4 ms·
EfficientSAM
- naveen99 3y agocan’t wait for everywhere all at once function.
- GaggiX 3y agohttps://github.com/ChaoningZhang/MobileSAM https://github.com/ChaoningZhang/MobileSAM was the previous attempt at reducing the size of the large image encoder used by SAM.
- cchance 3y agoit's called efficient Sam and it appears to be onpar or better than fastsam but did I miss a memory or speed comparison?
- yorwba 3y agoThe comparison is figure 1 of the paper. I think the bubble size represents number of parameters, which likely roughly corresponds to memory consumption.
- skadamat 3y agoExcited to play with this more! Forked the repo and added the models into the repo itself (migrated from Dropbox): https://github.com/xetdata/EfficientSAM https://github.com/xetdata/EfficientSAM
- ShadowBanThis01 3y agoIs what?
- IshanMi 3y agoSo if I'm understanding this correctly: The SAM paper from this past April (that let you do zero-shot segmentation on any image, seemingly better than even OpenAI's CLIP) was using a ~600M parameter ViT model to generate image embeddings. And in order to make it less computationally expensive to generate those same embeddings, they replace that model with a smaller ViT encoder that was pre-trained using the masked auto-encoder back propagation method?