generated_from_trainer

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plant-seedlings-model-resnet-152-2

This model is a fine-tuned version of microsoft/resnet-152 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
2.0105 0.2 100 1.8953 0.4062
0.995 0.39 200 1.0372 0.6685
0.9354 0.59 300 0.7713 0.7461
0.6444 0.79 400 0.6037 0.8026
0.6477 0.98 500 0.5981 0.7991
0.6551 1.18 600 0.5224 0.8310
0.6466 1.38 700 0.5216 0.8222
0.4006 1.57 800 0.4244 0.8541
0.4484 1.77 900 0.4513 0.8566
0.5155 1.96 1000 0.4071 0.8649
0.518 2.16 1100 0.4155 0.8679
0.3762 2.36 1200 0.4152 0.8733
0.5409 2.55 1300 0.4038 0.8728
0.3184 2.75 1400 0.3683 0.8777
0.3861 2.95 1500 0.3675 0.8811
0.4824 3.14 1600 0.4404 0.8595
0.2793 3.34 1700 0.3696 0.8816
0.4095 3.54 1800 0.3102 0.8939
0.4151 3.73 1900 0.3558 0.8875
0.4036 3.93 2000 0.3215 0.8998
0.3547 4.13 2100 0.3511 0.8885
0.3071 4.32 2200 0.3376 0.8885
0.3448 4.52 2300 0.3807 0.8743
0.3574 4.72 2400 0.2826 0.9106
0.4435 4.91 2500 0.3275 0.9013
0.2811 5.11 2600 0.3285 0.9003
0.3514 5.3 2700 0.3562 0.8949
0.2323 5.5 2800 0.3023 0.9037
0.3736 5.7 2900 0.3012 0.8998
0.2659 5.89 3000 0.3243 0.8964
0.3934 6.09 3100 0.3007 0.9042
0.1951 6.29 3200 0.2643 0.9204
0.2882 6.48 3300 0.2816 0.9175
0.1887 6.68 3400 0.2669 0.9165
0.3612 6.88 3500 0.3215 0.8993
0.1423 7.07 3600 0.2684 0.9170
0.2935 7.27 3700 0.2826 0.9072
0.1549 7.47 3800 0.2783 0.9072
0.2678 7.66 3900 0.2535 0.9140
0.1954 7.86 4000 0.2578 0.9136
0.2319 8.06 4100 0.2595 0.9106
0.2016 8.25 4200 0.2671 0.9160
0.284 8.45 4300 0.2688 0.9136
0.1635 8.64 4400 0.3101 0.9111
0.2609 8.84 4500 0.2990 0.9145
0.1826 9.04 4600 0.2630 0.9077
0.2091 9.23 4700 0.2712 0.9180
0.1217 9.43 4800 0.2550 0.9126
0.198 9.63 4900 0.2648 0.9140
0.2123 9.82 5000 0.2819 0.9116
0.1399 10.02 5100 0.2690 0.9165
0.2429 10.22 5200 0.2685 0.9194
0.1376 10.41 5300 0.2930 0.9091
0.192 10.61 5400 0.3042 0.9101
0.1872 10.81 5500 0.2693 0.9160
0.1629 11.0 5600 0.2563 0.9185
0.2487 11.2 5700 0.2476 0.9258
0.242 11.39 5800 0.2407 0.9283
0.166 11.59 5900 0.2382 0.9317
0.1181 11.79 6000 0.2576 0.9140
0.1407 11.98 6100 0.2520 0.9268
0.1931 12.18 6200 0.2634 0.9204
0.1064 12.38 6300 0.2655 0.9219
0.1261 12.57 6400 0.2569 0.9209
0.1978 12.77 6500 0.2801 0.9131
0.2031 12.97 6600 0.2541 0.9190
0.1245 13.16 6700 0.2331 0.9249
0.2824 13.36 6800 0.2573 0.9199
0.1302 13.56 6900 0.2452 0.9219
0.0825 13.75 7000 0.2384 0.9258
0.1491 13.95 7100 0.2373 0.9303
0.1859 14.15 7200 0.2623 0.9253
0.2094 14.34 7300 0.2308 0.9303
0.14 14.54 7400 0.2377 0.9298
0.1836 14.73 7500 0.2389 0.9268
0.1347 14.93 7600 0.2205 0.9327
0.0747 15.13 7700 0.2375 0.9288
0.1448 15.32 7800 0.2277 0.9342
0.0885 15.52 7900 0.2560 0.9219
0.0975 15.72 8000 0.2082 0.9293
0.1185 15.91 8100 0.2561 0.9214
0.1544 16.11 8200 0.2599 0.9283
0.0959 16.31 8300 0.2418 0.9263
0.0835 16.5 8400 0.2521 0.9352
0.0846 16.7 8500 0.2258 0.9347
0.1255 16.9 8600 0.2170 0.9342
0.1116 17.09 8700 0.2462 0.9288
0.1331 17.29 8800 0.2123 0.9420
0.0895 17.49 8900 0.2513 0.9293
0.1628 17.68 9000 0.2223 0.9283
0.2152 17.88 9100 0.2144 0.9396
0.1074 18.07 9200 0.2295 0.9376
0.1888 18.27 9300 0.2557 0.9337
0.1014 18.47 9400 0.2007 0.9411
0.0341 18.66 9500 0.2289 0.9371
0.0365 18.86 9600 0.2434 0.9337
0.1099 19.06 9700 0.2222 0.9337
0.1303 19.25 9800 0.2208 0.9317
0.1209 19.45 9900 0.2151 0.9401
0.2119 19.65 10000 0.2209 0.9376
0.0734 19.84 10100 0.2242 0.9381

Framework versions