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rtdetr_v2_r101vd-rocks-finetune

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rt_detr_v2
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metadata
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_v2_r101vd
tags:
  - generated_from_trainer
model-index:
  - name: rtdetr_v2_r101vd-rocks-finetune
    results: []

rtdetr_v2_r101vd-rocks-finetune

This model is a fine-tuned version of PekingU/rtdetr_v2_r101vd on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 12.1315
  • Map: 0.6911
  • Map 50: 0.9158
  • Map 75: 0.9158
  • Map Small: 0.6597
  • Map Medium: 0.7
  • Map Large: -1.0
  • Mar 1: 0.5
  • Mar 10: 0.8333
  • Mar 100: 0.8333
  • Mar Small: 0.9
  • Mar Medium: 0.7
  • Mar Large: -1.0
  • Map Stone: 0.6911
  • Mar 100 Stone: 0.8333

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 300
  • num_epochs: 45

Training results

Training Loss Epoch Step Validation Loss Map Map 50 Map 75 Map Small Map Medium Map Large Mar 1 Mar 10 Mar 100 Mar Small Mar Medium Mar Large Map Stone Mar 100 Stone
No log 1.0 130 43.3717 0.0276 0.0423 0.0301 0.0336 0.0313 0.0333 0.0416 0.4188 0.565 0.5609 0.5564 0.5793 0.0276 0.565
No log 2.0 260 19.5303 0.199 0.2747 0.2335 0.5123 0.228 0.0269 0.1921 0.535 0.6119 0.6936 0.595 0.5326 0.199 0.6119
No log 3.0 390 14.1844 0.1391 0.2029 0.1548 0.1355 0.1813 0.1363 0.2475 0.5729 0.6287 0.6518 0.6228 0.6076 0.1391 0.6287
76.178 4.0 520 14.1197 0.2443 0.3365 0.2704 0.3443 0.2898 0.1733 0.235 0.5792 0.6264 0.6636 0.6208 0.588 0.2443 0.6264
76.178 5.0 650 14.1867 0.1413 0.1963 0.1623 0.1773 0.1702 0.1508 0.1389 0.5531 0.6498 0.6909 0.6495 0.6011 0.1413 0.6498
76.178 6.0 780 14.0705 0.217 0.3 0.2459 0.3437 0.2312 0.1259 0.1934 0.5548 0.6488 0.6973 0.6386 0.6022 0.217 0.6488
76.178 7.0 910 14.4345 0.1072 0.164 0.1146 0.1183 0.1408 0.1143 0.1917 0.5472 0.665 0.7373 0.6584 0.5859 0.1072 0.665
13.1861 8.0 1040 14.1636 0.093 0.1354 0.0994 0.1355 0.1011 0.1415 0.1795 0.5224 0.6439 0.6973 0.6386 0.5859 0.093 0.6439
13.1861 9.0 1170 14.2129 0.2464 0.3366 0.2799 0.4291 0.2459 0.0826 0.1931 0.5677 0.6601 0.7182 0.6347 0.6185 0.2464 0.6601
13.1861 10.0 1300 14.2845 0.3264 0.4559 0.3618 0.518 0.3047 0.094 0.2492 0.6033 0.6564 0.7045 0.6436 0.613 0.3264 0.6564
13.1861 11.0 1430 13.8762 0.2687 0.3688 0.3145 0.433 0.2836 0.1072 0.2251 0.5627 0.6683 0.72 0.6614 0.6141 0.2687 0.6683
11.7639 12.0 1560 13.9386 0.2306 0.3174 0.273 0.3377 0.3147 0.13 0.232 0.5531 0.6502 0.6982 0.6396 0.6043 0.2306 0.6502
11.7639 13.0 1690 14.2454 0.1776 0.2663 0.1957 0.3221 0.1609 0.0457 0.1809 0.5211 0.6512 0.6809 0.6554 0.6109 0.1776 0.6512
11.7639 14.0 1820 14.3947 0.2122 0.2965 0.2381 0.3411 0.2566 0.0955 0.2267 0.5571 0.6531 0.7091 0.6267 0.6152 0.2122 0.6531
11.7639 15.0 1950 14.4063 0.2711 0.3709 0.3106 0.4107 0.307 0.1042 0.232 0.5723 0.6482 0.7027 0.6248 0.6087 0.2711 0.6482
11.0387 16.0 2080 14.7560 0.2464 0.3446 0.2848 0.4027 0.2642 0.1113 0.2073 0.5591 0.6452 0.6955 0.6396 0.5913 0.2464 0.6452
11.0387 17.0 2210 14.3657 0.2377 0.3213 0.2779 0.4069 0.2185 0.0691 0.1683 0.562 0.6776 0.7255 0.6693 0.6293 0.2377 0.6776
11.0387 18.0 2340 14.7338 0.1282 0.1787 0.1436 0.2257 0.0918 0.0771 0.1571 0.5036 0.6584 0.7091 0.6465 0.6109 0.1282 0.6584
