resnet50_fold_2_v3
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2156
- Accuracy: 0.9455
- F1 Score: 0.9473
- Recall: 0.9511
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 150
- num_epochs: 100
- label_smoothing_factor: 0.15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
|---|---|---|---|---|---|---|
| 2.7877 | 1.0 | 20 | 2.7748 | 0.2340 | 0.2038 | 0.2111 |
| 2.7713 | 2.0 | 40 | 2.7649 | 0.2821 | 0.1863 | 0.2317 |
| 2.7339 | 3.0 | 60 | 2.7455 | 0.3173 | 0.1730 | 0.2516 |
| 2.6964 | 4.0 | 80 | 2.7174 | 0.3429 | 0.1565 | 0.2663 |
| 2.6233 | 5.0 | 100 | 2.6713 | 0.3718 | 0.2136 | 0.2972 |
| 2.5505 | 6.0 | 120 | 2.5868 | 0.4103 | 0.2924 | 0.3438 |
| 2.4113 | 7.0 | 140 | 2.4547 | 0.5192 | 0.4524 | 0.4679 |
| 2.1943 | 8.0 | 160 | 2.2780 | 0.6635 | 0.6414 | 0.6308 |
| 1.9959 | 9.0 | 180 | 1.9887 | 0.7853 | 0.7871 | 0.7734 |
| 1.7676 | 10.0 | 200 | 1.7359 | 0.8365 | 0.8366 | 0.8360 |
| 1.5515 | 11.0 | 220 | 1.5632 | 0.8622 | 0.8634 | 0.8704 |
| 1.4094 | 12.0 | 240 | 1.4685 | 0.8814 | 0.8824 | 0.8885 |
| 1.3099 | 13.0 | 260 | 1.3924 | 0.8782 | 0.8791 | 0.8825 |
| 1.3021 | 14.0 | 280 | 1.3553 | 0.8942 | 0.8957 | 0.8999 |
| 1.2754 | 15.0 | 300 | 1.3459 | 0.8942 | 0.8965 | 0.9034 |
| 1.2809 | 16.0 | 320 | 1.3213 | 0.9006 | 0.9021 | 0.9064 |
| 1.2039 | 17.0 | 340 | 1.3051 | 0.9167 | 0.9193 | 0.9252 |
| 1.1827 | 18.0 | 360 | 1.2900 | 0.9135 | 0.9163 | 0.9182 |
| 1.1805 | 19.0 | 380 | 1.2933 | 0.9071 | 0.9092 | 0.9102 |
| 1.2095 | 20.0 | 400 | 1.2653 | 0.9071 | 0.9090 | 0.9072 |
| 1.1459 | 21.0 | 420 | 1.2834 | 0.9295 | 0.9318 | 0.9366 |
| 1.1535 | 22.0 | 440 | 1.2681 | 0.9231 | 0.9238 | 0.9268 |
| 1.1871 | 23.0 | 460 | 1.2560 | 0.9295 | 0.9310 | 0.9329 |
| 1.1502 | 24.0 | 480 | 1.2490 | 0.9391 | 0.9413 | 0.9440 |
| 1.1546 | 25.0 | 500 | 1.2599 | 0.9231 | 0.9259 | 0.9245 |
| 1.1360 | 26.0 | 520 | 1.2493 | 0.9295 | 0.9318 | 0.9356 |
| 1.0990 | 27.0 | 540 | 1.2512 | 0.9263 | 0.9284 | 0.9305 |
| 1.1106 | 28.0 | 560 | 1.2462 | 0.9327 | 0.9349 | 0.9385 |
| 1.1113 | 29.0 | 580 | 1.2399 | 0.9263 | 0.9275 | 0.9280 |
| 1.0854 | 30.0 | 600 | 1.2411 | 0.9231 | 0.9251 | 0.9295 |
| 1.0828 | 31.0 | 620 | 1.2326 | 0.9359 | 0.9383 | 0.9405 |
| 1.1117 | 32.0 | 640 | 1.2407 | 0.9327 | 0.9346 | 0.9351 |
| 1.0534 | 33.0 | 660 | 1.2415 | 0.9199 | 0.9209 | 0.9214 |
