Gallente
Intaki
0.53
Last Active:
29 days ago
Birthday:
Nov 10, 2023 (1 years old)
Next Birthday:
Nov 10, 2025 (60 days remaining)
Combat Metrics
Kills
243
Losses
19
Efficiency
92.7%
Danger Ratio
74.3%
ISK Metrics
ISK Killed
317.13B ISK
ISK Lost
34.92B ISK
ISK Efficiency
90.1%
ISK Balance
282.20B ISK
Solo Activity
Solo Kills
12
Solo Losses
13
Solo Kill Ratio
4.9%
Solo Efficiency
48.0%
Other Metrics
NPC Losses
9
NPC Loss Ratio
47.4
Avg. Kills/Day
0.4
Activity
Medium
Character Biography
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*