Here, we give the full list of models trained in-house for specific use-cases. Each model is accompanied with its own README, retraining docker and retraining guide.
- FLOPs in the table are counted as MAC operations.
Important:
Retraining is not available inside the docker version of Hailo Software Suite. In case you use it, clone the hailo_model_zoo outside of the docker, and perform the retraining there:
git clone https://github.com/hailo-ai/hailo_model_zoo.git
- Object Detection
| Network Name | mAP* | Input Resolution (HxWxC) | Params (M) | FLOPs (G) |
|---|---|---|---|---|
| yolov5m_vehicles | 46.5 | 640x640x3 | 21.47 | 25.63 |
| tiny_yolov4_license_plates | 73.45 | 416x416x3 | 5.87 | 3.4 |
| yolov5s_personface | 47.5 | 640x640x3 | 7.25 | 8.38 |
- License Plate Recognition
| Network Name | Accuracy* | Input Resolution (HxWxC) | Params (M) | FLOPs (G) |
|---|---|---|---|---|
| lprnet | 99.96 | 75x300x3 | 7.14 | 18.29 |
* Evaluated on internal dataset
- Person Re-ID
| Network Name | Accuracy* | Input Resolution (HxWxC) | Params (M) | FLOPs (G) |
|---|---|---|---|---|
| repvgg_a0_person_reid_512 | 89.9 | 256x128x3 | 7.68 | 0.89 |
| repvgg_a0_person_reid_2048 | 90.02 | 256x128x3 | 9.65 | 0.89 |
* Evaluated on Market-1501