| 1.0891 | 34.0 | 680 | 1.2391 | 0.9231 | 0.9261 | 0.9263 |
| 1.1107 | 35.0 | 700 | 1.2374 | 0.9263 | 0.9264 | 0.9265 |
| 1.0882 | 36.0 | 720 | 1.2434 | 0.9295 | 0.9315 | 0.9302 |
| 1.0882 | 37.0 | 740 | 1.2353 | 0.9295 | 0.9312 | 0.9299 |
| 1.0987 | 38.0 | 760 | 1.2156 | 0.9455 | 0.9473 | 0.9511 |
| 1.0452 | 39.0 | 780 | 1.2226 | 0.9327 | 0.9348 | 0.9361 |
| 1.0637 | 40.0 | 800 | 1.2304 | 0.9327 | 0.9343 | 0.9344 |
| 1.0404 | 41.0 | 820 | 1.2234 | 0.9359 | 0.9379 | 0.9408 |
| 1.0338 | 42.0 | 840 | 1.2404 | 0.9263 | 0.9286 | 0.9283 |
| 1.0687 | 43.0 | 860 | 1.2207 | 0.9359 | 0.9372 | 0.9401 |
| 1.0685 | 44.0 | 880 | 1.2256 | 0.9327 | 0.9340 | 0.9371 |
| 1.0674 | 45.0 | 900 | 1.2268 | 0.9359 | 0.9384 | 0.9425 |
| 1.0527 | 46.0 | 920 | 1.2313 | 0.9327 | 0.9340 | 0.9366 |
| 1.0477 | 47.0 | 940 | 1.2367 | 0.9327 | 0.9339 | 0.9378 |
| 1.0481 | 48.0 | 960 | 1.2147 | 0.9391 | 0.9416 | 0.9457 |
| 1.0861 | 49.0 | 980 | 1.2289 | 0.9327 | 0.9347 | 0.9371 |
| 1.0387 | 50.0 | 1000 | 1.2225 | 0.9295 | 0.9315 | 0.9339 |
| 1.0592 | 51.0 | 1020 | 1.2252 | 0.9359 | 0.9377 | 0.9395 |
| 1.0397 | 52.0 | 1040 | 1.2287 | 0.9327 | 0.9345 | 0.9359 |
| 1.0772 | 53.0 | 1060 | 1.2241 | 0.9359 | 0.9378 | 0.9383 |
| 1.0461 | 54.0 | 1080 | 1.2280 | 0.9359 | 0.9374 | 0.9378 |
| 1.0345 | 55.0 | 1100 | 1.2358 | 0.9263 | 0.9282 | 0.9274 |
| 1.0456 | 56.0 | 1120 | 1.2268 | 0.9295 | 0.9315 | 0.9344 |
| 1.0747 | 57.0 | 1140 | 1.2241 | 0.9327 | 0.9345 | 0.9356 |
| 1.0194 | 58.0 | 1160 | 1.2096 | 0.9327 | 0.9344 | 0.9361 |
| 1.0270 | 59.0 | 1180 | 1.2059 | 0.9423 | 0.9437 | 0.9457 |
| 1.0273 | 60.0 | 1200 | 1.2210 | 0.9423 | 0.9441 | 0.9457 |
| 1.0271 | 61.0 | 1220 | 1.2130 | 0.9391 | 0.9408 | 0.9432 |
| 1.0133 | 62.0 | 1240 | 1.2107 | 0.9327 | 0.9350 | 0.9383 |
| 1.0046 | 63.0 | 1260 | 1.2163 | 0.9359 | 0.9381 | 0.9420 |
| 1.0196 | 64.0 | 1280 | 1.2262 | 0.9359 | 0.9381 | 0.9420 |
| 1.0338 | 65.0 | 1300 | 1.2231 | 0.9359 | 0.9377 | 0.9395 |
| 1.0166 | 66.0 | 1320 | 1.2208 | 0.9327 | 0.9347 | 0.9371 |
| 1.0027 | 67.0 | 1340 | 1.2165 | 0.9295 | 0.9314 | 0.9346 |
| 1.0150 | 68.0 | 1360 | 1.2331 | 0.9391 | 0.9416 | 0.9432 |
| 1.0003 | 69.0 | 1380 | 1.2159 | 0.9327 | 0.9358 | 0.9383 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2