CoolFace
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Melo1512/vit-msn-small-wbc-classifier-lowlr-500

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

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vit-msn-small-wbc-classifier-lowlr-500

This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.2021
  • —Accuracy: 0.9250

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 1e-07
  • —trainbatchsize: 64
  • —evalbatchsize: 64
  • —seed: 42
  • —gradientaccumulationsteps: 4
  • —totaltrainbatch_size: 256
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —lrschedulerwarmup_ratio: 0.1
  • —num_epochs: 500

Training results

Training LossEpochStepValidation LossAccuracy
1.54581.02081.60830.3335
1.55532.04161.58340.3335
1.51443.06241.54280.3335
1.44084.08321.48650.3335
1.36895.010401.41680.3335
1.32246.012481.33520.3343
1.2287.014561.24460.3462
1.15298.016641.15260.4247
1.07739.018721.06700.5535
1.005710.020801.00250.6327
0.957611.022880.95500.6800
0.903912.024960.91140.7124
0.868413.027040.86360.7392
0.835214.029120.81120.7623
0.78215.031200.76140.7764
0.735916.033280.71450.7867
0.704417.035360.67020.7948
0.656818.037440.63110.8020
0.629619.039520.59870.8069
0.631520.041600.56930.8136
0.614621.043680.54370.8181
0.563222.045760.52160.8247
0.570523.047840.50220.8309
0.550324.049920.48320.8363
0.554125.052000.46660.8425
0.526326.054080.45040.8468
0.494727.056160.43570.8514
0.528.058240.42450.8547
0.495929.060320.41120.8582
0.492930.062400.40050.8609
0.457831.064480.39100.8628
0.456532.066560.38160.8654
0.449233.068640.37370.8684
0.431934.070720.36540.8705
0.439535.072800.35870.8722
0.443636.074880.34980.8751
0.419637.076960.34400.8769
0.413838.079040.33810.8789
0.441439.081120.33360.8805
0.42440.083200.33030.8813
0.405841.085280.32410.8832
0.371442.087360.32100.8844
0.38443.089440.31960.8848
0.379544.091520.31630.8861
0.405245.093600.30800.8895
0.386446.095680.30970.8865
0.388147.097760.30120.8910
0.360648.099840.30560.8877
0.376549.0101920.29810.8907
0.376250.0104000.29460.8922
0.353551.0106080.30040.8899
0.366752.0108160.29180.8924
0.366353.0110240.29210.8923
0.373554.0112320.28740.8942
0.364955.0114400.28670.8947
0.353156.0116480.28280.8967
0.360857.0118560.28250.8963
0.348358.0120640.28110.8970
0.354459.0122720.27850.8982
0.376860.0124800.27300.9002
0.365961.0126880.27350.8995
0.354762.0128960.27320.8999
0.340363.0131040.26960.9003
0.348264.0133120.26870.9014
0.350365.0135200.26730.9011
0.324966.0137280.26780.9012
0.333467.0139360.26790.9014
0.330868.0141440.26460.9018
0.329769.0143520.26760.9007
0.356670.0145600.26090.9037
0.325671.0147680.26350.9027
0.343972.0149760.26060.9036
0.332473.0151840.25780.9051
0.331674.0153920.25520.9058
0.322475.0156000.25550.9060
0.329476.0158080.25430.9061
0.321177.0160160.25380.9063
0.332678.0162240.25610.9047
0.326179.0164320.25470.9059
0.308280.0166400.25540.9048
0.334181.0168480.25380.9057
0.335582.0170560.25080.9073
0.331183.0172640.24860.9081
0.351184.0174720.24780.9085
0.346785.0176800.25030.9070
0.31286.0178880.24670.9086
0.33687.0180960.24550.9090
0.306588.0183040.24840.9080
0.336589.0185120.24530.9094
0.310690.0187200.24540.9092
0.298491.0189280.24890.9089
0.30992.0191360.24400.9102
0.3193.0193440.24070.9108
0.329494.0195520.24160.9105
0.30995.0197600.24230.9106
0.314196.0199680.24420.9096
0.309897.0201760.24040.9112
0.320698.0203840.24140.9111
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0.2948108.0224640.23420.9128
0.309109.0226720.23210.9134
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0.289111.0230880.23310.9132
0.3103112.0232960.23430.9131
0.3124113.0235040.23300.9136
0.3305114.0237120.23200.9138
0.2994115.0239200.23230.9135
0.3011116.0241280.23160.9136
0.2999117.0243360.23180.9143
0.3082118.0245440.23220.9144
0.2923119.0247520.23020.9142
0.315120.0249600.23090.9142
0.3058121.0251680.22920.9143
0.3044122.0253760.22700.9160
0.2679123.0255840.23010.9146
0.3091124.0257920.22890.9152
0.2926125.0260000.22730.9156
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0.2968143.0297440.22190.9176
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0.2781145.0301600.22510.9163
0.2977146.0303680.22340.9173
0.318147.0305760.22170.9181
0.3054148.0307840.22220.9178
0.2889149.0309920.22230.9176
0.2739150.0312000.21950.9187
0.2746151.0314080.22230.9175
0.3057152.0316160.22020.9182
0.2913153.0318240.21870.9193
0.3006154.0320320.21860.9192
0.3062155.0322400.21800.9190
0.2891156.0324480.22020.9185
0.3066157.0326560.21900.9182
0.2989158.0328640.21850.9191
0.2885159.0330720.22040.9184
0.2936160.0332800.21730.9194
0.2872161.0334880.22090.9179
0.3034162.0336960.21850.9191
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0.3209164.0341120.21790.9191
0.2718165.0343200.21880.9190
0.299166.0345280.21990.9185
0.2725167.0347360.21800.9194
0.3013168.0349440.21660.9198
0.3017169.0351520.21620.9193
0.3002170.0353600.21860.9191
0.2901171.0355680.21890.9184
0.2687172.0357760.21590.9200
0.2929173.0359840.21650.9196
0.2932174.0361920.21690.9195
0.2915175.0364000.21650.9201
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0.2879235.0488800.20870.9222
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0.2686467.0971360.20170.9248
0.2778468.0973440.20210.9245
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0.2659472.0981760.20200.9247
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0.2766474.0985920.20220.9247
0.2687475.0988000.20200.9248
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0.2644477.0992160.20240.9246
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0.2568499.01037920.20190.9248
0.2589500.01040000.20190.9248

Framework versions

  • —Transformers 4.44.2
  • —Pytorch 2.4.1+cu121
  • —Datasets 3.2.0
  • —Tokenizers 0.19.1