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Models/PlaNet

PlaNet

Reported on 19 benchmarks across 5 tasks · 2 papers · 16 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision5 results

Image ClassificationonYFCC26k
City level (25 km)· 2016-02-17
11
best: 25.8 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
Image ClassificationonYFCC26k
Continent level (2500 km)· 2016-02-17
47.7
best: 79 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
Image ClassificationonYFCC26k
Country level (750 km)· 2016-02-17
28.5
best: 63.2 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
Image ClassificationonYFCC26k
Region level (200 km)· 2016-02-17
16.9
best: 42.7 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
Image ClassificationonYFCC26k
Street level (1 km)· 2016-02-17
4.4
best: 11.6 (GeoCLIP)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314

Graphs5 results

4K 60FpsonYFCC26k
City level (25 km)· 2016-02-17
11
best: 25.8 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
4K 60FpsonYFCC26k
Continent level (2500 km)· 2016-02-17
47.7
best: 79 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
4K 60FpsonYFCC26k
Country level (750 km)· 2016-02-17
28.5
best: 63.2 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
4K 60FpsonYFCC26k
Region level (200 km)· 2016-02-17
16.9
best: 42.7 (PIGEOTTO)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314
4K 60FpsonYFCC26k
Street level (1 km)· 2016-02-17
4.4
best: 11.6 (GeoCLIP)
SOTA
PlaNet - Photo Geolocation with Convolutional Neural Networks arXiv:1602.05314

Robots3 results

Continuous ControlonDeepMind Walker Walk (Images)
Return· 2018-11-12
890
best: 921 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
Continuous ControlonDeepMind Cup Catch (Images)
Return· 2018-11-12
914
best: 963 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
Continuous ControlonDeepMind Cheetah Run (Images)
Return
650
best: 800 (DreamerV1)

Methodology3 results

3DonDeepMind Walker Walk (Images)
Return· 2018-11-12
890
best: 921 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
3DonDeepMind Cup Catch (Images)
Return· 2018-11-12
914
best: 963 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
3DonDeepMind Cheetah Run (Images)
Return
650
best: 800 (DreamerV1)

Medical3 results

3D Face ModellingonDeepMind Walker Walk (Images)
Return· 2018-11-12
890
best: 921 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
3D Face ModellingonDeepMind Cup Catch (Images)
Return· 2018-11-12
914
best: 963 (DrQ)
SOTA
Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551
3D Face ModellingonDeepMind Cheetah Run (Images)
Return
650
best: 800 (DreamerV1)