Bulan: September 2021

Simulasi Pendaratan Starship dari SpaceX

Proses instalasi

conda install pip
pip install jupyter
pip install jupyterthemes
pip install matplotlib
pip install casidi

Referensi

Incorrect Labels in Global Wheat Head Dataset 2021

There are several incorrect labels in Global Wheat Head Dataset 2021. Some images are annotated in their original size. After that, the images are resized to final size (1024×1024 pixels). As the result, the bounding boxes are a little bit off.

Here is the list of affected images

Image LabelReason
0af5c1bc753619e4f5d504e5424d056af22954f04d50cd0d4a21682cfdd9a4dc.png Image file resized after annotation
4c9c82eeefaaa8b3b7300561820274c0ff576b47ada9239862f4a295cbdb18b7.png Image file resized after annotation
6be51c1a5132034427ecabaafa679fcac7c8f95e05a595df69401766b90d7890.png Image file resized after annotation

To correct the annotation, the coordinates must be multiplied by a correction factor and shifted a little bit downward. Here is the comparison between original annotation (green) and corrected annotation (red).

0af5c1bc753619e4f5d504e5424d056af22954f04d50cd0d4a21682cfdd9a4dc

IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation

Abstract

A benchmark provides an ecosystem to measure the advancement of models with standard datasets and automatic and human evaluation metrics. We introduce IndoNLG, the first such benchmark for the Indonesian language for natural language generation (NLG). It covers six tasks: summarization, question answering, open chitchat, as well as three different language-pairs of machine translation tasks. We provide a vast and clean pre-training corpus of Indonesian, Sundanese, and Javanese datasets called Indo4B-Plus, which is used to train our pre-trained NLG model, IndoBART. We evaluate the effectiveness and efficiency of IndoBART by conducting extensive evaluation on all IndoNLG tasks. Our findings show that IndoBART achieves competitive performance on Indonesian tasks with five times fewer parameters compared to the largest multilingual model in our benchmark, mBART-LARGE (Liu et al., 2020), and an almost 4x and 2.5x faster inference time on the CPU and GPU respectively. We additionally demonstrate the ability of IndoBART to learn Javanese and Sundanese, and it achieves decent performance on machine translation tasks.

Link: https://arxiv.org/abs/2104.08200