#jupyter_notebook
DINOv3 offers powerful self-supervised vision models from Meta AI, like ViT up to 7B parameters and ConvNeXt, pretrained on 1.7B web or satellite images. Load them easily via PyTorch Hub, Hugging Face Transformers (v4.56+), or timm (v1.0.20+), with code examples for features, depth, detection, and segmentation. You benefit by using these top-performing, dense features without fine-tuning or labels—saving time and compute for tasks like classification, object detection, and zero-shot analysis on your images.
https://github.com/facebookresearch/dinov3
Lookonchain | ꘜ
Whales are accumulating $BGB recently.
0x8900 withdrew 192,668 $BGB($936K) from #Bitget over the past 2 months.
0x171D withdrew 30,607 $BGB($134K) from #Bitget 2 days ago.
0x7C9C withdrew 20,980 $BGB($102K) from #Bitget over the past 3 months.
Notably, #Bitget has burned a total of 860M $BGB($5.25B) over the past 8 months, reducing the total supply by 43%.
https://intel.arkm.com/explorer/address/0x89006C3aADfF87c5113b835660E3459C6Ad61F16
https://intel.arkm.com/explorer/address/0x171D1285a9a8De3f16d4c45706d4E2F4A5C9e175
https://intel.arkm.com/explorer/address/0x7C9C4f9046ba2173fae539FE62eEFAb1aBAD1523