Awesome
YOLO-v5 TFLite Model
YOLOv5 - most advanced vision AI model for object detection. Natively implemented in PyTorch and exportable to TFLite for use in edge solutions. This repository provides an Object Detection model in TensorFlow Lite (TFLite) for TensorFlow 2.x. These models primarily come from two repositories - ultralytics and zldrobit. We provide end-to-end code that show the inference process using TFLite and model conversion.
English-ASR pip wheel
TFHub
live streaming is the future work.
Installation
- pip3 install -r requirements.txt
Pretrained Checkpoints
| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>test<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>V100 (ms) | params<br><sup>(M) | FLOPs<br><sup>640 (B) | |
|---|---|---|---|---|---|---|---|---|
| [YOLOv5s] | 640 | 36.7 | 36.7 | 55.4 | 2.0 | 7.3 | 17.0 | |
| [YOLOv5m] | 640 | 44.5 | 44.5 | 63.1 | 2.7 | 21.4 | 51.3 | |
| [YOLOv5l] | 640 | 48.2 | 48.2 | 66.9 | 3.8 | 47.0 | 115.4 | |
| [YOLOv5x] | 640 | 50.4 | 50.4 | 68.8 | 6.1 | 87.7 | 218.8 | |
| [YOLOv5s6] | 1280 | 43.3 | 43.3 | 61.9 | 4.3 | 12.7 | 17.4 | |
| [YOLOv5m6] | 1280 | 50.5 | 50.5 | 68.7 | 8.4 | 35.9 | 52.4 | |
| [YOLOv5l6] | 1280 | 53.4 | 53.4 | 71.1 | 12.3 | 77.2 | 117.7 | |
| [YOLOv5x6] | 1280 | 54.4 | 54.4 | 72.0 | 22.4 | 141.8 | 222.9 | |
| [YOLOv5x6] TTA | 1280 | 55.0 | 55.0 | 72.0 | 70.8 | - | - |
- AP<sup>test</sup> denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
- AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by
python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65 - Speed<sub>GPU</sub> averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by
python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45 - All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).
- Test Time Augmentation (TTA) includes reflection and scale augmentation. Reproduce TTA by
python test.py --data coco.yaml --img 1536 --iou 0.7 --augment