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convnextv2-tiny-1k-224-finetuned-all_wear

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convnextv2
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metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnextv2-tiny-1k-224-finetuned-eurosat
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8288288288288288

convnextv2-tiny-1k-224-finetuned-eurosat

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7923
  • Accuracy: 0.8288

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.5919 0.992 31 3.5736 0.0315
3.5392 1.984 62 3.4990 0.0473
3.4591 2.976 93 3.3992 0.1104
3.3361 4.0 125 3.2526 0.2523
3.2066 4.992 156 3.0851 0.3761
3.0024 5.984 187 2.8376 0.4437
2.8094 6.976 218 2.6320 0.4910
2.509 8.0 250 2.3765 0.5360
2.2526 8.992 281 2.0400 0.5923
1.9442 9.984 312 1.7940 0.6396
1.7672 10.9760 343 1.5892 0.6824
1.5273 12.0 375 1.3500 0.7185
1.3854 12.992 406 1.2243 0.7162
1.197 13.984 437 1.1022 0.7387
1.1114 14.9760 468 1.0138 0.7613
0.9364 16.0 500 0.9164 0.7748
0.8755 16.992 531 0.9058 0.7523
0.7473 17.984 562 0.8045 0.7928
0.7189 18.976 593 0.7735 0.7883
0.6461 20.0 625 0.6876 0.8198
0.6041 20.992 656 0.7212 0.7973
0.5016 21.984 687 0.6611 0.8198
0.4996 22.976 718 0.6110 0.8153
0.4825 24.0 750 0.6476 0.8063
0.434 24.992 781 0.6793 0.8041
0.4296 25.984 812 0.6015 0.8018
0.36 26.976 843 0.6615 0.8063
0.3646 28.0 875 0.6059 0.8221
0.3542 28.992 906 0.6973 0.7928
0.3091 29.984 937 0.6400 0.8266
0.2774 30.976 968 0.5798 0.8266
0.3166 32.0 1000 0.6134 0.8333
0.2878 32.992 1031 0.6353 0.8063
0.2529 33.984 1062 0.6628 0.8243
0.2601 34.976 1093 0.6367 0.8041
0.2208 36.0 1125 0.6313 0.8288
0.2342 36.992 1156 0.5969 0.8378
0.2122 37.984 1187 0.6391 0.8198
0.1791 38.976 1218 0.6771 0.8108
0.2113 40.0 1250 0.7035 0.8086
0.1703 40.992 1281 0.7096 0.8153
0.1751 41.984 1312 0.5964 0.8446
0.1889 42.976 1343 0.6607 0.8446
0.1791 44.0 1375 0.7000 0.8243
0.1372 44.992 1406 0.6866 0.8243
0.1785 45.984 1437 0.6621 0.8266
0.1469 46.976 1468 0.6391 0.8266
0.1628 48.0 1500 0.6623 0.8356
0.1425 48.992 1531 0.6443 0.8288
0.1727 49.984 1562 0.6361 0.8446
0.1442 50.976 1593 0.6397 0.8491
0.1386 52.0 1625 0.6835 0.8423
0.1564 52.992 1656 0.7072 0.8266
0.1151 53.984 1687 0.6835 0.8311
0.1446 54.976 1718 0.7347 0.8198
0.1353 56.0 1750 0.6935 0.8401
0.13 56.992 1781 0.7337 0.8198
0.1312 57.984 1812 0.6625 0.8311
0.1201 58.976 1843 0.6956 0.8243
0.1411 60.0 1875 0.7290 0.8243
0.1116 60.992 1906 0.7052 0.8356
0.1251 61.984 1937 0.6915 0.8311
0.1101 62.976 1968 0.6457 0.8378
0.0883 64.0 2000 0.6553 0.8378
0.1225 64.992 2031 0.6454 0.8401
0.1135 65.984 2062 0.6616 0.8514
0.1009 66.976 2093 0.6375 0.8536
0.1027 68.0 2125 0.6754 0.8266
