--- 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-latest 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: 0.8636363636363636 --- # ebayes/tree-crown-latest 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: 0.6589 - Accuracy: 0.8636 ## 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 | 36 | 1.5994 | 0.6364 | | No log | 2.0 | 72 | 1.2587 | 0.6818 | | No log | 3.0 | 108 | 1.0993 | 0.7045 | | No log | 4.0 | 144 | 0.9721 | 0.7955 | | No log | 5.0 | 180 | 0.9282 | 0.7955 | | No log | 6.0 | 216 | 0.8947 | 0.7955 | | No log | 7.0 | 252 | 0.8858 | 0.7727 | | No log | 8.0 | 288 | 0.8159 | 0.7955 | | No log | 9.0 | 324 | 0.7772 | 0.7727 | | No log | 10.0 | 360 | 0.7519 | 0.7955 | | No log | 11.0 | 396 | 0.6982 | 0.7955 | | No log | 12.0 | 432 | 0.6639 | 0.7955 | | No log | 13.0 | 468 | 0.6690 | 0.8409 | | 0.6601 | 14.0 | 504 | 0.6565 | 0.8409 | | 0.6601 | 15.0 | 540 | 0.6401 | 0.8409 | | 0.6601 | 16.0 | 576 | 0.5868 | 0.8864 | | 0.6601 | 17.0 | 612 | 0.5840 | 0.8864 | | 0.6601 | 18.0 | 648 | 0.6214 | 0.8409 | | 0.6601 | 19.0 | 684 | 0.6447 | 0.8636 | | 0.6601 | 20.0 | 720 | 0.6387 | 0.8409 | | 0.6601 | 21.0 | 756 | 0.5714 | 0.8636 | | 0.6601 | 22.0 | 792 | 0.5483 | 0.8864 | | 0.6601 | 23.0 | 828 | 0.5600 | 0.8864 | | 0.6601 | 24.0 | 864 | 0.5785 | 0.8864 | | 0.6601 | 25.0 | 900 | 0.5806 | 0.8864 | | 0.6601 | 26.0 | 936 | 0.5598 | 0.8636 | | 0.6601 | 27.0 | 972 | 0.5549 | 0.8864 | | 0.1909 | 28.0 | 1008 | 0.5145 | 0.8864 | | 0.1909 | 29.0 | 1044 | 0.5294 | 0.8636 | | 0.1909 | 30.0 | 1080 | 0.5846 | 0.8636 | | 0.1909 | 31.0 | 1116 | 0.5347 | 0.8864 | | 0.1909 | 32.0 | 1152 | 0.5251 | 0.8864 | | 0.1909 | 33.0 | 1188 | 0.5193 | 0.8864 | | 0.1909 | 34.0 | 1224 | 0.6406 | 0.8409 | | 0.1909 | 35.0 | 1260 | 0.5039 | 0.8864 | | 0.1909 | 36.0 | 1296 | 0.5137 | 0.8864 | | 0.1909 | 37.0 | 1332 | 0.6023 | 0.8636 | | 0.1909 | 38.0 | 1368 | 0.5625 | 0.8864 | | 0.1909 | 39.0 | 1404 | 0.5752 | 0.8864 | | 0.1909 | 40.0 | 1440 | 0.5903 | 0.8864 | | 0.1909 | 41.0 | 1476 | 0.5143 | 0.8864 | | 0.0968 | 42.0 | 1512 | 0.5261 | 0.8864 | | 0.0968 | 43.0 | 1548 | 0.5942 | 0.8864 | | 0.0968 | 44.0 | 1584 | 0.6026 | 0.8636 | | 0.0968 | 45.0 | 1620 | 0.5638 | 0.8864 | | 0.0968 | 46.0 | 1656 | 0.6019 | 0.8864 | | 0.0968 | 47.0 | 1692 | 0.5953 | 