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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_finetuned_oculardataset
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_finetuned_oculardataset
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: 1.0672
- Map: 0.3032
- Map 50: 0.4973
- Map 75: 0.3701
- Map Small: 0.2981
- Map Medium: 0.6746
- Map Large: -1.0
- Mar 1: 0.1
- Mar 10: 0.3678
- Mar 100: 0.4114
- Mar Small: 0.4054
- Mar Medium: 0.7421
- Mar Large: -1.0
- Map Falciparum Trophozoite: 0.0156
- Mar 100 Falciparum Trophozoite: 0.1511
- Map Wbc: 0.5908
- Mar 100 Wbc: 0.6716
## 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 | 1.6645 | 0.131 | 0.2562 | 0.1153 | 0.1289 | 0.3974 | -1.0 | 0.0647 | 0.2312 | 0.3164 | 0.314 | 0.6159 | -1.0 | 0.0004 | 0.0456 | 0.2616 | 0.5873 |
| No log | 2.0 | 172 | 1.4800 | 0.2028 | 0.4079 | 0.1766 | 0.1993 | 0.4876 | -1.0 | 0.0677 | 0.2725 | 0.3282 | 0.3251 | 0.628 | -1.0 | 0.0007 | 0.0648 | 0.405 | 0.5915 |
| No log | 3.0 | 258 | 1.3829 | 0.2264 | 0.4496 | 0.1936 | 0.2193 | 0.5542 | -1.0 | 0.0729 | 0.2807 | 0.3215 | 0.3168 | 0.629 | -1.0 | 0.0019 | 0.0706 | 0.451 | 0.5725 |
| No log | 4.0 | 344 | 1.3318 | 0.2089 | 0.4403 | 0.1427 | 0.2056 | 0.4726 | -1.0 | 0.0691 | 0.2751 | 0.3221 | 0.3116 | 0.6748 | -1.0 | 0.002 | 0.0941 | 0.4158 | 0.5502 |
| No log | 5.0 | 430 | 1.2739 | 0.2454 | 0.4562 | 0.2342 | 0.2354 | 0.614 | -1.0 | 0.0777 | 0.3046 | 0.3482 | 0.338 | 0.7262 | -1.0 | 0.002 | 0.0906 | 0.4888 | 0.6058 |
| 1.7665 | 6.0 | 516 | 1.2365 | 0.2599 | 0.4744 | 0.2599 | 0.2522 | 0.6258 | -1.0 | 0.0846 | 0.3217 | 0.361 | 0.354 | 0.7 | -1.0 | 0.005 | 0.1047 | 0.5149 | 0.6173 |
| 1.7665 | 7.0 | 602 | 1.2548 | 0.2488 | 0.4689 | 0.2302 | 0.2434 | 0.5622 | -1.0 | 0.0788 | 0.31 | 0.3519 | 0.3446 | 0.6888 | -1.0 | 0.0038 | 0.1012 | 0.4938 | 0.6026 |
| 1.7665 | 8.0 | 688 | 1.2031 | 0.2715 | 0.474 | 0.3074 | 0.2664 | 0.6153 | -1.0 | 0.0897 | 0.3309 | 0.3744 | 0.3723 | 0.657 | -1.0 | 0.0058 | 0.1164 | 0.5373 | 0.6325 |
| 1.7665 | 9.0 | 774 | 1.2492 | 0.2417 | 0.4715 | 0.2154 | 0.2349 | 0.5753 | -1.0 | 0.0789 | 0.3064 | 0.3503 | 0.342 | 0.686 | -1.0 | 0.0043 | 0.1129 | 0.4791 | 0.5877 |
| 1.7665 | 10.0 | 860 | 1.1861 | 0.2752 | 0.4772 | 0.2891 | 0.2683 | 0.6259 | -1.0 | 0.0872 | 0.3342 | 0.3823 | 0.379 | 0.6813 | -1.0 | 0.0061 | 0.1217 | 0.5443 | 0.6429 |
| 1.7665 | 11.0 | 946 | 1.1996 | 0.2607 | 0.4605 | 0.2779 | 0.2565 | 0.5972 | -1.0 | 0.085 | 0.326 | 0.3722 | 0.3669 | 0.6813 | -1.0 | 0.0041 | 0.1254 | 0.5173 | 0.6189 |
| 1.2663 | 12.0 | 1032 | 1.1664 | 0.2764 | 0.4753 | 0.3137 | 0.2718 | 0.6148 | -1.0 | 0.0892 | 0.333 | 0.3781 | 0.3741 | 0.685 | -1.0 | 0.0054 | 0.1188 | 0.5473 | 0.6375 |
| 1.2663 | 13.0 | 1118 | 1.1451 | 0.2804 | 0.4694 | 0.3212 | 0.2732 | 0.6595 | -1.0 | 0.092 | 0.3412 | 0.3852 | 0.3787 | 0.7187 | -1.0 | 0.0051 | 0.1282 | 0.5557 | 0.6421 |
| 1.2663 | 14.0 | 1204 | 1.1251 | 0.2889 | 0.4761 | 0.3401 | 0.2835 | 0.6619 | -1.0 | 0.0926 | 0.3496 | 0.3979 | 0.393 | 0.714 | -1.0 | 0.0091 | 0.1391 | 0.5687 | 0.6567 |
