Multiresolution CNN
Žádný odhad
Žádné hardwareové požadavky na tento model
Hmotnosti tohoto modelu nebyly publikovány, takže jej nelze stáhnout ani spustit na vlastním hardwaru v jakékoli velikosti. Je dostupný pouze prostřednictvím svého poskytovatele a žádná grafická karta to nezmění..
Záznam.
Plná specifikace
Všechno zaznamenané k tomuto modelu. Většina z toho popisuje, jak byl trénován, nikoli jak běží — užitečný kontext pro posouzení, kolik práce do něj bylo vloženo, a jak se srovnává s modely postavenými na jinou škálu.
Původ
Kdo vybudoval tento model, kde a kdy byl publikován.
- Organizace
- Google,Stanford University
- Typ organizace
- Industry,Academia
- Země
- United States of America
- Publikováno
- 23 June 2014
- Authors
- A Karpathy, G Toderici, S Shetty, T Leung
Co to dělá
Problémové oblasti, pro které byl model vyvinut. Model může obsahovat několik z každého.
- Doména
- Video
- Úkol
- Video classification
Velikost
Jak velký je model a na kolik dat byl vytrénován. Parametry jsou číslo, které rozhoduje o tom, zda se vejde na danou grafickou kartu.
- Parametry
- 126.1M
- Tréninková data
- 50,000,000 tokens
"Using shorthand notation, the full [single frame] architecture is C(96, 11, 3)-N-P-C(256, 5, 1)-N-P-C(384, 3, 1)-C(384, 3, 1)-C(256, 3, 1)-P-FC(4096)-FC(4096), where C(d, f, s) indicates a convolutional layer with d filters of spatial size f ×f, applied to the input with stride s" Two such single-frame architectures are concatenated as shown in figure 2 "Since the input is only of half the spatial size as the full-frame models, we take out the last pooling layer to ensure that both streams st…
"We further estimate the size of our dataset of sampled frames to be on the order of 50 million examples and that our networks have each seen approximately 500 million examples throughout the training period in total." So 5e+7 datapoints and 10 epochs.
Jak je klasifikováno
Labely, které se na zdrojovém datovém souboru používají při sledování významných modelů, a jak moc si je jistý touto položkou.
- Proč je to sledováno
- Highly cited
- Citations
- 6,254
Zdroje
Odkud tento záznam pochází a kdy byl naposled zkontrolován.
- Odkaz
- Large-Scale Video Classification with Convolutional Neural Networks
- Naposledy aktualizováno
- 28 November 2025
Co čísla znamenají
Background
Multiresolution CNN was published by Google,Stanford University, in United States of America, in June 2014. It comes out of industry,Academia.
It works in Video, and is recorded as doing video classification.
Jeho váhy nikdy nebyly zveřejněny, takže je možné ho dosáhnout pouze prostřednictvím jeho poskytovatele. Žádná grafická karta to nezmění.
Jak byla trénována
The training set ran to roughly 50,000,000 tokens.
Its inclusion criterion is highly cited.
Odpovědi
Multiresolution CNN — Časté dotazy
Is Multiresolution CNN open source?
The licensing for Multiresolution CNN was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Multiresolution CNN have?
Multiresolution CNN has 126.1M parameters. "Using shorthand notation, the full [single frame] architecture is C(96, 11, 3)-N-P-C(256, 5, 1)-N-P-C(384, 3, 1)-C(384, 3, 1)-C(256, 3, 1)-P-FC(4096)-FC(4096), where C(d, f, s) indicates a convolutional layer with d filters of spatial size f ×f, applied to the input with stride s" Two such single-frame architectures are concatenated as shown in figure 2 "Since the input is only of half the spatial size as the full-frame models, we take out the last pooling layer to ensure that both streams still terminate in a layer of size 7×7×256. " We assume the input are T=10 frames with C=3 color channels each 2*(256*(10*3*5*5+1) + 384*(256*3*3+1) + 384*(384*3*3+1) + 256*(384*3*3+1)) + (2*7*7*256 + 1)*4096 + (4096+1)*4096. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Multiresolution CNN?
Multiresolution CNN was published by Google,Stanford University, based in United States of America, categorised as industry,Academia.
When was Multiresolution CNN released?
Multiresolution CNN was published in June 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Multiresolution CNN used for?
Multiresolution CNN works in Video, and is recorded as handling video classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Multiresolution CNN?
None. Multiresolution CNN is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
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