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---
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: ms_detr_finetuned_diana
results: []
---
<!-- 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. -->
# ms_detr_finetuned_diana
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3578
- Map: 0.7134
- Map 50: 0.8181
- Map 75: 0.8181
- Map Small: -1.0
- Map Medium: 0.7864
- Map Large: 0.7101
- Mar 1: 0.1236
- Mar 10: 0.7964
- Mar 100: 0.825
- Mar Small: -1.0
- Mar Medium: 0.8
- Mar Large: 0.8302
- Map Per Class: -1.0
- Mar 100 Per Class: -1.0
- Classes: 0
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 50
### 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 Per Class | Mar 100 Per Class | Classes |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-------------:|:-----------------:|:-------:|
| 2.1209 | 1.0 | 10 | 2.1209 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.4805 | 2.0 | 20 | 1.5062 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.205 | 3.0 | 30 | 1.3151 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.2767 | 4.0 | 40 | 1.1969 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 1.095 | 5.0 | 50 | 1.0561 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.8968 | 6.0 | 60 | 0.9283 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.8153 | 7.0 | 70 | 0.8459 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.7445 | 8.0 | 80 | 0.7019 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.5769 | 9.0 | 90 | 0.6067 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.5487 | 10.0 | 100 | 0.5308 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0 |
| 0.4492 | 11.0 | 110 | 0.5069 | 0.0089 | 0.0099 | 0.0099 | -1.0 | 0.0 | 0.0089 | 0.0064 | 0.0064 | 0.0064 | -1.0 | 0.0 | 0.0078 | -1.0 | -1.0 | 0 |
| 0.3912 | 12.0 | 120 | 0.5000 | 0.0385 | 0.0438 | 0.0438 | -1.0 | 0.0 | 0.0461 | 0.0314 | 0.0386 | 0.0386 | -1.0 | 0.0 | 0.0466 | -1.0 | -1.0 | 0 |
| 0.3875 | 13.0 | 130 | 0.4488 | 0.0901 | 0.1036 | 0.1036 | -1.0 | 0.0 | 0.1072 | 0.0571 | 0.0886 | 0.0886 | -1.0 | 0.0 | 0.1069 | -1.0 | -1.0 | 0 |
| 0.4356 | 14.0 | 140 | 0.4592 | 0.3255 | 0.3789 | 0.3789 | -1.0 | 0.1382 | 0.3656 | 0.1007 | 0.3657 | 0.3657 | -1.0 | 0.1417 | 0.4121 | -1.0 | -1.0 | 0 |
| 0.3536 | 15.0 | 150 | 0.4293 | 0.3127 | 0.3673 | 0.3584 | -1.0 | 0.1914 | 0.3399 | 0.1064 | 0.3629 | 0.3629 | -1.0 | 0.1958 | 0.3974 | -1.0 | -1.0 | 0 |
| 0.3617 | 16.0 | 160 | 0.4128 | 0.4625 | 0.5397 | 0.5288 | -1.0 | 0.3614 | 0.4897 | 0.1164 | 0.5329 | 0.5329 | -1.0 | 0.3625 | 0.5681 | -1.0 | -1.0 | 0 |