0.0925 68.992 2156 0.7497 0.8176
0.0878 69.984 2187 0.6573 0.8491
0.1093 70.976 2218 0.7015 0.8356
0.1024 72.0 2250 0.6907 0.8446
0.0934 72.992 2281 0.7059 0.8356
0.103 73.984 2312 0.7159 0.8356
0.0974 74.976 2343 0.7324 0.8266
0.1049 76.0 2375 0.7397 0.8311
0.097 76.992 2406 0.7529 0.8176
0.0816 77.984 2437 0.7175 0.8423
0.0902 78.976 2468 0.7745 0.8288
0.0827 80.0 2500 0.7017 0.8423
0.0818 80.992 2531 0.7712 0.8243
0.076 81.984 2562 0.7341 0.8423
0.0837 82.976 2593 0.7242 0.8491
0.0743 84.0 2625 0.6999 0.8446
0.0552 84.992 2656 0.6875 0.8401
0.0762 85.984 2687 0.6743 0.8581
0.0742 86.976 2718 0.7027 0.8446
0.0708 88.0 2750 0.7367 0.8356
0.086 88.992 2781 0.6905 0.8401
0.0575 89.984 2812 0.7041 0.8423
0.0733 90.976 2843 0.6465 0.8423
0.0701 92.0 2875 0.7066 0.8401
0.0782 92.992 2906 0.6955 0.8243
0.0754 93.984 2937 0.6836 0.8468
0.0545 94.976 2968 0.7290 0.8288
0.0913 96.0 3000 0.7665 0.8266
0.0816 96.992 3031 0.7661 0.8311
0.0696 97.984 3062 0.6921 0.8356
0.0627 98.976 3093 0.7070 0.8446
0.0562 100.0 3125 0.7442 0.8401
0.0742 100.992 3156 0.7000 0.8423
0.0545 101.984 3187 0.7312 0.8401
0.0635 102.976 3218 0.7231 0.8491
0.0608 104.0 3250 0.7332 0.8333
0.0769 104.992 3281 0.7328 0.8356
0.057 105.984 3312 0.6954 0.8378
0.0447 106.976 3343 0.7006 0.8423
0.0629 108.0 3375 0.7149 0.8423
0.0394 108.992 3406 0.7469 0.8378
0.0602 109.984 3437 0.7274 0.8468
0.0635 110.976 3468 0.7495 0.8446
0.0565 112.0 3500 0.7885 0.8401
0.035 112.992 3531 0.7178 0.8468
0.0604 113.984 3562 0.7574 0.8356
0.0507 114.976 3593 0.7901 0.8266
0.05 116.0 3625 0.7730 0.8198
0.0465 116.992 3656 0.7967 0.8401
0.042 117.984 3687 0.7767 0.8423
0.0609 118.976 3718 0.7872 0.8378
0.0379 120.0 3750 0.7685 0.8514
0.0579 120.992 3781 0.7709 0.8423
0.0471 121.984 3812 0.7601 0.8423
0.0488 122.976 3843 0.8231 0.8356
0.0531 124.0 3875 0.8016 0.8378
0.0446 124.992 3906 0.7806 0.8423
0.0479 125.984 3937 0.7668 0.8378
0.0525 126.976 3968 0.7874 0.8288
0.0512 128.0 4000 0.7652 0.8311
0.0473 128.992 4031 0.7721 0.8356
0.0579 129.984 4062 0.7607 0.8356
0.0444 130.976 4093 0.7917 0.8356
0.0462 132.0 4125 0.7877 0.8333
0.0483 132.992 4156 0.8122 0.8401
0.042 133.984 4187 0.7956 0.8378
0.0439 134.976 4218 0.8281 0.8311
0.0458 136.0 4250 0.7723 0.8446
0.0307 136.992 4281 0.7686 0.8446
0.0481 137.984 4312 0.7834 0.8378
0.0503 138.976 4343 0.7987 0.8378
0.038 140.0 4375 0.8156 0.8311
0.0472 140.992 4406 0.8030 0.8356
0.0282 141.984 4437 0.7884 0.8378
0.0541 142.976 4468 0.7969 0.8311
0.0415 144.0 4500 0.7899 0.8333
0.0579 144.992 4531 0.7979 0.8266
0.048 145.984 4562 0.7935 0.8288
0.0353 146.976 4593 0.7933 0.8288
0.0438 148.0 4625 0.7916 0.8288
0.0487 148.8 4650 0.7923 0.8288

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1