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argosopentech
Poor Chinese translation？
I seem to get some completely unrelated translation results. I don’t know if my use method is wrong or the model effect is poor.
Vehicle detection technology is of great significance for realizing automatic monitoring and AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of YOLOv5, has often been used to detect vehicles. However, it suffers some challenges, such as a high computational load and undesirable detection rate. To address these issues, an improved lightweight YOLOv5 method is proposed for vehicle detection in this paper. In the presented method, C3Ghost and Ghost modules are introduced into the YOLOv5 neck network to reduce the floating-point operations (FLOPs) in the feature channel fusion process and enhance the feature expression performance. A convolutional block attention module (CBAM) is introduced to the YOLOv5 backbone network to select the information critical to the vehicle detection task and suppress uncritical information, thus improving the detection accuracy of the algorithm. Furthermore, CIoU_Loss is considered the bounding box regression loss function to accelerate the bounding box regression rate and improve the localization accuracy of the algorithm. To verify the performance of the proposed approach, we tested our model via two case studies, i.e., the PASCAL VOC dataset and MS COCO dataset. The results show that the detection precision of the proposed model increased 3.2%, the FLOPs decreased 15.24%, and the number of model parameters decreased 19.37% compared with those of the existing YOLOv5. Through case studies and comparisons, the effectiveness and superiority of the presented approach are demonstrated.
I found this issue too. In zh->en, even the simple word hello can't be translated correctly in Chinese.
So, I tranlated the above sentence to English. And I got this:
I have also found the same problems. In Chinese English translation, even your good translation error
It just not translate...
$ cat test.py
import argostranslate.package
import argostranslate.translate
from_code = "en"
to_code = "zh"
# Download and install Argos Translate package
argostranslate.package.update_package_index()
available_packages = argostranslate.package.get_available_packages()
package_to_install = next(
filter(
lambda x: x.from_code == from_code and x.to_code == to_code, available_packages
)
)
argostranslate.package.install_from_path(package_to_install.download())
# Translate
translatedText = argostranslate.translate.translate("Hello World", from_code, to_code)
print(translatedText)
# '你好 世界'
$ python test.py
Hello World
I don't know what you mean.
The python script is used to transelate "Hello World" into Chinese. But when run the script, it does not translate into Chinese. still English.
Maybe it is because argosopentech is good at sentence but bad at word.
https://huggingface.co/Helsinki-NLP/opus-mt-zh-en does a pretty good job, I wonder if we can use that.
# pip install torch
# pip install sentencepiece
# pip install sacremoses
from transformers import MarianMTModel, MarianTokenizer
def chinese_to_english(text):
model_name = 'Helsinki-NLP/opus-mt-zh-en'
model = MarianMTModel.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)
# Tokenize the text
tokenized_text = tokenizer.encode(text, return_tensors="pt")
# Translate the tokenized text
translated_tokens = model.generate(tokenized_text)
# Decode the translated tokens to a string
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
return translated_text
if __name__ == "__main__":
chinese_text = input("Enter Chinese text: ")
translated_text = chinese_to_english(chinese_text)
print(f"Translated Text: {translated_text}")```
We can! https://community.libretranslate.com/t/opus-mt-language-models-port-thread/757/2
https://github.com/LibreTranslate/Locomotive/#convert-helsinki-nlp-opus-mt-models
