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---
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
- generated_from_trainer
datasets:
- dsi
model-index:
- name: detr_finetunned_ocular
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. -->
# detr_finetunned_ocular
This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co./microsoft/conditional-detr-resnet-50) on the dsi dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0529
- Map: 0.3106
- Map 50: 0.5077
- Map 75: 0.3672
- Map Small: 0.3046
- Map Medium: 0.6881
- Map Large: -1.0
- Mar 1: 0.0994
- Mar 10: 0.3691
- Mar 100: 0.4215
- Mar Small: 0.4155
- Mar Medium: 0.7477
- Mar Large: -1.0
- Map Falciparum Trophozoite: 0.0214
- Mar 100 Falciparum Trophozoite: 0.1656
- Map Wbc: 0.5997
- Mar 100 Wbc: 0.6773
## 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: 30
### 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 Falciparum Trophozoite | Mar 100 Falciparum Trophozoite | Map Wbc | Mar 100 Wbc |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------------------------:|:------------------------------:|:-------:|:-----------:|
| No log | 1.0 | 86 | 1.6663 | 0.1273 | 0.2547 | 0.1068 | 0.12 | 0.3504 | -1.0 | 0.0547 | 0.2421 | 0.3256 | 0.3213 | 0.6374 | -1.0 | 0.0008 | 0.065 | 0.2537 | 0.5862 |
| No log | 2.0 | 172 | 1.5286 | 0.1796 | 0.3765 | 0.1218 | 0.1738 | 0.4192 | -1.0 | 0.0651 | 0.247 | 0.3015 | 0.3051 | 0.4738 | -1.0 | 0.0012 | 0.0865 | 0.3581 | 0.5165 |
| No log | 3.0 | 258 | 1.3820 | 0.2265 | 0.4343 | 0.2063 | 0.2188 | 0.5554 | -1.0 | 0.0753 | 0.2934 | 0.3347 | 0.3267 | 0.6813 | -1.0 | 0.0016 | 0.0818 | 0.4513 | 0.5876 |
| No log | 4.0 | 344 | 1.3362 | 0.2148 | 0.4433 | 0.1633 | 0.206 | 0.5348 | -1.0 | 0.0719 | 0.2764 | 0.3262 | 0.3193 | 0.6262 | -1.0 | 0.004 | 0.1074 | 0.4256 | 0.5451 |
| No log | 5.0 | 430 | 1.2962 | 0.2558 | 0.4542 | 0.2703 | 0.2493 | 0.5953 | -1.0 | 0.0826 | 0.3145 | 0.3488 | 0.3417 | 0.6897 | -1.0 | 0.0029 | 0.092 | 0.5087 | 0.6055 |
| 1.6845 | 6.0 | 516 | 1.2470 | 0.2723 | 0.4597 | 0.3089 | 0.2662 | 0.6388 | -1.0 | 0.0882 | 0.3285 | 0.3762 | 0.3691 | 0.7234 | -1.0 | 0.0053 | 0.1119 | 0.5393 | 0.6405 |
| 1.6845 | 7.0 | 602 | 1.1917 | 0.2733 | 0.4681 | 0.3094 | 0.2677 | 0.6133 | -1.0 | 0.0882 | 0.3294 | 0.3767 | 0.3709 | 0.7019 | -1.0 | 0.0093 | 0.1204 | 0.5373 | 0.633 |
| 1.6845 | 8.0 | 688 | 1.2070 | 0.271 | 0.4732 | 0.2973 | 0.2641 | 0.6376 | -1.0 | 0.0913 | 0.3372 | 0.3799 | 0.3728 | 0.7112 | -1.0 | 0.0069 | 0.1333 | 0.5352 | 0.6265 |
| 1.6845 | 9.0 | 774 | 1.1872 | 0.2638 | 0.4778 | 0.2601 | 0.2566 | 0.649 | -1.0 | 0.0865 | 0.3286 | 0.3765 | 0.371 | 0.6907 | -1.0 | 0.0076 | 0.1272 | 0.52 | 0.6258 |
| 1.6845 | 10.0 | 860 | 1.1518 | 0.2767 | 0.4774 | 0.3047 | 0.2705 | 0.6371 | -1.0 | 0.0896 | 0.3389 | 0.3838 | 0.3791 | 0.6953 | -1.0 | 0.0067 | 0.1272 | 0.5467 | 0.6403 |
| 1.6845 | 11.0 | 946 | 1.1263 | 0.2846 | 0.4783 | 0.3251 | 0.2783 | 0.658 | -1.0 | 0.0915 | 0.3472 | 0.3884 | 0.3835 | 0.7131 | -1.0 | 0.006 | 0.1217 | 0.5632 | 0.6551 |
| 1.2411 | 12.0 | 1032 | 1.1376 | 0.2879 | 0.4859 | 0.323 | 0.2805 | 0.6685 | -1.0 | 0.0961 | 0.3494 | 0.3925 | 0.3858 | 0.7374 | -1.0 | 0.01 | 0.1266 | 0.5658 | 0.6583 |