| 0.392 | 17.0 | 170 | 0.4258 | 0.4683 | 0.5332 | 0.5332 | -1.0 | 0.3911 | 0.4868 | 0.1179 | 0.5486 | 0.5486 | -1.0 | 0.4 | 0.5793 | -1.0 | -1.0 | 0 |
| 0.3694 | 18.0 | 180 | 0.4563 | 0.4006 | 0.4614 | 0.4614 | -1.0 | 0.3313 | 0.4267 | 0.1157 | 0.4714 | 0.4714 | -1.0 | 0.3333 | 0.5 | -1.0 | -1.0 | 0 |
| 0.3569 | 19.0 | 190 | 0.4160 | 0.4912 | 0.5672 | 0.5672 | -1.0 | 0.386 | 0.5185 | 0.1157 | 0.58 | 0.58 | -1.0 | 0.4 | 0.6172 | -1.0 | -1.0 | 0 |
| 0.3839 | 20.0 | 200 | 0.4665 | 0.5324 | 0.6311 | 0.6212 | -1.0 | 0.4719 | 0.5561 | 0.115 | 0.6114 | 0.6114 | -1.0 | 0.475 | 0.6397 | -1.0 | -1.0 | 0 |
| 0.3123 | 21.0 | 210 | 0.4144 | 0.4808 | 0.5519 | 0.5519 | -1.0 | 0.3279 | 0.5235 | 0.1164 | 0.5643 | 0.5643 | -1.0 | 0.3333 | 0.6121 | -1.0 | -1.0 | 0 |
| 0.2824 | 22.0 | 220 | 0.3918 | 0.5587 | 0.6403 | 0.6403 | -1.0 | 0.468 | 0.5874 | 0.1186 | 0.6557 | 0.6557 | -1.0 | 0.4792 | 0.6922 | -1.0 | -1.0 | 0 |
| 0.2545 | 23.0 | 230 | 0.3530 | 0.5577 | 0.6299 | 0.6299 | -1.0 | 0.448 | 0.5846 | 0.1179 | 0.645 | 0.6514 | -1.0 | 0.4542 | 0.6922 | -1.0 | -1.0 | 0 |
| 0.2716 | 24.0 | 240 | 0.3540 | 0.6501 | 0.7455 | 0.7369 | -1.0 | 0.6292 | 0.6653 | 0.1236 | 0.7486 | 0.77 | -1.0 | 0.6375 | 0.7974 | -1.0 | -1.0 | 0 |
| 0.2631 | 25.0 | 250 | 0.3608 | 0.5918 | 0.6733 | 0.6733 | -1.0 | 0.5879 | 0.6012 | 0.1193 | 0.6936 | 0.6936 | -1.0 | 0.6 | 0.7129 | -1.0 | -1.0 | 0 |
| 0.2628 | 26.0 | 260 | 0.3607 | 0.6089 | 0.6904 | 0.6904 | -1.0 | 0.6516 | 0.608 | 0.1193 | 0.6943 | 0.7114 | -1.0 | 0.6583 | 0.7224 | -1.0 | -1.0 | 0 |
| 0.2653 | 27.0 | 270 | 0.3692 | 0.6648 | 0.7623 | 0.7538 | -1.0 | 0.7795 | 0.6512 | 0.1171 | 0.75 | 0.7771 | -1.0 | 0.8042 | 0.7716 | -1.0 | -1.0 | 0 |
| 0.2272 | 28.0 | 280 | 0.3657 | 0.5998 | 0.6814 | 0.6814 | -1.0 | 0.614 | 0.602 | 0.12 | 0.695 | 0.7007 | -1.0 | 0.6292 | 0.7155 | -1.0 | -1.0 | 0 |
| 0.3795 | 29.0 | 290 | 0.3728 | 0.6409 | 0.7284 | 0.7277 | -1.0 | 0.6901 | 0.6407 | 0.1264 | 0.7364 | 0.7486 | -1.0 | 0.7042 | 0.7578 | -1.0 | -1.0 | 0 |
| 0.2568 | 30.0 | 300 | 0.3724 | 0.6933 | 0.7956 | 0.7854 | -1.0 | 0.7381 | 0.6926 | 0.1236 | 0.7821 | 0.8043 | -1.0 | 0.7542 | 0.8147 | -1.0 | -1.0 | 0 |
| 0.2632 | 31.0 | 310 | 0.3741 | 0.6626 | 0.7614 | 0.7522 | -1.0 | 0.7747 | 0.651 | 0.1243 | 0.7479 | 0.7671 | -1.0 | 0.7958 | 0.7612 | -1.0 | -1.0 | 0 |
| 0.3576 | 32.0 | 320 | 0.3649 | 0.6734 | 0.7746 | 0.7746 | -1.0 | 0.7503 | 0.6686 | 0.1236 | 0.7586 | 0.7757 | -1.0 | 0.7667 | 0.7776 | -1.0 | -1.0 | 0 |
| 0.2254 | 33.0 | 330 | 0.3683 | 0.6991 | 0.8085 | 0.8084 | -1.0 | 0.7949 | 0.691 | 0.1243 | 0.7929 | 0.8121 | -1.0 | 0.8125 | 0.8121 | -1.0 | -1.0 | 0 |
| 0.2495 | 34.0 | 340 | 0.3459 | 0.6975 | 0.811 | 0.8021 | -1.0 | 0.7652 | 0.6919 | 0.1257 | 0.7793 | 0.805 | -1.0 | 0.7833 | 0.8095 | -1.0 | -1.0 | 0 |
