Image Classification
timm
TensorBoard
vision
facial-expression-recognition
vit
vision-transformer
fer
ferplus
Instructions to use peepeeyanto/ViTFERPP_vit_small_patch16_224.augreg_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use peepeeyanto/ViTFERPP_vit_small_patch16_224.augreg_in1k with timm:
import timm model = timm.create_model("hf_hub:peepeeyanto/ViTFERPP_vit_small_patch16_224.augreg_in1k", pretrained=True) - Notebooks
- Google Colab
- Kaggle
| Epoch,Model1_Train_Loss,Model1_Val_Acc,Model2_Train_Loss,Model2_Val_Acc | |
| 1,,35.74,2.0360705567318855,22.568616724384142 | |
| 2,1.1986,64.96,1.9792786671214126,28.714201857764852 | |
| 3,1.0441,69.91,1.7821412383084092,52.3717207174597 | |
| 4,0.9982,68.54,1.6183974135435368,61.03818857129263 | |
| 5,1.0101,63.25,1.5248827010250547,61.7989283324995 | |
| 6,1.0382,63.53,1.4669698419753445,67.19869004684303 | |
| 7,0.9866,64.74,1.4290498651956256,70.68914373393275 | |
| 8,0.9614,67.26,1.3899061232092278,68.09367889549965 | |
| 9,0.9348,67.36,1.3519616517724033,72.85203231661302 | |
| 10,0.917,66.42,1.327325906479758,72.13604118488284 | |
| 11,0.9211,70.44,1.2994008415053335,72.52386902966192 | |
| 12,0.8618,70.79,1.274697577554073,75.3430823016565 | |
| 13,0.8535,68.26,1.2592898128135352,73.29952621459961 | |
| 14,0.8238,72.2,1.237077219064156,74.82100560727723 | |
| 15,0.7955,72.94,1.2224091088942934,75.82040918527754 | |
| 16,0.7659,69.08,1.2057472752612173,75.83532527295388 | |
| 17,0.7439,72.08,1.1897126553161292,76.04415603014051 | |
| 18,0.7126,72.69,1.176309052836952,76.1485714832752 | |
| 19,0.6794,71.33,1.1633472284346675,77.08830949059534 | |
| 20,0.65,74.52,1.145757243251116,78.43079130063478 | |
| 21,0.6196,75.07,1.1302614176387422,78.0429628549726 | |
| 22,0.602,73.93,1.1211656371942547,78.62470563328635 | |
| 23,0.5602,74.6,1.1059313349461442,78.53520640439235 | |
| 24,0.5343,77.01,1.0975406804438412,77.50597055658235 | |
| 25,0.5009,75.85,1.084783926392286,78.96778403830699 | |
| 26,0.4729,76.18,1.081091641239002,78.60978895838335 | |
| 27,0.445,76.64,1.066453042212856,78.5799557747306 | |
| 28,0.4161,76.22,1.0537544052566639,78.66945435380595 | |
| 29,0.3894,77.03,1.0435457479155235,79.05728320688507 | |
| 30,0.3514,78.37,1.0366827436326223,79.53460898205887 | |
| 31,0.3326,77.68,1.0267470826942955,79.54952623280819 | |
| 32,0.3043,80.12,1.0243510783574228,79.99702059738961 | |
| 33,0.2952,78.04,1.0170651106743152,79.59427570898379 | |
| 34,0.2709,79.5,1.0055033556011874,80.08651905469507 | |
| 35,0.2688,79.95,1.0005212784098667,80.38484843306439 | |
| 36,0.2305,80.3,0.9912698331632113,80.41468205144696 | |
| 37,0.2143,80.36,0.984084892501101,79.9671874558442 | |
| 38,0.1931,79.33,0.9812082930615074,80.16110167810626 | |
| 39,0.175,80.44,0.9815773544699381,80.54892974025026 | |
| 40,0.1695,80.5,0.9741421825292578,80.45943074351564 | |
| 41,0.1556,80.55,0.9691943751567859,80.65334544261486 | |
| 42,0.139,80.65,0.9659411455170389,80.66826100907063 | |
| 43,0.1334,80.92,0.9666519684084295,80.47434704172868 | |
| 44,0.1305,80.89,0.9551008048525267,80.72792770640662 | |
| 45,0.1145,80.97,0.9540892773838134,81.02625692203677 | |
| 46,0.1095,81.41,0.9528565328372153,80.90692550868579 | |
| 47,0.1039,81.18,0.9488438052709023,80.57876276685401 | |
| 48,0.0988,81.24,0.9472993114633423,80.7577597679557 | |
| 49,0.0942,81.13,0.9416460762754011,80.71301133194515 | |
| 50,0.0896,,0.9394862801549537,80.68317776591226 | |