| 1.2411 | 13.0 | 1118 | 1.1416 | 0.2834 | 0.4863 | 0.3144 | 0.2777 | 0.6417 | -1.0 | 0.0952 | 0.3447 | 0.3903 | 0.3865 | 0.6888 | -1.0 | 0.0113 | 0.137 | 0.5555 | 0.6436 |
| 1.2411 | 14.0 | 1204 | 1.1278 | 0.276 | 0.4831 | 0.3022 | 0.2711 | 0.6209 | -1.0 | 0.0905 | 0.3392 | 0.3877 | 0.3829 | 0.6879 | -1.0 | 0.0117 | 0.1436 | 0.5403 | 0.6318 |
| 1.2411 | 15.0 | 1290 | 1.1409 | 0.2707 | 0.495 | 0.2817 | 0.264 | 0.6219 | -1.0 | 0.0891 | 0.3353 | 0.3851 | 0.379 | 0.6991 | -1.0 | 0.0137 | 0.1442 | 0.5277 | 0.6261 |
| 1.2411 | 16.0 | 1376 | 1.0948 | 0.2904 | 0.4937 | 0.329 | 0.2846 | 0.6672 | -1.0 | 0.0965 | 0.3524 | 0.4003 | 0.3935 | 0.7346 | -1.0 | 0.0145 | 0.1468 | 0.5662 | 0.6539 |
| 1.2411 | 17.0 | 1462 | 1.1197 | 0.2851 | 0.4952 | 0.3222 | 0.2799 | 0.6439 | -1.0 | 0.0928 | 0.346 | 0.4017 | 0.3969 | 0.7047 | -1.0 | 0.0156 | 0.1554 | 0.5546 | 0.6479 |
| 1.1067 | 18.0 | 1548 | 1.0893 | 0.2967 | 0.4986 | 0.3418 | 0.2901 | 0.6747 | -1.0 | 0.0959 | 0.354 | 0.4052 | 0.3995 | 0.729 | -1.0 | 0.0149 | 0.1483 | 0.5786 | 0.6621 |
| 1.1067 | 19.0 | 1634 | 1.0634 | 0.2982 | 0.4997 | 0.3448 | 0.2918 | 0.6726 | -1.0 | 0.0981 | 0.3605 | 0.4124 | 0.4072 | 0.7271 | -1.0 | 0.0176 | 0.1595 | 0.5789 | 0.6654 |
| 1.1067 | 20.0 | 1720 | 1.0817 | 0.2984 | 0.5033 | 0.3386 | 0.2941 | 0.6425 | -1.0 | 0.0951 | 0.3557 | 0.4077 | 0.4028 | 0.7206 | -1.0 | 0.0172 | 0.1538 | 0.5796 | 0.6617 |
| 1.1067 | 21.0 | 1806 | 1.0688 | 0.3004 | 0.4941 | 0.3493 | 0.2959 | 0.6539 | -1.0 | 0.0979 | 0.3631 | 0.4109 | 0.4075 | 0.7084 | -1.0 | 0.0164 | 0.1534 | 0.5845 | 0.6685 |
| 1.1067 | 22.0 | 1892 | 1.0602 | 0.3052 | 0.5043 | 0.3517 | 0.3006 | 0.6705 | -1.0 | 0.0979 | 0.3651 | 0.4174 | 0.4127 | 0.7271 | -1.0 | 0.0209 | 0.163 | 0.5895 | 0.6719 |
| 1.1067 | 23.0 | 1978 | 1.0534 | 0.3061 | 0.501 | 0.3603 | 0.301 | 0.6674 | -1.0 | 0.0965 | 0.3646 | 0.4157 | 0.4106 | 0.7364 | -1.0 | 0.0199 | 0.1552 | 0.5923 | 0.6762 |
| 1.0314 | 24.0 | 2064 | 1.0490 | 0.3069 | 0.5057 | 0.3602 | 0.3012 | 0.6797 | -1.0 | 0.0983 | 0.3678 | 0.42 | 0.4142 | 0.7439 | -1.0 | 0.0188 | 0.1642 | 0.5951 | 0.6758 |
| 1.0314 | 25.0 | 2150 | 1.0600 | 0.3072 | 0.51 | 0.3643 | 0.3008 | 0.6851 | -1.0 | 0.0976 | 0.3673 | 0.4212 | 0.4157 | 0.7411 | -1.0 | 0.0203 | 0.1658 | 0.594 | 0.6765 |
| 1.0314 | 26.0 | 2236 | 1.0578 | 0.3092 | 0.5073 | 0.3689 | 0.3033 | 0.6886 | -1.0 | 0.099 | 0.3678 | 0.4228 | 0.4172 | 0.743 | -1.0 | 0.0215 | 0.1683 | 0.597 | 0.6773 |
| 1.0314 | 27.0 | 2322 | 1.0551 | 0.3102 | 0.5093 | 0.3679 | 0.3046 | 0.6894 | -1.0 | 0.0993 | 0.3685 | 0.4223 | 0.4162 | 0.7505 | -1.0 | 0.0215 | 0.1667 | 0.599 | 0.6779 |
| 1.0314 | 28.0 | 2408 | 1.0531 | 0.3107 | 0.5105 | 0.369 | 0.305 | 0.6888 | -1.0 | 0.0996 | 0.3685 | 0.4216 | 0.4154 | 0.7495 | -1.0 | 0.0222 | 0.1658 | 0.5992 | 0.6773 |
| 1.0314 | 29.0 | 2494 | 1.0528 | 0.3104 | 0.5077 | 0.3676 | 0.3045 | 0.6868 | -1.0 | 0.0992 | 0.3694 | 0.4211 | 0.4152 | 0.7467 | -1.0 | 0.0214 | 0.165 | 0.5994 | 0.6771 |
| 0.9801 | 30.0 | 2580 | 1.0529 | 0.3106 | 0.5077 | 0.3672 | 0.3046 | 0.6881 | -1.0 | 0.0994 | 0.3691 | 0.4215 | 0.4155 | 0.7477 | -1.0 | 0.0214 | 0.1656 | 0.5997 | 0.6773 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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