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--- |
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license: apache-2.0 |
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base_model: microsoft/conditional-detr-resnet-50 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: detr_finetuned_trashify_box_detector |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# detr_finetuned_trashify_box_detector |
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This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2103 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 59.9289 | 1.0 | 9 | 59.2281 | |
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| 54.8011 | 2.0 | 18 | 43.7291 | |
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| 34.6414 | 3.0 | 27 | 22.8508 | |
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| 15.8995 | 4.0 | 36 | 8.1439 | |
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| 6.2262 | 5.0 | 45 | 3.7740 | |
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| 3.6279 | 6.0 | 54 | 2.9067 | |
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| 3.0063 | 7.0 | 63 | 2.9085 | |
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| 2.9371 | 8.0 | 72 | 2.5180 | |
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| 2.6431 | 9.0 | 81 | 2.3684 | |
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| 2.6074 | 10.0 | 90 | 2.2390 | |
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| 2.4797 | 11.0 | 99 | 2.0779 | |
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| 2.4703 | 12.0 | 108 | 2.1778 | |
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| 2.484 | 13.0 | 117 | 2.1163 | |
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| 2.3445 | 14.0 | 126 | 2.0291 | |
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| 2.3304 | 15.0 | 135 | 1.9298 | |
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| 2.3748 | 16.0 | 144 | 2.0053 | |
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| 2.2942 | 17.0 | 153 | 1.9890 | |
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| 2.2322 | 18.0 | 162 | 1.9092 | |
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| 2.2244 | 19.0 | 171 | 1.8906 | |
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| 2.1804 | 20.0 | 180 | 1.8215 | |
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| 2.1202 | 21.0 | 189 | 1.8159 | |
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| 2.1076 | 22.0 | 198 | 1.7617 | |
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| 2.0875 | 23.0 | 207 | 1.7749 | |
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| 2.0771 | 24.0 | 216 | 1.7343 | |
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| 2.0842 | 25.0 | 225 | 1.7500 | |
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| 1.9582 | 26.0 | 234 | 1.7164 | |
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| 1.9865 | 27.0 | 243 | 1.6550 | |
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| 1.9712 | 28.0 | 252 | 1.6415 | |
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| 1.9536 | 29.0 | 261 | 1.5604 | |
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| 1.8901 | 30.0 | 270 | 1.5556 | |
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| 1.8435 | 31.0 | 279 | 1.5524 | |
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| 1.8589 | 32.0 | 288 | 1.5356 | |
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| 1.848 | 33.0 | 297 | 1.4709 | |
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| 1.8287 | 34.0 | 306 | 1.5041 | |
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| 1.7871 | 35.0 | 315 | 1.4433 | |
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| 1.7822 | 36.0 | 324 | 1.4514 | |
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| 1.7421 | 37.0 | 333 | 1.3894 | |
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| 1.701 | 38.0 | 342 | 1.4114 | |
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| 1.6385 | 39.0 | 351 | 1.3454 | |
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| 1.6397 | 40.0 | 360 | 1.3693 | |
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| 1.6628 | 41.0 | 369 | 1.4018 | |
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| 1.6068 | 42.0 | 378 | 1.3678 | |
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| 1.6293 | 43.0 | 387 | 1.4055 | |
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| 1.655 | 44.0 | 396 | 1.3835 | |
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| 1.5538 | 45.0 | 405 | 1.3675 | |
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| 1.5304 | 46.0 | 414 | 1.3579 | |
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| 1.564 | 47.0 | 423 | 1.3538 | |
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| 1.5554 | 48.0 | 432 | 1.3381 | |
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| 1.5507 | 49.0 | 441 | 1.2798 | |
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| 1.5419 | 50.0 | 450 | 1.2863 | |
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| 1.5634 | 51.0 | 459 | 1.2955 | |
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| 1.5321 | 52.0 | 468 | 1.2939 | |
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| 1.5152 | 53.0 | 477 | 1.2924 | |
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| 1.5063 | 54.0 | 486 | 1.2978 | |
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| 1.4461 | 55.0 | 495 | 1.2925 | |
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| 1.3762 | 56.0 | 504 | 1.2707 | |
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| 1.4025 | 57.0 | 513 | 1.2640 | |
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| 1.4257 | 58.0 | 522 | 1.2618 | |
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| 1.4425 | 59.0 | 531 | 1.2675 | |
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| 1.3617 | 60.0 | 540 | 1.2676 | |
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| 1.4396 | 61.0 | 549 | 1.2623 | |
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| 1.3183 | 62.0 | 558 | 1.2347 | |
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| 1.3449 | 63.0 | 567 | 1.2279 | |
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| 1.3784 | 64.0 | 576 | 1.2135 | |
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| 1.3639 | 65.0 | 585 | 1.2468 | |
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| 1.3633 | 66.0 | 594 | 1.2355 | |
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| 1.2905 | 67.0 | 603 | 1.2114 | |
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| 1.3364 | 68.0 | 612 | 1.2301 | |
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| 1.2687 | 69.0 | 621 | 1.2220 | |
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| 1.2859 | 70.0 | 630 | 1.2227 | |
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| 1.2576 | 71.0 | 639 | 1.2194 | |
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| 1.3423 | 72.0 | 648 | 1.2285 | |
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| 1.2954 | 73.0 | 657 | 1.2470 | |
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| 1.3408 | 74.0 | 666 | 1.2514 | |
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| 1.2383 | 75.0 | 675 | 1.2120 | |
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| 1.2192 | 76.0 | 684 | 1.2318 | |
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| 1.221 | 77.0 | 693 | 1.2252 | |
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| 1.2352 | 78.0 | 702 | 1.2074 | |
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| 1.2214 | 79.0 | 711 | 1.2175 | |
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| 1.1694 | 80.0 | 720 | 1.2202 | |
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| 1.2178 | 81.0 | 729 | 1.2277 | |
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| 1.1687 | 82.0 | 738 | 1.2280 | |
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| 1.1424 | 83.0 | 747 | 1.2266 | |
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| 1.1668 | 84.0 | 756 | 1.2227 | |
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| 1.1986 | 85.0 | 765 | 1.2313 | |
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| 1.1763 | 86.0 | 774 | 1.2127 | |
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| 1.1623 | 87.0 | 783 | 1.2150 | |
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| 1.1303 | 88.0 | 792 | 1.2078 | |
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| 1.1504 | 89.0 | 801 | 1.2033 | |
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| 1.1061 | 90.0 | 810 | 1.2065 | |
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| 1.1025 | 91.0 | 819 | 1.2076 | |
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| 1.0971 | 92.0 | 828 | 1.2012 | |
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| 1.137 | 93.0 | 837 | 1.2228 | |
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| 1.1341 | 94.0 | 846 | 1.2145 | |
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| 1.0625 | 95.0 | 855 | 1.2141 | |
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| 1.1355 | 96.0 | 864 | 1.2167 | |
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| 1.1636 | 97.0 | 873 | 1.2121 | |
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| 1.1306 | 98.0 | 882 | 1.2106 | |
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| 1.1104 | 99.0 | 891 | 1.2111 | |
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| 1.1377 | 100.0 | 900 | 1.2103 | |
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### Framework versions |
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- Transformers 4.44.0 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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