0.8864 | | 0.0968 | 48.0 | 1728 | 0.6043 | 0.8864 | | 0.0968 | 49.0 | 1764 | 0.5866 | 0.8864 | | 0.0968 | 50.0 | 1800 | 0.5090 | 0.8864 | | 0.0968 | 51.0 | 1836 | 0.5704 | 0.8864 | | 0.0968 | 52.0 | 1872 | 0.5579 | 0.8636 | | 0.0968 | 53.0 | 1908 | 0.5058 | 0.8864 | | 0.0968 | 54.0 | 1944 | 0.5418 | 0.8864 | | 0.0968 | 55.0 | 1980 | 0.5708 | 0.8864 | | 0.0656 | 56.0 | 2016 | 0.5818 | 0.8864 | | 0.0656 | 57.0 | 2052 | 0.5539 | 0.8864 | | 0.0656 | 58.0 | 2088 | 0.5849 | 0.8864 | | 0.0656 | 59.0 | 2124 | 0.5396 | 0.8864 | | 0.0656 | 60.0 | 2160 | 0.5631 | 0.8864 | | 0.0656 | 61.0 | 2196 | 0.5919 | 0.8864 | | 0.0656 | 62.0 | 2232 | 0.5955 | 0.8864 | | 0.0656 | 63.0 | 2268 | 0.5438 | 0.8864 | | 0.0656 | 64.0 | 2304 | 0.5989 | 0.8636 | | 0.0656 | 65.0 | 2340 | 0.5062 | 0.8864 | | 0.0656 | 66.0 | 2376 | 0.5820 | 0.8636 | | 0.0656 | 67.0 | 2412 | 0.5301 | 0.8864 | | 0.0656 | 68.0 | 2448 | 0.6138 | 0.8864 | | 0.0656 | 69.0 | 2484 | 0.5710 | 0.8636 | | 0.0491 | 70.0 | 2520 | 0.6141 | 0.8636 | | 0.0491 | 71.0 | 2556 | 0.6304 | 0.8636 | | 0.0491 | 72.0 | 2592 | 0.5568 | 0.8636 | | 0.0491 | 73.0 | 2628 | 0.6437 | 0.8636 | | 0.0491 | 74.0 | 2664 | 0.5329 | 0.8864 | | 0.0491 | 75.0 | 2700 | 0.6453 | 0.8864 | | 0.0491 | 76.0 | 2736 | 0.6267 | 0.8636 | | 0.0491 | 77.0 | 2772 | 0.6246 | 0.8636 | | 0.0491 | 78.0 | 2808 | 0.6408 | 0.8636 | | 0.0491 | 79.0 | 2844 | 0.6208 | 0.8636 | | 0.0491 | 80.0 | 2880 | 0.5944 | 0.8636 | | 0.0491 | 81.0 | 2916 | 0.6848 | 0.8636 | | 0.0491 | 82.0 | 2952 | 0.6700 | 0.8409 | | 0.0491 | 83.0 | 2988 | 0.5625 | 0.8864 | | 0.0474 | 84.0 | 3024 | 0.4997 | 0.8864 | | 0.0474 | 85.0 | 3060 | 0.6110 | 0.8864 | | 0.0474 | 86.0 | 3096 | 0.5661 | 0.8864 | | 0.0474 | 87.0 | 3132 | 0.5681 | 0.8864 | | 0.0474 | 88.0 | 3168 | 0.5794 | 0.8636 | | 0.0474 | 89.0 | 3204 | 0.6098 | 0.8864 | | 0.0474 | 90.0 | 3240 | 0.6009 | 0.8636 | | 0.0474 | 91.0 | 3276 | 0.5000 | 0.8864 | | 0.0474 | 92.0 | 3312 | 0.5285 | 0.8864 | | 0.0474 | 93.0 | 3348 | 0.5774 | 0.8864 | | 0.0474 | 94.0 | 3384 | 0.5896 | 0.8864 | | 0.0474 | 95.0 | 3420 | 0.5478 | 0.8864 | | 0.0474 | 96.0 | 3456 | 0.5815 | 0.8864 | | 0.0474 | 97.0 | 3492 | 0.5675 | 0.8864 | | 0.0393 | 98.0 | 3528 | 0.5773 | 0.8864 | | 0.0393 | 99.0 | 3564 | 0.6099 | 0.8864 | | 0.0393 | 100.0 | 3600 | 