11.0387 19.0 2470 13.8830 0.2278 0.3285 0.2549 0.3816 0.2429 0.0808 0.2069 0.5726 0.665 0.7136 0.6436 0.6304 0.2278 0.665
10.3071 20.0 2600 13.9696 0.2359 0.3283 0.2678 0.3442 0.2589 0.2031 0.2452 0.5931 0.6726 0.7382 0.6475 0.6217 0.2359 0.6726
10.3071 21.0 2730 14.1270 0.2196 0.3031 0.2525 0.3577 0.2166 0.1295 0.2079 0.532 0.6591 0.7309 0.6267 0.6087 0.2196 0.6591
10.3071 22.0 2860 14.3896 0.2209 0.3264 0.2574 0.396 0.1652 0.0887 0.1769 0.5182 0.6459 0.7055 0.6347 0.587 0.2209 0.6459
10.3071 23.0 2990 14.4689 0.2234 0.3249 0.2436 0.3388 0.2667 0.071 0.2033 0.5426 0.6624 0.7036 0.6446 0.6326 0.2234 0.6624
9.6574 24.0 3120 14.6690 0.2165 0.2991 0.2533 0.3543 0.225 0.0944 0.2182 0.5482 0.6637 0.7245 0.6455 0.6109 0.2165 0.6637
9.6574 25.0 3250 14.7214 0.2511 0.3417 0.2827 0.4054 0.2958 0.0878 0.2066 0.5446 0.6469 0.7082 0.599 0.6261 0.2511 0.6469
9.6574 26.0 3380 14.4813 0.2302 0.3265 0.2656 0.3893 0.2184 0.1056 0.1891 0.5162 0.6528 0.7155 0.6158 0.6185 0.2302 0.6528
9.0433 27.0 3510 14.5406 0.2407 0.3245 0.2803 0.3773 0.268 0.1287 0.2076 0.5347 0.6482 0.7145 0.6277 0.5913 0.2407 0.6482
9.0433 28.0 3640 14.4553 0.2026 0.2886 0.2381 0.2977 0.2368 0.0888 0.2139 0.5396 0.6502 0.7 0.6406 0.6011 0.2026 0.6502
9.0433 29.0 3770 14.7926 0.2045 0.2902 0.2295 0.322 0.2166 0.0971 0.2109 0.5172 0.6386 0.7 0.6109 0.5957 0.2045 0.6386
9.0433 30.0 3900 14.9966 0.2289 0.3217 0.2619 0.3852 0.243 0.0661 0.2168 0.5083 0.6548 0.7127 0.6515 0.5891 0.2289 0.6548
8.5206 31.0 4030 14.8464 0.2238 0.3236 0.2494 0.3919 0.1991 0.0635 0.1908 0.5132 0.6419 0.7109 0.6267 0.5761 0.2238 0.6419
8.5206 32.0 4160 14.9270 0.2427 0.3487 0.2798 0.395 0.2652 0.0847 0.2162 0.5182 0.632 0.7109 0.602 0.5707 0.2427 0.632
8.5206 33.0 4290 14.6849 0.2358 0.3282 0.2763 0.3972 0.2281 0.0893 0.2046 0.5317 0.6521 0.7282 0.6228 0.5935 0.2358 0.6521
8.5206 34.0 4420 14.7376 0.2675 0.3831 0.3025 0.4577 0.238 0.087 0.2251 0.5498 0.6495 0.7173 0.6168 0.6043 0.2675 0.6495
7.9929 35.0 4550 14.7160 0.2474 0.3483 0.28 0.4288 0.2226 0.0857 0.1983 0.534 0.6455 0.7218 0.6178 0.5848 0.2474 0.6455
7.9929 36.0 4680 14.2937 0.2556 0.3545 0.2968 0.424 0.2496 0.1339 0.2139 0.5469 0.6515 0.72 0.6426 0.5793 0.2556 0.6515
7.9929 37.0 4810 14.6087 0.2737 0.3809 0.3078 0.4279 0.268 0.1645 0.2459 0.5558 0.6508 0.7109 0.6208 0.612 0.2737 0.6508
7.9929 38.0 4940 14.7062 0.239 0.3428 0.2685 0.4064 0.2152 0.0871 0.196 0.5241 0.6337 0.7136 0.6109 0.563 0.239 0.6337
7.5722 39.0 5070 14.8826 0.252 0.3564 0.2801 0.4203 0.2244 0.1091 0.2145 0.5409 0.6399 0.7145 0.6059 0.588 0.252 0.6399
7.5722 40.0 5200 14.7745 0.2677 0.3671 0.3017 0.44 0.2654 0.1579 0.2251 0.5505 0.6505 0.7282 0.6218 0.5891 0.2677 0.6505
7.5722 41.0 5330 14.9496 0.2455 0.3407 0.2728 0.4184 0.2176 0.1036 0.2046 0.5343 0.638 0.7245 0.5911 0.5859 0.2455 0.638
7.5722 42.0 5460 15.1104 0.2375 0.3346 0.259 0.4124 0.2009 0.097 0.1835 0.5191 0.6376 0.7182 0.6158 0.5652 0.2375 0.6376
7.1411 43.0 5590 15.1514 0.2455 0.3469 0.2643 0.4123 0.2304 0.0974 0.2026 0.5257 0.638 0.7118 0.6079 0.5826 0.2455 0.638
7.1411 44.0 5720 15.0940 0.2383 0.3423 0.2613 0.3993 0.2116 0.0984 0.199 0.5099 0.6208 0.7018 0.5822 0.5663 0.2383 0.6208
7.1411 45.0 5850 15.0988 0.2459 0.3522 0.2692 0.4006 0.2335 0.1053 0.2165 0.5191 0.6241 0.7036 0.5842 0.5728 0.2459 0.6241

Framework versions

  • Transformers 4.52.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1