generated_from_trainer

<!-- 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. -->

plant-seedlings-freeze-0-6-aug-3-all-train

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.1589 0.25 100 0.3561 0.9141
0.1629 0.49 200 0.3792 0.8932
0.1222 0.74 300 0.3749 0.8975
0.1363 0.98 400 0.3021 0.9122
0.0699 1.23 500 0.4802 0.8883
0.0759 1.47 600 0.3129 0.9214
0.2157 1.72 700 0.3362 0.9159
0.1148 1.97 800 0.2898 0.9122
0.3137 2.21 900 0.4267 0.8969
0.072 2.46 1000 0.3180 0.9141
0.0775 2.7 1100 0.3856 0.9067
0.1019 2.95 1200 0.3182 0.9153
0.1119 3.19 1300 0.4458 0.8944
0.1342 3.44 1400 0.4718 0.8889
0.1658 3.69 1500 0.3697 0.9012
0.1609 3.93 1600 0.4079 0.9024
0.1223 4.18 1700 0.3688 0.9147
0.1821 4.42 1800 0.3392 0.9116
0.0901 4.67 1900 0.3726 0.8969
0.0857 4.91 2000 0.3158 0.9196
0.1245 5.16 2100 0.3503 0.9122
0.133 5.41 2200 0.3712 0.9134
0.171 5.65 2300 0.3543 0.9067
0.1222 5.9 2400 0.3031 0.9227
0.1504 6.14 2500 0.3356 0.9085
0.0889 6.39 2600 0.3695 0.9116
0.0185 6.63 2700 0.3509 0.9141
0.1201 6.88 2800 0.3330 0.9177
0.0766 7.13 2900 0.2718 0.9251
0.0998 7.37 3000 0.3471 0.9233
0.1654 7.62 3100 0.3285 0.9196
0.0529 7.86 3200 0.3394 0.9190
0.1199 8.11 3300 0.2968 0.9294
0.0338 8.35 3400 0.2784 0.9251
0.124 8.6 3500 0.3099 0.9251
0.0581 8.85 3600 0.3372 0.9263
0.1776 9.09 3700 0.3580 0.9134
0.1598 9.34 3800 0.3158 0.9196
0.1122 9.58 3900 0.3369 0.9190
0.0808 9.83 4000 0.3259 0.9368
0.1086 10.07 4100 0.3691 0.9190
0.0197 10.32 4200 0.3101 0.9355
0.065 10.57 4300 0.3479 0.9227
0.1183 10.81 4400 0.3281 0.9319
0.044 11.06 4500 0.4357 0.9134
0.1021 11.3 4600 0.3211 0.9337
0.0615 11.55 4700 0.2947 0.9398
0.0664 11.79 4800 0.4421 0.9184
0.092 12.04 4900 0.3333 0.9202
0.1544 12.29 5000 0.3062 0.9245
0.1324 12.53 5100 0.2756 0.9294
0.1132 12.78 5200 0.2570 0.9362
0.0899 13.02 5300 0.2486 0.9386
0.0712 13.27 5400 0.2878 0.9306
0.0411 13.51 5500 0.2663 0.9368
0.0559 13.76 5600 0.2751 0.9355
0.0928 14.0 5700 0.3093 0.9269
0.0504 14.25 5800 0.2954 0.9319
0.0995 14.5 5900 0.2636 0.9337
0.1139 14.74 6000 0.2827 0.9349
0.0992 14.99 6100 0.2662 0.9368
0.1519 15.23 6200 0.2720 0.9398
0.0192 15.48 6300 0.3252 0.9269
0.0592 15.72 6400 0.3382 0.9263
0.0382 15.97 6500 0.2710 0.9349
0.0723 16.22 6600 0.2671 0.9374
0.0073 16.46 6700 0.3451 0.9263
0.1796 16.71 6800 0.3196 0.9196
0.0919 16.95 6900 0.2464 0.9337
0.0739 17.2 7000 0.2258 0.9392
0.0468 17.44 7100 0.2483 0.9411
0.145 17.69 7200 0.2639 0.9312
0.0243 17.94 7300 0.2574 0.9362
0.0648 18.18 7400 0.2554 0.9331
0.0508 18.43 7500 0.2554 0.9374
0.0475 18.67 7600 0.2915 0.9337
0.0708 18.92 7700 0.2801 0.9300
0.1476 19.16 7800 0.2479 0.9411
0.1535 19.41 7900 0.2412 0.9411
0.0873 19.66 8000 0.2544 0.9398
0.0416 19.9 8100 0.2334 0.9423
0.1157 20.15 8200 0.2059 0.9540
0.039 20.39 8300 0.2601 0.9362
0.0223 20.64 8400 0.2234 0.9484
0.0779 20.88 8500 0.2468 0.9405
0.0604 21.13 8600 0.2334 0.9374
0.1206 21.38 8700 0.2504 0.9398
0.0738 21.62 8800 0.2505 0.9398
0.0438 21.87 8900 0.2148 0.9472
0.0689 22.11 9000 0.2286 0.9435
0.0505 22.36 9100 0.1956 0.9472
0.0581 22.6 9200 0.2104 0.9484
0.1575 22.85 9300 0.2309 0.9441
0.048 23.1 9400 0.2685 0.9398
0.0784 23.34 9500 0.2329 0.9454
0.0771 23.59 9600 0.2294 0.9466
0.0545 23.83 9700 0.2037 0.9484
0.0481 24.08 9800 0.1994 0.9540
0.0663 24.32 9900 0.1993 0.9490
0.0921 24.57 10000 0.2204 0.9521
0.0939 24.82 10100 0.2362 0.9454

Framework versions