.py`

### Evaluation of body detection and segmentation on validation datasets

If you want to test your own models you need to create a script analogous to eval_detection.py.

`python eval_detection.py`

### Evaluation of head and landmark detection on validation datasets

If you want to test your own models you need to create a script analogous to eval_landmark.py.

`python eval_landmark.py`

### Testing models for body, head and landmarks detection

prepare_table.py runs Mask R-CNN trained on Oxford IIIT pets to predict body bounding boxes,
and Keypoint R-CNN trained on Cat Dataset + 350 manually selected examples of dogs from kashtanka.pet with good annotations from the previous model to predict head bounding boxes and landmarks.
If you want to test your own models you need to create a script analogous to prepare_tables.py to create tables with predictions.

`python prepare_tables.py` to get 3 .tsv files for the assessment

To test Head detection use:

`python score_detection.py detected_head.tsv data_25 Head`

To test Body detection use:

`python score_detection.py detected_body.tsv data_25 Animal`

For landmark detection evaluation use:

`python score_landmark.py landmark.tsv data_25`

### How to convert from Label Studio format to the format of the evaluation scripts

TODO

## Training Feature Extractors (FE)

Results on data_25 val part for FE

| Model | ROC AUC | Accuracy | candR@10 | candR@100 |
| ------- | ---------- | ---------- | ---------- | ---------- | 
| Dog Head SGD | 0.973 | 0.938 | 0.777 | 0.911 | 
| Dog Head AdamW | 0.975 | 0.94 | 0.733 | 0.906 |
| Cat Head SGD | 0.958 | 0.915 | 0.653 | 0.904 |
| Cat Head AdamW | 0.97 | 0.922 | 0.753 | 0.93 |
| Dog Body SGD | 0.974 | 0.926 | 0.636 | 0.864 | 
| Dog Body AdamW | 0.878 | 0.8 | 0.348 | 0.56 |
| Cat Body SGD | 0.968 | 0.917 | 0.538 | 0.811 |
| Cat Body AdamW | 0.965 | 0.91 | 0.545 | 0.809 |

Results of the pipelines (detector + FE) on kashtanka.pet public test

| Pipeline | candR@10 lost hard | candR@100 lost hard | candR@10 lost simple | candR@100 lost simple |
| ------- | ---------- | ---------- | ---------- | ---------- | 
| Head-based | 0.386 | 0.569 | 0.52 | 0.632 | 
| Ensemble | 0.395 | 0.604 | 0.583 | 0.735 |

### Prepare the datasets for training Feature Extraction
transform_reproduce.py runs head detection and alignment model and body detection and segmentation model on petfinder data and kashtanka_25

`python transform_reproduce.py`

### Training FE for Cats
Head-specific model (Cat Head SGD)

`python main.py --config configs/to_reproduce/cat_fe/cat_fe_head.py`

Body-specific model (Cat Body AdamW)

`python main.py --config configs/to_reproduce/cat_fe/body_cat_fe.py`

### Training FE for Dogs
Head-specific model (Dog Head SGD)

`python main.py --config configs/to_reproduce/dog_fe/fe_dogs_config.py`

Body-specific model (Dog Body SGD)

`python main.py --config configs/to_reproduce/dog_fe/body_dog_fe.py`

### Evaluation of FE on validation datasets

To validate Head-specific model (Dog Head SGD)

`python eval_fe_dog_head_sgd.py`

To validate Head-specific model (Cat Head SGD)

`python eval_fe_cat_head_sgd.py`

### Generate .tsv for kashtanka.pet testing

Combination:

`python generate_tsv_to_reproduce1.py`

Only Face-based:

`python generate_tsv_to_reproduce2.py`

The scripts produce pred_scores_test1.tsv and pred_scores_test2.tsv correspondingly. 
The files then should be submitted using file sent to you after your registration on http://92.63.96.33/c/_lostpets_v3_1/description.
Pay attention you can modify the scripts and provide your own checkpoints