0.53
Apr 4, 2026
Nov 10, 2023(2y)
Nov 10, 2026(72d)
Combat
Kills106
Losses19
Efficiency85%
ISK
Destroyed266.43b
Lost34.92b
ISK Eff.88%
Solo
Solo Kills13
Solo Ratio12%
Final Blows23
Points106
Other
NPC Losses9
NPC Loss Ratio47%
Avg Kills/Day0.10
ActivityInactive
Gallente
Intaki
0.53
Last Active
Apr 4, 2026
Birthday
Nov 10, 2023 (2 years old)
Next Birthday
Nov 10, 2026 (72 days)
Combat
Kills106
Losses19
Efficiency85%
Danger Ratio53%
ISK
Destroyed266.43b
Lost34.92b
ISK Efficiency88%
Balance+231.51b
Solo
Solo Kills13
Solo Ratio12%
Final Blows23
Points106
Other
NPC Losses9
NPC Loss Ratio47%
Avg Kills/Day0.10
ActivityInactive
Nothing in the last 7d
Bio
This is a ResNeXt series AI model, performing a series of automated procedures
We present a simple, highly modularized network architecture for Fully automated for various activities. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology.
Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size ofthe set of transformations), as an essential factor in addition to the dimensions of depth and width.
On the ImageNet-1K dataset, we empirically show that even under the restricted condition ofmaintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity.
Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart.
https://arxiv.org/abs/1611.05431
Peaceful farm, non-interference with each other
If you interfere with me, it's a tit for tat
超网订单：帕拉丁级*
We present a simple, highly modularized network architecture for Fully automated for various activities. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology.
Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call “cardinality” (the size ofthe set of transformations), as an essential factor in addition to the dimensions of depth and width.
On the ImageNet-1K dataset, we empirically show that even under the restricted condition ofmaintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity.
Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart.
https://arxiv.org/abs/1611.05431
Peaceful farm, non-interference with each other
If you interfere with me, it's a tit for tat
超网订单：帕拉丁级*
Dashboard
Stats
No activity in the last 90d
Last seen Apr 2026
Intel Profile
PlaystyleSolo (0 kills)
Avg Fleet: -
Top Fleet Partners
