https://github.com/pd-mera/object-detection-validation
Evaluation for object detection models
https://github.com/pd-mera/object-detection-validation
average-precision evaluation mean-average-precision object-detection onnx onnxruntime precision recall
Last synced: 3 months ago
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Evaluation for object detection models
- Host: GitHub
- URL: https://github.com/pd-mera/object-detection-validation
- Owner: PD-Mera
- Created: 2023-11-03T08:52:41.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-11-08T07:23:08.000Z (over 1 year ago)
- Last Synced: 2025-01-08T19:46:47.859Z (5 months ago)
- Topics: average-precision, evaluation, mean-average-precision, object-detection, onnx, onnxruntime, precision, recall
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Implement validation of object detection models
## Environments
Simply install requirements
``` bash
pip install -r requirements.txt
```
## Models
### YOLO Format
Model in onnx format, with input is an image with pixel values from [0, 1], outputs with shape `[batch_size, anchors, 4 + num_classes + 1]`
Example 1 line in outputs is
``` bash
# Format is [x_center, y_center, w, h, obj_conf, class_1_conf, class_2_conf]
[0.51 0.75 0.21 0.19 0.85 0.1 0.9]
```
### Retina Format
Model in onnx format, with input is an image read from `cv2.imread`, 3 outputs are `scores`, `bboxes` and `landmarks`
Example 1 line in outputs is
``` bash
# bboxes
[ 0.67413944 1.0143973 -1.1417826 0.16699778]
# scores
[9.9962950e-01 3.7046883e-04]
# landmarks
```
## Prepare labels
Ground truth labels in yolo format `[class_idx, x_center, y_center, w, h]`
``` txt
0 0.725000 0.287500 0.089394 0.070455
```
Predict labels also in yolo format, with confidence `[class_idx, x_center, y_center, w, h, conf]`
``` txt
0 0.8927168528238932 0.9185009002685547 0.17276369730631513 0.13056144714355478 0.8909046649932861
```
## Run
To get predict of model, modify path in `predict.py` and run
``` bash
python predict.py --onnx ./weights/yolov5m_v0_0_1.onnx \
--image_dir ../__testset__/TCB-VJA-testset_1/all/images \
--save_txt ./output_labels \
--backend yolo \
--gpu
```
To get evaluation value such as `mAP`, run
``` bash
python evaluate.py -gt ../__testset__/TCB-VJA-testset_1/all/labels \
-dr ./output_labels \
--image_dir ../__testset__/TCB-VJA-testset_1/all/images \
--num_classes 2
```
## References
- https://github.com/Cartucho/mAP
- https://github.com/ppogg/Retinaface_Ghost