| 0.2051 | 35.0 | 350 | 0.3508 | 0.6903 | 0.7939 | 0.7845 | -1.0 | 0.797 | 0.6835 | 0.1243 | 0.7693 | 0.7943 | -1.0 | 0.8167 | 0.7897 | -1.0 | -1.0 | 0 |
| 0.2159 | 36.0 | 360 | 0.3510 | 0.693 | 0.7971 | 0.7971 | -1.0 | 0.7619 | 0.6898 | 0.1214 | 0.7807 | 0.7986 | -1.0 | 0.7792 | 0.8026 | -1.0 | -1.0 | 0 |
| 0.2234 | 37.0 | 370 | 0.3512 | 0.7033 | 0.8062 | 0.8062 | -1.0 | 0.7588 | 0.7014 | 0.1236 | 0.78 | 0.8036 | -1.0 | 0.7708 | 0.8103 | -1.0 | -1.0 | 0 |
| 0.2732 | 38.0 | 380 | 0.3603 | 0.6916 | 0.7917 | 0.7917 | -1.0 | 0.6964 | 0.7019 | 0.1236 | 0.7857 | 0.7993 | -1.0 | 0.7083 | 0.8181 | -1.0 | -1.0 | 0 |
| 0.2397 | 39.0 | 390 | 0.3633 | 0.7141 | 0.8125 | 0.804 | -1.0 | 0.7074 | 0.7255 | 0.1264 | 0.7971 | 0.8186 | -1.0 | 0.7167 | 0.8397 | -1.0 | -1.0 | 0 |
| 0.2534 | 40.0 | 400 | 0.3574 | 0.7115 | 0.8104 | 0.8104 | -1.0 | 0.705 | 0.722 | 0.1236 | 0.7979 | 0.8179 | -1.0 | 0.7125 | 0.8397 | -1.0 | -1.0 | 0 |
| 0.2168 | 41.0 | 410 | 0.3547 | 0.7106 | 0.8087 | 0.8087 | -1.0 | 0.7594 | 0.7141 | 0.1257 | 0.8007 | 0.8229 | -1.0 | 0.7708 | 0.8336 | -1.0 | -1.0 | 0 |
| 0.2237 | 42.0 | 420 | 0.3590 | 0.7055 | 0.8105 | 0.8105 | -1.0 | 0.759 | 0.7089 | 0.1243 | 0.7964 | 0.8186 | -1.0 | 0.7708 | 0.8284 | -1.0 | -1.0 | 0 |
| 0.2152 | 43.0 | 430 | 0.3582 | 0.7132 | 0.82 | 0.82 | -1.0 | 0.7865 | 0.7109 | 0.1243 | 0.7971 | 0.8243 | -1.0 | 0.8 | 0.8293 | -1.0 | -1.0 | 0 |
| 0.1932 | 44.0 | 440 | 0.3612 | 0.7056 | 0.8112 | 0.8112 | -1.0 | 0.7825 | 0.7023 | 0.1229 | 0.7857 | 0.815 | -1.0 | 0.8 | 0.8181 | -1.0 | -1.0 | 0 |
| 0.1897 | 45.0 | 450 | 0.3557 | 0.7077 | 0.8105 | 0.8105 | -1.0 | 0.755 | 0.7111 | 0.1229 | 0.7957 | 0.8186 | -1.0 | 0.7667 | 0.8293 | -1.0 | -1.0 | 0 |
| 0.213 | 46.0 | 460 | 0.3557 | 0.7136 | 0.8193 | 0.8193 | -1.0 | 0.786 | 0.7101 | 0.1236 | 0.7943 | 0.8229 | -1.0 | 0.8 | 0.8276 | -1.0 | -1.0 | 0 |
| 0.2169 | 47.0 | 470 | 0.3567 | 0.7142 | 0.8192 | 0.8192 | -1.0 | 0.7864 | 0.711 | 0.1243 | 0.7957 | 0.8243 | -1.0 | 0.8 | 0.8293 | -1.0 | -1.0 | 0 |
| 0.1971 | 48.0 | 480 | 0.3577 | 0.713 | 0.8181 | 0.8181 | -1.0 | 0.7864 | 0.7097 | 0.1236 | 0.7957 | 0.8243 | -1.0 | 0.8 | 0.8293 | -1.0 | -1.0 | 0 |
| 0.2515 | 49.0 | 490 | 0.3580 | 0.7134 | 0.8181 | 0.8181 | -1.0 | 0.7864 | 0.71 | 0.1236 | 0.7964 | 0.825 | -1.0 | 0.8 | 0.8302 | -1.0 | -1.0 | 0 |
| 0.2874 | 50.0 | 500 | 0.3578 | 0.7134 | 0.8181 | 0.8181 | -1.0 | 0.7864 | 0.7101 | 0.1236 | 0.7964 | 0.825 | -1.0 | 0.8 | 0.8302 | -1.0 | -1.0 | 0 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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