where is yolov4full.tflite ？

[wwdok]

I tried to build and install this repo‘s android app， but it reports the error： Caused by: java.lang.RuntimeException: java.io.FileNotFoundException: yolov4full.tflite

[sterlingrpi]

Did you run convert_tflite.py? The repo doesn't come with the model. I had to download the yolov4 weights and then ran the below command to generate the tflite file. Hope this helps.

python convert_tflite.py --weights ./data/yolov4.weights --output ./data/yolov4.tflite

[wwdok]

Did you run convert_tflite.py? The repo doesn't come with the model. I had to download the yolov4 weights and then ran the below command to generate the tflite file. Hope this helps.

python convert_tflite.py --weights ./data/yolov4.weights --output ./data/yolov4.tflite

Thank you for your tip ! But the exported yolov4.tflite is 63040 KB. I think this is too large to intergrate into android app, it is not suitable and practical.

[sterlingrpi]

That's about the size I got. Still less than the 240MB of the original model. You could try changing the optimizes on line 69 of convert_tflite.py from converter.optimizations = [tf.lite.Optimize.DEFAULT] to converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]. Or try tiny the tiny version of YOLO. Not sure if weights are available or you will have to train your own though.

[wwdok]

That's about the size I got. Still less than the 240MB of the original model. You could try changing the optimizes on line 69 of convert_tflite.py from converter.optimizations = [tf.lite.Optimize.DEFAULT] to converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]. Or try tiny the tiny version of YOLO. Not sure if weights are available or you will have to train your own though.

Is converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE] effective for you ？ For me, it still export the same big size yolov4.tflite. By the way, i also try the other two command line :

# yolov4 quantize float16
python convert_tflite.py --weights ./data/yolov4.weights --output ./data/yolov4-fp16.tflite --quantize_mode float16

# yolov4 quantize int8
python convert_tflite.py --weights ./data/yolov4.weights --output ./data/yolov4-fp16.tflite --quantize_mode full_int8 --dataset ./coco_dataset/coco/val207.txt

the first command line produce a more big tflite file which is 125,789 KB, the second command line has two errors: one is the --dataset path, another is after corect the dadaset path, the terminal report : RuntimeError: Max and min for dynamic tensors should be recorded during calibration

[sterlingrpi]

I have not tried optimize for size no. But thought it worth a try since it's an easy change.

Full int8 quantization requires a representative dataset so it can find the full dynamic range of each activation. So you'll need data for that. However, I didn't suggest it for your case as I don't think it will result in a smaller file since all the weights are already 8 bit in the regular tflite file. Full int 8 makes all the activations 8 bit as well, as far as my understanding of it.

[wwdok]

@hunglc007 Can you update and fix the typo of #convert-to-tflite part

[wwdok]

I have not tried optimize for size no. But thought it worth a try since it's an easy change.

Full int8 quantization requires a representative dataset so it can find the full dynamic range of each activation. So you'll need data for that. However, I didn't suggest it for your case as I don't think it will result in a smaller file since all the weights are already 8 bit in the regular tflite file. Full int 8 makes all the activations 8 bit as well, as far as my understanding of it.

Yeah, you reminds me ! The following screenshot is the content of representative dataset - val2017.txt. Does it mean in the folder of /media/user/Source/Data/coco_dataset/coco/images/val2017/, i got to have these real images, and what does the subsequent integers mean ?

[sterlingrpi]

Right, this is the file that points to where the files are located. The integers are the bounding boxes and the object class. I suggest studying on how YOLO works.

There are tools that can help label your own images. Like Vott. Or there are existing labelled data sets.

[wwdok]

Right, this is the file that points to where the files are located. The integers are the bounding boxes and the object class. I suggest studying on how YOLO works.

There are tools that can help label your own images. Like Vott. Or there are existing labelled data sets.

@sterlingrpi I got it ！Thanks ！

[nightfuryyy]

I have not tried optimize for size no. But thought it worth a try since it's an easy change. Full int8 quantization requires a representative dataset so it can find the full dynamic range of each activation. So you'll need data for that. However, I didn't suggest it for your case as I don't think it will result in a smaller file since all the weights are already 8 bit in the regular tflite file. Full int 8 makes all the activations 8 bit as well, as far as my understanding of it.

Yeah, you reminds me ! The following scr eenshot is the content of representative dataset - val2017.txt. Does it mean in the folder of /media/user/Source/Data/coco_dataset/coco/images/val2017/, i got to have these real images, and what does the subsequent integers mean ?

u can chage func def representative_data_gen in convert_tflite.py file fimage = open(FLAGS.dataset).read().split() for input_value in range(1000): path = "path_to_image/"+fimage[input_value] if os.path.exists(path): print(fimage[input_value]) original_image=cv2.imread(path) original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB) image_data = utils.image_preporcess(np.copy(original_image), [FLAGS.input_size, FLAGS.input_size]) img_in = image_data[np.newaxis, ...].astype(np.float32) print(input_value) yield [img_in] else: print(path) continue

[EuphoriaCelestial]

@sterlingrpi where did you put the .tflite file?

[sterlingrpi]

@sterlingrpi where did you put the .tflite file?

I left it in the default data directory

[EuphoriaCelestial]

@sterlingrpi how does it perform on your phone? I installed on Google Pixel XL, it painfully slow

[sterlingrpi]

@EuphoriaCelestial haven't tried on a phone. I'm running on RPi. But I can concur it is slow. You can try full int8 quantization and/or YOLO tiny. We are working on this in another thread. But it's a process https://github.com/hunglc007/tensorflow-yolov4-tflite/issues/53

[codeman008]

我没有尝试优化尺寸。但是认为这是值得尝试的，因为这是一个容易的更改。 完整的int8量化需要一个有代表性的数据集，因此它可以找到每个激活的完整动态范围。因此，您将需要数据。但是，我没有针对您的情况提出建议，因为我认为这不会导致文件变小，因为常规tflite文件中的所有权重已为8位。据我所知，Full int 8也使所有激活都为8位。

是的，你提醒我！以下屏幕截图是代表性数据集的内容-val2017.txt。这是否意味着在/ media / user / Source / Data / coco_dataset / coco / images / val2017 /的文件夹中，我必须拥有这些真实图像，并且随后的整数是什么意思？

I also met the same problem. How did you solve it? thank you