| --- |
| license: apache-2.0 |
| tags: |
| - masked-auto-encoding |
| - generated_from_trainer |
| datasets: |
| - image_folder |
| base_model: facebook/vit-mae-base |
| model-index: |
| - name: test_mae_flysheet |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # test_mae_flysheet |
|
|
| This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/flysheet dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2675 |
|
|
| ## 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: 3.75e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 1337 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.05 |
| - num_epochs: 100.0 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 2.284 | 1.0 | 28 | 2.2812 | |
| | 2.137 | 2.0 | 56 | 2.0288 | |
| | 1.6016 | 3.0 | 84 | 1.2437 | |
| | 0.8055 | 4.0 | 112 | 0.7419 | |
| | 0.5304 | 5.0 | 140 | 0.5151 | |
| | 0.4873 | 6.0 | 168 | 0.4884 | |
| | 0.442 | 7.0 | 196 | 0.4441 | |
| | 0.4039 | 8.0 | 224 | 0.4159 | |
| | 0.3866 | 9.0 | 252 | 0.3975 | |
| | 0.391 | 10.0 | 280 | 0.3869 | |
| | 0.3549 | 11.0 | 308 | 0.3801 | |
| | 0.3462 | 12.0 | 336 | 0.3577 | |
| | 0.3402 | 13.0 | 364 | 0.3519 | |
| | 0.3357 | 14.0 | 392 | 0.3447 | |
| | 0.3474 | 15.0 | 420 | 0.3369 | |
| | 0.3254 | 16.0 | 448 | 0.3386 | |
| | 0.3033 | 17.0 | 476 | 0.3294 | |
| | 0.3047 | 18.0 | 504 | 0.3274 | |
| | 0.3103 | 19.0 | 532 | 0.3209 | |
| | 0.3067 | 20.0 | 560 | 0.3186 | |
| | 0.2959 | 21.0 | 588 | 0.3190 | |
| | 0.2899 | 22.0 | 616 | 0.3147 | |
| | 0.2872 | 23.0 | 644 | 0.3082 | |
| | 0.2956 | 24.0 | 672 | 0.3070 | |
| | 0.2865 | 25.0 | 700 | 0.3072 | |
| | 0.2947 | 26.0 | 728 | 0.3072 | |
| | 0.2811 | 27.0 | 756 | 0.3131 | |
| | 0.2935 | 28.0 | 784 | 0.3069 | |
| | 0.2814 | 29.0 | 812 | 0.3043 | |
| | 0.2753 | 30.0 | 840 | 0.2984 | |
| | 0.2823 | 31.0 | 868 | 0.2995 | |
| | 0.2962 | 32.0 | 896 | 0.3012 | |
| | 0.2869 | 33.0 | 924 | 0.3050 | |
| | 0.2833 | 34.0 | 952 | 0.2960 | |
| | 0.2892 | 35.0 | 980 | 0.3039 | |
| | 0.2764 | 36.0 | 1008 | 0.3010 | |
| | 0.2807 | 37.0 | 1036 | 0.2998 | |
| | 0.2843 | 38.0 | 1064 | 0.2989 | |
| | 0.2808 | 39.0 | 1092 | 0.2970 | |
| | 0.2862 | 40.0 | 1120 | 0.2940 | |
| | 0.2601 | 41.0 | 1148 | 0.2952 | |
| | 0.2742 | 42.0 | 1176 | 0.2940 | |
| | 0.2791 | 43.0 | 1204 | 0.2997 | |