0.7255 | 0.8409 | | 0.0393 | 101.0 | 3636 | 0.6300 | 0.8864 | | 0.0393 | 102.0 | 3672 | 0.5979 | 0.8409 | | 0.0393 | 103.0 | 3708 | 0.6031 | 0.8864 | | 0.0393 | 104.0 | 3744 | 0.6200 | 0.8864 | | 0.0393 | 105.0 | 3780 | 0.6120 | 0.8864 | | 0.0393 | 106.0 | 3816 | 0.5514 | 0.9091 | | 0.0393 | 107.0 | 3852 | 0.6425 | 0.8864 | | 0.0393 | 108.0 | 3888 | 0.6152 | 0.8864 | | 0.0393 | 109.0 | 3924 | 0.6023 | 0.8864 | | 0.0393 | 110.0 | 3960 | 0.6170 | 0.8864 | | 0.0393 | 111.0 | 3996 | 0.6556 | 0.8864 | | 0.0404 | 112.0 | 4032 | 0.6380 | 0.8864 | | 0.0404 | 113.0 | 4068 | 0.6216 | 0.8864 | | 0.0404 | 114.0 | 4104 | 0.5775 | 0.8864 | | 0.0404 | 115.0 | 4140 | 0.6120 | 0.8864 | | 0.0404 | 116.0 | 4176 | 0.6221 | 0.8864 | | 0.0404 | 117.0 | 4212 | 0.6807 | 0.8636 | | 0.0404 | 118.0 | 4248 | 0.6805 | 0.8636 | | 0.0404 | 119.0 | 4284 | 0.6660 | 0.8636 | | 0.0404 | 120.0 | 4320 | 0.6626 | 0.8636 | | 0.0404 | 121.0 | 4356 | 0.6656 | 0.8636 | | 0.0404 | 122.0 | 4392 | 0.6151 | 0.8636 | | 0.0404 | 123.0 | 4428 | 0.6525 | 0.8636 | | 0.0404 | 124.0 | 4464 | 0.6311 | 0.8636 | | 0.0268 | 125.0 | 4500 | 0.6375 | 0.8636 | | 0.0268 | 126.0 | 4536 | 0.6252 | 0.8636 | | 0.0268 | 127.0 | 4572 | 0.6182 | 0.8409 | | 0.0268 | 128.0 | 4608 | 0.6195 | 0.8636 | | 0.0268 | 129.0 | 4644 | 0.6417 | 0.8636 | | 0.0268 | 130.0 | 4680 | 0.6440 | 0.8636 | | 0.0268 | 131.0 | 4716 | 0.6726 | 0.8636 | | 0.0268 | 132.0 | 4752 | 0.6781 | 0.8636 | | 0.0268 | 133.0 | 4788 | 0.6412 | 0.8636 | | 0.0268 | 134.0 | 4824 | 0.6514 | 0.8636 | | 0.0268 | 135.0 | 4860 | 0.6452 | 0.8636 | | 0.0268 | 136.0 | 4896 | 0.6453 | 0.8864 | | 0.0268 | 137.0 | 4932 | 0.6408 | 0.8864 | | 0.0268 | 138.0 | 4968 | 0.6461 | 0.8864 | | 0.0244 | 139.0 | 5004 | 0.6597 | 0.8864 | | 0.0244 | 140.0 | 5040 | 0.6539 | 0.8864 | | 0.0244 | 141.0 | 5076 | 0.6415 | 0.8864 | | 0.0244 | 142.0 | 5112 | 0.6438 | 0.8864 | | 0.0244 | 143.0 | 5148 | 0.6581 | 0.8636 | | 0.0244 | 144.0 | 5184 | 0.6570 | 0.8636 | | 0.0244 | 145.0 | 5220 | 0.6626 | 0.8636 | | 0.0244 | 146.0 | 5256 | 0.6622 | 0.8636 | | 0.0244 | 147.0 | 5292 | 0.6647 | 0.8636 | | 0.0244 | 148.0 | 5328 | 0.6619 | 0.8636 | | 0.0244 | 149.0 | 5364 | 0.6591 | 0.8636 | | 0.0244 | 150.0 | 5400 | 0.6589 | 0.8636 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1