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Added ImageNet validation results for 164 pretrained
#PyTorch models on several datasets, incl ImageNet-A, ImageNetV2, and Imagenet-Sketch. No surprise, models with exposure to more data do quite well. Without extra, EfficientNets are holding their own.https://github.com/rwightman/pytorch-image-models/tree/master/results …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Ross Wightman proslijedio/la je Tweet
Are you an angel investor based in Vancouver interested in AI and AI-enabled companies? Join us at
@SFUVentureLabs on February 5 for our themed meeting. Learn the latest trends from this hot industry, hear from a panel, and meet ~10 companies. https://www.vantec.ca/events/vantec-meeting-78 …#BCTechHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Another result, modifying the ‘deep stem’ from Bag of Tricks. Tiering the stem to (24, 48, 64) or (24, 32, 64) shows benefit over the D stem of (32, 32, 64). 77.6% D vs 78.0% for tiered on an SE-ResNeXt26-32x4d base. Tiered stem inspired by
@jeremyphowardPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I ran a few
#PyTorch ResNet experiments over the holiday season. Testing my (re)implementation of Google’s AugMix, I trained a vanilla ResNet50 to 78.994% top-1, just ImageNet, 224 resolution. With test time mean-max pooling at 288x288, the same weights manage 80% top-1.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Kaggle + RL. Looking foward to seeing what is on the horizon for this new competition category!https://twitter.com/kaggle/status/1213226539044745217 …
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Ross Wightman proslijedio/la je Tweet
0/ Is there an Enterprise Margin Crisis? It's not uncommon to see software startups with surprisingly low margins (30-40%). We believe there is a broader trend going on here, which I explore in this thread.
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Ross Wightman proslijedio/la je Tweet
Very excited to share the amazing
@rapidsai libraries with the Vancouver data science community. Hope to see you there!https://twitter.com/ArnieB0426/status/1205374041466425345 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The TLDR of the paper; use adversarial examples as training data augmentation, maintain separate BatchNorm for normal vs adversarial examples. Neat. As usual I've ported & tested
#PyTorch weightshttps://github.com/rwightman/gen-efficientnet-pytorch …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hard to keep up with
@tanmingxing@quocleix and team! I just finished making it through EfficientDet and Noisy Student and now have AdvProp, w/ a quietly released update of all EfficientNet weights, incl a new B8 model spec.https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
More to throw on the ever increasing RL reading list...https://twitter.com/kaixhin/status/1197697440356458498 …
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A port of the recently (and long awaited) official Tensorflow release of MobileNetV3 large/small/minimalistic models added... I spent a long time staring at code before realizing the SE blocks don't inherit the act fn of their parent block as in MnasNet, EffNet, MixNet, etc.
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Ross Wightman proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Nice to see helpful AI / ML technology coming from local (
#Vancouver) inventors!https://twitter.com/UBCEngPhys/status/1186353921242984449 …
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A
#PyTorch implementation of EfficientNet-CondConv w/ some group conv crazyness and weights ported from official TF impl. Joining EfficientNet, EfficientNet-EdgeTPU, MixNet, and others for the only complete family of these models in PyTorch.https://github.com/rwightman/gen-efficientnet-pytorch …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The last batch of weakly-supervised ResNext-101 models are great for transfer learning, I expect these will be too.https://twitter.com/ylecun/status/1185371540260642822 …
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Ross Wightman proslijedio/la je Tweet
Introducing sotabench : a new service with the mission of benchmarking every open source ML model. We run GitHub repos on free GPU servers to capture their results: compare to papers, other models and see speed/accuracy trade-offs. Check it out:http://sotabench.com Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Ross Wightman proslijedio/la je Tweet
2nd iteration of
@TaloHQ
We ditched our custom UI for an integration model w/ ur dev team's favourite tools e.g. @grafana
Real time running #aws cost
Forecasting + anomaly detection w/ ML
Integrate your CI/CD and dev tools (e.g Jenkins)
Fully customizable dashpic.twitter.com/pAUkgvl91YHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
MixNet in the
#PyTorch mix now w/ ported weights working well. Thanks@tanmingxing and team for another great model with weights and code!https://github.com/rwightman/pytorch-image-models …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Another impressive NAS result from the Google AI team.https://twitter.com/tanmingxing/status/1154084867455377408 …
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