https://github.com/hunglc007/tensorflow-yolov4-tflite
YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
https://github.com/hunglc007/tensorflow-yolov4-tflite
android object-detection tensorflow tensorrt tf2 tflite yolov3 yolov3-tiny yolov4
Last synced: 1 day ago
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YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
- Host: GitHub
- URL: https://github.com/hunglc007/tensorflow-yolov4-tflite
- Owner: hunglc007
- License: mit
- Created: 2020-04-24T10:22:52.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-12T16:19:31.000Z (11 months ago)
- Last Synced: 2025-04-06T14:08:14.019Z (8 days ago)
- Topics: android, object-detection, tensorflow, tensorrt, tf2, tflite, yolov3, yolov3-tiny, yolov4
- Language: Python
- Homepage: https://github.com/hunglc007/tensorflow-yolov4-tflite
- Size: 193 MB
- Stars: 2,258
- Watchers: 43
- Forks: 1,237
- Open Issues: 316
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-yolo-object-detection - hunglc007/tensorflow-yolov4-tflite - yolov4-tflite?style=social"/> : YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite. (Other Versions of YOLO)
- awesome-yolo-object-detection - hunglc007/tensorflow-yolov4-tflite - yolov4-tflite?style=social"/> : YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite. (Other Versions of YOLO)
README
# tensorflow-yolov4-tflite
[](LICENSE)
YOLOv4, YOLOv4-tiny Implemented in Tensorflow 2.0.
Convert YOLO v4, YOLOv3, YOLO tiny .weights to .pb, .tflite and trt format for tensorflow, tensorflow lite, tensorRT.
Download yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT
### Prerequisites
* Tensorflow 2.3.0rc0
### Performance
### Demo
```bash
# Convert darknet weights to tensorflow
## yolov4
python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4
## yolov4-tiny
python save_model.py --weights ./data/yolov4-tiny.weights --output ./checkpoints/yolov4-tiny-416 --input_size 416 --model yolov4 --tiny
# Run demo tensorflow
python detect.py --weights ./checkpoints/yolov4-416 --size 416 --model yolov4 --image ./data/kite.jpg
python detect.py --weights ./checkpoints/yolov4-tiny-416 --size 416 --model yolov4 --image ./data/kite.jpg --tiny
```
If you want to run yolov3 or yolov3-tiny change ``--model yolov3`` in command
#### Output
##### Yolov4 original weight
##### Yolov4 tflite int8
### Convert to tflite
```bash
# Save tf model for tflite converting
python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4-416 --input_size 416 --model yolov4 --framework tflite
# yolov4
python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416.tflite
# yolov4 quantize float16
python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416-fp16.tflite --quantize_mode float16
# yolov4 quantize int8
python convert_tflite.py --weights ./checkpoints/yolov4-416 --output ./checkpoints/yolov4-416-int8.tflite --quantize_mode int8 --dataset ./coco_dataset/coco/val207.txt
# Run demo tflite model
python detect.py --weights ./checkpoints/yolov4-416.tflite --size 416 --model yolov4 --image ./data/kite.jpg --framework tflite
```
Yolov4 and Yolov4-tiny int8 quantization have some issues. I will try to fix that. You can try Yolov3 and Yolov3-tiny int8 quantization
### Convert to TensorRT
```bash# yolov3
python save_model.py --weights ./data/yolov3.weights --output ./checkpoints/yolov3.tf --input_size 416 --model yolov3
python convert_trt.py --weights ./checkpoints/yolov3.tf --quantize_mode float16 --output ./checkpoints/yolov3-trt-fp16-416
# yolov3-tiny
python save_model.py --weights ./data/yolov3-tiny.weights --output ./checkpoints/yolov3-tiny.tf --input_size 416 --tiny
python convert_trt.py --weights ./checkpoints/yolov3-tiny.tf --quantize_mode float16 --output ./checkpoints/yolov3-tiny-trt-fp16-416
# yolov4
python save_model.py --weights ./data/yolov4.weights --output ./checkpoints/yolov4.tf --input_size 416 --model yolov4
python convert_trt.py --weights ./checkpoints/yolov4.tf --quantize_mode float16 --output ./checkpoints/yolov4-trt-fp16-416
```
### Evaluate on COCO 2017 Dataset
```bash
# run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset
# preprocess coco dataset
cd data
mkdir dataset
cd ..
cd scripts
python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl
python coco_annotation.py --coco_path ./coco
cd ..
# evaluate yolov4 model
python evaluate.py --weights ./data/yolov4.weights
cd mAP/extra
python remove_space.py
cd ..
python main.py --output results_yolov4_tf
```
#### mAP50 on COCO 2017 Dataset
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 | 55.43 | 52.32 | |
| YoloV4 | 61.96 | 57.33 | |
### Benchmark
```bash
python benchmarks.py --size 416 --model yolov4 --weights ./data/yolov4.weights
```
#### TensorRT performance
| YoloV4 416 images/s | FP32 | FP16 | INT8 |
|---------------------|----------|----------|----------|
| Batch size 1 | 55 | 116 | |
| Batch size 8 | 70 | 152 | |
#### Tesla P100
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 FPS | 40.6 | 49.4 | 61.3 |
| YoloV4 FPS | 33.4 | 41.7 | 50.0 |
#### Tesla K80
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 FPS | 10.8 | 12.9 | 17.6 |
| YoloV4 FPS | 9.6 | 11.7 | 16.0 |
#### Tesla T4
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 FPS | 27.6 | 32.3 | 45.1 |
| YoloV4 FPS | 24.0 | 30.3 | 40.1 |
#### Tesla P4
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 FPS | 20.2 | 24.2 | 31.2 |
| YoloV4 FPS | 16.2 | 20.2 | 26.5 |
#### Macbook Pro 15 (2.3GHz i7)
| Detection | 512x512 | 416x416 | 320x320 |
|-------------|---------|---------|---------|
| YoloV3 FPS | | | |
| YoloV4 FPS | | | |
### Traning your own model
```bash
# Prepare your dataset
# If you want to train from scratch:
In config.py set FISRT_STAGE_EPOCHS=0
# Run script:
python train.py
# Transfer learning:
python train.py --weights ./data/yolov4.weights
```
The training performance is not fully reproduced yet, so I recommended to use Alex's [Darknet](https://github.com/AlexeyAB/darknet) to train your own data, then convert the .weights to tensorflow or tflite.
### TODO
* [x] Convert YOLOv4 to TensorRT
* [x] YOLOv4 tflite on android
* [ ] YOLOv4 tflite on ios
* [x] Training code
* [x] Update scale xy
* [ ] ciou
* [ ] Mosaic data augmentation
* [x] Mish activation
* [x] yolov4 tflite version
* [x] yolov4 in8 tflite version for mobile
### References
* YOLOv4: Optimal Speed and Accuracy of Object Detection [YOLOv4](https://arxiv.org/abs/2004.10934).
* [darknet](https://github.com/AlexeyAB/darknet)
My project is inspired by these previous fantastic YOLOv3 implementations:
* [Yolov3 tensorflow](https://github.com/YunYang1994/tensorflow-yolov3)
* [Yolov3 tf2](https://github.com/zzh8829/yolov3-tf2)