--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: ebayes/tree_crown_model-test23 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: 0.8695652173913043 --- # ebayes/tree_crown_model-test23 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2838 - Accuracy: 0.8696 ## 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: 2e-05 - train_batch_size: 10 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 11 | 1.9719 | 0.6522 | | No log | 2.0 | 22 | 1.6381 | 0.6522 | | No log | 3.0 | 33 | 1.3958 | 0.6522 | | No log | 4.0 | 44 | 1.2541 | 0.6522 | | No log | 5.0 | 55 | 1.1207 | 0.6957 | | No log | 6.0 | 66 | 1.0262 | 0.8261 | | No log | 7.0 | 77 | 0.9421 | 0.8261 | | No log | 8.0 | 88 | 0.9031 | 0.8261 | | No log | 9.0 | 99 | 0.8398 | 0.8261 | | No log | 10.0 | 110 | 0.7975 | 0.8261 | | No log | 11.0 | 121 | 0.7547 | 0.8696 | | No log | 12.0 | 132 | 0.7451 | 0.8696 | | No log | 13.0 | 143 | 0.7017 | 0.8696 | | No log | 14.0 | 154 | 0.6789 | 0.8696 | | No log | 15.0 | 165 | 0.6688 | 0.8696 | | No log | 16.0 | 176 | 0.6809 | 0.8696 | | No log | 17.0 | 187 | 0.6342 | 0.8696 | | No log | 18.0 | 198 | 0.6437 | 0.8696 | | No log | 19.0 | 209 | 0.5902 | 0.8696 | | No log | 20.0 | 220 | 0.5874 | 0.8696 | | No log | 21.0 | 231 | 0.6042 | 0.8696 | | No log | 22.0 | 242 | 0.5682 | 0.8696 | | No log | 23.0 | 253 | 0.5395 | 0.8696 | | No log | 24.0 | 264 | 0.5487 | 0.8696 | | No log | 25.0 | 275 | 0.5239 | 0.8696 | | No log | 26.0 | 286 | 0.5436 | 0.8696 | | No log | 27.0 | 297 | 0.5169 | 0.8696 | | No log | 28.0 | 308 | 0.5189 | 0.8696 | | No log | 29.0 | 319 | 0.5314 | 0.8261 | | No log | 30.0 | 330 | 0.4707 | 0.8696 | | No log | 31.0 | 341 | 0.5169 | 0.8261 | | No log | 32.0 | 352 | 0.5229 | 0.8696 | | No log | 33.0 | 363 | 0.4598 | 0.8696 | | No log | 34.0 | 374 | 0.4911 | 0.8696 | | No log | 35.0 | 385 | 0.4516 | 0.8696 | | No log | 36.0 | 396 | 0.4121 | 0.9130 | | No log | 37.0 | 407 | 0.4875 | 0.8696 | | No log | 38.0 | 418 | 0.4147 | 0.9130 | | No log | 39.0 | 429 | 0.5118 | 0.8696 | | No log | 40.0 | 440 | 0.4266 | 0.8696 | | No log | 41.0 | 451 | 0.4114 | 0.8696 | | No log | 42.0 | 462 | 0.4549 | 0.8261 | | No log | 43.0 | 473 | 0.3795 | 0.9565 | | No log | 44.0 | 484 | 0.4286 | 0.8696 | | No log | 45.0 | 495 | 0.4409 | 0.8696 | | 0.6437 | 46.0 | 506 | 0.4099 | 0.8696 | | 0.6437 | 47.0 | 517 | 0.4075 | 0.9130 | | 0.6437 | 48.0 | 528 | 0.3886 | 0.9130 | | 0.6437 | 49.0 | 539 | 0.3900 | 0.8696 | | 0.6437 | 50.0 | 550 | 0.3947 | 0.8696 | | 0.6437 | 51.0 | 561 | 0.3676 | 0.8696 | | 0.6437 | 52.0 | 572 | 0.3560 | 0.9130 | | 0.6437 | 53.0 | 583 | 0.4100 | 0.8696 | | 0.6437 | 54.0 | 594 | 0.4078 | 0.8696 | | 0.6437 | 55.0 | 605 | 0.4357 | 0.8696 | | 0.6437 | 56.0 | 616 | 0.3815 | 0.8696 | | 0.6437 | 57.0 | 627 | 0.4172 | 0.8696 | | 0.6437 | 58.0 | 638 | 0.4781 | 0.8696 | | 0.6437 | 59.0 | 649 | 0.3847 | 0.8696 | | 0.6437 | 60.0 | 660 | 0.3260 | 0.9130 | | 0.6437 | 61.0 | 671 | 0.3578 | 0.8696 | | 0.6437 | 62.0 | 682 | 0.3096 | 0.9130 | | 0.6437 | 63.0 | 693 | 0.2946 | 0.9130 | | 0.6437 | 64.0 | 704 | 0.3383 | 0.8696 | | 0.6437 | 65.0 | 715 | 0.3748 | 0.8696 | | 0.6437 | 66.0 | 726 | 0.3199 | 0.9130 | | 0.6437 | 67.0 | 737 | 0.3761 | 0.8696 | | 0.6437 | 68.0 | 748 | 0.3332 | 0.8696 | | 0.6437 | 69.0 | 759 | 0.2815 | 0.9130 | | 0.6437 | 70.0 | 770 | 0.3236 | 0.8696 | | 0.6437 | 71.0 | 781 | 0.2962 | 0.9130 | | 0.6437 | 72.0 | 792 | 0.3075 | 0.9130 | | 0.6437 | 73.0 | 803 | 0.3461 | 0.8696 | | 0.6437 | 74.0 | 814 | 0.3138 | 0.9130 | | 0.6437 | 75.0 | 825 | 0.3043 | 0.9130 | | 0.6437 | 76.0 | 836 | 0.2967 | 0.9130 | | 0.6437 | 77.0 | 847 | 0.3008 | 0.9130 | | 0.6437 | 78.0 | 858 | 0.2856 | 0.9130 | | 0.6437 | 79.0 | 869 | 0.2939 | 0.9130 | | 0.6437 | 80.0 | 880 | 0.3491 | 0.9130 | | 0.6437 | 81.0 | 891 | 0.3049 | 0.9130 | | 0.6437 | 82.0 | 902 | 0.3577 | 0.8696 | | 0.6437 | 83.0 | 913 | 0.3369 | 0.8696 | | 0.6437 | 84.0 | 924 | 0.2952 | 0.9130 | | 0.6437 | 85.0 | 935 | 0.2881 | 0.9130 | | 0.6437 | 86.0 | 946 | 0.3349 | 0.8696 | | 0.6437 | 87.0 | 957 | 0.3025 | 0.9130 | | 0.6437 | 88.0 | 968 | 0.2943 | 0.8696 | | 0.6437 | 89.0 | 979 | 0.3035 | 0.9130 | | 0.6437 | 90.0 | 990 | 0.2599 | 0.9130 | | 0.1677 | 91.0 | 1001 | 0.3061 | 0.8696 | | 0.1677 | 92.0 | 1012 | 0.4316 | 0.8261 | | 0.1677 | 93.0 | 1023 | 0.3431 | 0.8696 | | 0.1677 | 94.0 | 1034 | 0.3246 | 0.8696 | | 0.1677 | 95.0 | 1045 | 0.3256 | 0.8696 | | 0.1677 | 96.0 | 1056 | 0.2846 | 0.9130 | | 0.1677 | 97.0 | 1067 | 0.3077 | 0.8696 | | 0.1677 | 98.0 | 1078 | 0.2988 | 0.9130 | | 0.1677 | 99.0 | 1089 | 0.2957 | 0.9130 | | 0.1677 | 100.0 | 1100 | 0.2983 | 0.9130 | | 0.1677 | 101.0 | 1111 | 0.2908 | 0.8696 | | 0.1677 | 102.0 | 1122 | 0.2715 | 0.9130 | | 0.1677 | 103.0 | 1133 | 0.3208 | 0.9130 | | 0.1677 | 104.0 | 1144 | 0.3622 | 0.8261 | | 0.1677 | 105.0 | 1155 | 0.3314 | 0.8696 | | 0.1677 | 106.0 | 1166 | 0.3226 | 0.9130 | | 0.1677 | 107.0 | 1177 | 0.3009 | 0.9565 | | 0.1677 | 108.0 | 1188 | 0.3162 | 0.9130 | | 0.1677 | 109.0 | 1199 | 0.2927 | 0.9565 | | 0.1677 | 110.0 | 1210 | 0.2434 | 0.9130 | | 0.1677 | 111.0 | 1221 | 0.3389 | 0.8696 | | 0.1677 | 112.0 | 1232 | 0.3686 | 0.8696 | | 0.1677 | 113.0 | 1243 | 0.3192 | 0.9130 | | 0.1677 | 114.0 | 1254 | 0.2720 | 0.8696 | | 0.1677 | 115.0 | 1265 | 0.2955 | 0.8696 | | 0.1677 | 116.0 | 1276 | 0.3318 | 0.9130 | | 0.1677 | 117.0 | 1287 | 0.3248 | 0.9130 | | 0.1677 | 118.0 | 1298 | 0.3115 | 0.8696 | | 0.1677 | 119.0 | 1309 | 0.2711 | 0.9130 | | 0.1677 | 120.0 | 1320 | 0.2592 | 0.8696 | | 0.1677 | 121.0 | 1331 | 0.2830 | 0.8696 | | 0.1677 | 122.0 | 1342 | 0.2956 | 0.9130 | | 0.1677 | 123.0 | 1353 | 0.3158 | 0.9130 | | 0.1677 | 124.0 | 1364 | 0.3328 | 0.8696 | | 0.1677 | 125.0 | 1375 | 0.3487 | 0.8696 | | 0.1677 | 126.0 | 1386 | 0.3375 | 0.8696 | | 0.1677 | 127.0 | 1397 | 0.3488 | 0.8696 | | 0.1677 | 128.0 | 1408 | 0.3377 | 0.8696 | | 0.1677 | 129.0 | 1419 | 0.3295 | 0.8696 | | 0.1677 | 130.0 | 1430 | 0.3198 | 0.8696 | | 0.1677 | 131.0 | 1441 | 0.2813 | 0.9130 | | 0.1677 | 132.0 | 1452 | 0.2730 | 0.9130 | | 0.1677 | 133.0 | 1463 | 0.2861 | 0.8696 | | 0.1677 | 134.0 | 1474 | 0.3158 | 0.8696 | | 0.1677 | 135.0 | 1485 | 0.3229 | 0.8696 | | 0.1677 | 136.0 | 1496 | 0.3169 | 0.8696 | | 0.1074 | 137.0 | 1507 | 0.3215 | 0.8696 | | 0.1074 | 138.0 | 1518 | 0.3039 | 0.8696 | | 0.1074 | 139.0 | 1529 | 0.2803 | 0.9130 | | 0.1074 | 140.0 | 1540 | 0.2707 | 0.9130 | | 0.1074 | 141.0 | 1551 | 0.2601 | 0.9130 | | 0.1074 | 142.0 | 1562 | 0.2599 | 0.9130 | | 0.1074 | 143.0 | 1573 | 0.2647 | 0.9130 | | 0.1074 | 144.0 | 1584 | 0.2697 | 0.9130 | | 0.1074 | 145.0 | 1595 | 0.2738 | 0.9130 | | 0.1074 | 146.0 | 1606 | 0.2759 | 0.9130 | | 0.1074 | 147.0 | 1617 | 0.2797 | 0.9130 | | 0.1074 | 148.0 | 1628 | 0.2798 | 0.9130 | | 0.1074 | 149.0 | 1639 | 0.2829 | 0.8696 | | 0.1074 | 150.0 | 1650 | 0.2838 | 0.8696 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1