| | 0.2759 | 44.0 | 1232 | 0.2951 | |
| | 0.2819 | 45.0 | 1260 | 0.2896 | |
| | 0.287 | 46.0 | 1288 | 0.2938 | |
| | 0.2711 | 47.0 | 1316 | 0.2973 | |
| | 0.2782 | 48.0 | 1344 | 0.2946 | |
| | 0.2674 | 49.0 | 1372 | 0.2913 | |
| | 0.268 | 50.0 | 1400 | 0.2944 | |
| | 0.2624 | 51.0 | 1428 | 0.2940 | |
| | 0.2842 | 52.0 | 1456 | 0.2978 | |
| | 0.2753 | 53.0 | 1484 | 0.2951 | |
| | 0.2733 | 54.0 | 1512 | 0.2880 | |
| | 0.2782 | 55.0 | 1540 | 0.2969 | |
| | 0.2789 | 56.0 | 1568 | 0.2919 | |
| | 0.2815 | 57.0 | 1596 | 0.2916 | |
| | 0.2629 | 58.0 | 1624 | 0.2947 | |
| | 0.2716 | 59.0 | 1652 | 0.2828 | |
| | 0.2623 | 60.0 | 1680 | 0.2924 | |
| | 0.2773 | 61.0 | 1708 | 0.2765 | |
| | 0.268 | 62.0 | 1736 | 0.2754 | |
| | 0.2839 | 63.0 | 1764 | 0.2744 | |
| | 0.2684 | 64.0 | 1792 | 0.2744 | |
| | 0.2865 | 65.0 | 1820 | 0.2716 | |
| | 0.2845 | 66.0 | 1848 | 0.2769 | |
| | 0.2663 | 67.0 | 1876 | 0.2754 | |
| | 0.269 | 68.0 | 1904 | 0.2737 | |
| | 0.2681 | 69.0 | 1932 | 0.2697 | |
| | 0.2748 | 70.0 | 1960 | 0.2779 | |
| | 0.2769 | 71.0 | 1988 | 0.2728 | |
| | 0.2805 | 72.0 | 2016 | 0.2729 | |
| | 0.2771 | 73.0 | 2044 | 0.2728 | |
| | 0.2717 | 74.0 | 2072 | 0.2749 | |
| | 0.267 | 75.0 | 2100 | 0.2732 | |
| | 0.2812 | 76.0 | 2128 | 0.2743 | |
| | 0.2749 | 77.0 | 2156 | 0.2739 | |
| | 0.2746 | 78.0 | 2184 | 0.2730 | |
| | 0.2707 | 79.0 | 2212 | 0.2743 | |
| | 0.2644 | 80.0 | 2240 | 0.2740 | |
| | 0.2691 | 81.0 | 2268 | 0.2727 | |
| | 0.2679 | 82.0 | 2296 | 0.2771 | |
| | 0.2748 | 83.0 | 2324 | 0.2744 | |
| | 0.2744 | 84.0 | 2352 | 0.2703 | |
| | 0.2715 | 85.0 | 2380 | 0.2733 | |
| | 0.2682 | 86.0 | 2408 | 0.2715 | |
| | 0.2641 | 87.0 | 2436 | 0.2722 | |
| | 0.274 | 88.0 | 2464 | 0.2748 | |
| | 0.2669 | 89.0 | 2492 | 0.2753 | |
| | 0.2707 | 90.0 | 2520 | 0.2724 | |
| | 0.2755 | 91.0 | 2548 | 0.2703 | |
| | 0.2769 | 92.0 | 2576 | 0.2737 | |
| | 0.2659 | 93.0 | 2604 | 0.2721 | |
| | 0.2674 | 94.0 | 2632 | 0.2763 | |
| | 0.2723 | 95.0 | 2660 | 0.2723 | |
| | 0.2723 | 96.0 | 2688 | 0.2744 | |
| | 0.272 | 97.0 | 2716 | 0.2686 | |
| | 0.27 | 98.0 | 2744 | 0.2728 | |
| | 0.2721 | 99.0 | 2772 | 0.2743 | |
| | 0.2692 | 100.0 | 2800 | 0.2748 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.18.0.dev0 |
| - Pytorch 1.10.0+cu111 |
| - Datasets 1.18.4 |
| - Tokenizers 0.11.6 |
| |