| 1.2663 | 15.0 | 1290 | 1.1493 | 0.2778 | 0.4695 | 0.3126 | 0.2706 | 0.6531 | -1.0 | 0.0911 | 0.3415 | 0.3881 | 0.3792 | 0.743 | -1.0 | 0.0054 | 0.1382 | 0.5502 | 0.6379 |
| 1.2663 | 16.0 | 1376 | 1.1125 | 0.2846 | 0.4799 | 0.3307 | 0.2804 | 0.6415 | -1.0 | 0.0926 | 0.3498 | 0.4005 | 0.3954 | 0.7159 | -1.0 | 0.0075 | 0.1452 | 0.5617 | 0.6558 |
| 1.2663 | 17.0 | 1462 | 1.1002 | 0.2909 | 0.4816 | 0.3471 | 0.2859 | 0.6545 | -1.0 | 0.0956 | 0.3554 | 0.4036 | 0.3969 | 0.7421 | -1.0 | 0.0077 | 0.145 | 0.5741 | 0.6622 |
| 1.1448 | 18.0 | 1548 | 1.1066 | 0.2853 | 0.484 | 0.3205 | 0.2796 | 0.6647 | -1.0 | 0.0918 | 0.3472 | 0.3944 | 0.3883 | 0.7196 | -1.0 | 0.0092 | 0.1415 | 0.5613 | 0.6474 |
| 1.1448 | 19.0 | 1634 | 1.0993 | 0.2933 | 0.4838 | 0.3441 | 0.2884 | 0.6683 | -1.0 | 0.0978 | 0.3581 | 0.401 | 0.3958 | 0.7252 | -1.0 | 0.0079 | 0.1374 | 0.5787 | 0.6645 |
| 1.1448 | 20.0 | 1720 | 1.0850 | 0.298 | 0.4855 | 0.3594 | 0.2923 | 0.6669 | -1.0 | 0.0963 | 0.3606 | 0.4011 | 0.3952 | 0.7374 | -1.0 | 0.0093 | 0.1348 | 0.5867 | 0.6675 |
| 1.1448 | 21.0 | 1806 | 1.0814 | 0.3006 | 0.4908 | 0.3618 | 0.2951 | 0.6868 | -1.0 | 0.0994 | 0.3628 | 0.4056 | 0.4001 | 0.7355 | -1.0 | 0.0117 | 0.1413 | 0.5896 | 0.67 |
| 1.1448 | 22.0 | 1892 | 1.0836 | 0.2975 | 0.495 | 0.3541 | 0.2924 | 0.6712 | -1.0 | 0.0989 | 0.3628 | 0.4084 | 0.4036 | 0.7196 | -1.0 | 0.0135 | 0.1534 | 0.5816 | 0.6633 |
| 1.1448 | 23.0 | 1978 | 1.0813 | 0.2996 | 0.4965 | 0.3567 | 0.2941 | 0.6792 | -1.0 | 0.0979 | 0.3625 | 0.408 | 0.402 | 0.7364 | -1.0 | 0.015 | 0.1505 | 0.5842 | 0.6655 |
| 1.0601 | 24.0 | 2064 | 1.0707 | 0.3048 | 0.4952 | 0.3624 | 0.2987 | 0.6876 | -1.0 | 0.0981 | 0.3659 | 0.4118 | 0.4054 | 0.7486 | -1.0 | 0.0144 | 0.1501 | 0.5951 | 0.6735 |
| 1.0601 | 25.0 | 2150 | 1.0736 | 0.2982 | 0.4935 | 0.3584 | 0.2931 | 0.6732 | -1.0 | 0.0992 | 0.3638 | 0.41 | 0.4053 | 0.7224 | -1.0 | 0.0126 | 0.1521 | 0.5839 | 0.6678 |
| 1.0601 | 26.0 | 2236 | 1.0717 | 0.3034 | 0.4978 | 0.3622 | 0.2986 | 0.6788 | -1.0 | 0.0995 | 0.3659 | 0.411 | 0.405 | 0.7421 | -1.0 | 0.015 | 0.1501 | 0.5918 | 0.6719 |
| 1.0601 | 27.0 | 2322 | 1.0688 | 0.3025 | 0.4978 | 0.3622 | 0.2975 | 0.6747 | -1.0 | 0.1 | 0.3674 | 0.4108 | 0.4047 | 0.7421 | -1.0 | 0.0161 | 0.1524 | 0.5888 | 0.6693 |
| 1.0601 | 28.0 | 2408 | 1.0679 | 0.3031 | 0.4968 | 0.3638 | 0.2976 | 0.6805 | -1.0 | 0.0999 | 0.3679 | 0.4106 | 0.4046 | 0.7421 | -1.0 | 0.0156 | 0.1507 | 0.5905 | 0.6705 |
| 1.0601 | 29.0 | 2494 | 1.0669 | 0.3035 | 0.4976 | 0.3717 | 0.2985 | 0.6751 | -1.0 | 0.0999 | 0.368 | 0.4115 | 0.4055 | 0.743 | -1.0 | 0.0156 | 0.1509 | 0.5915 | 0.6721 |
| 1.0103 | 30.0 | 2580 | 1.0672 | 0.3032 | 0.4973 | 0.3701 | 0.2981 | 0.6746 | -1.0 | 0.1 | 0.3678 | 0.4114 | 0.4054 | 0.7421 | -1.0 | 0.0156 | 0.1511 | 0.5908 | 0.6716 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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