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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: 1.0598
- Map: 0.3166
- Map 50: 0.5255
- Map 75: 0.3725
- Map Small: 0.3115
- Map Medium: 0.6744
- Map Large: -1.0
- Mar 1: 0.1043
- Mar 10: 0.3801
- Mar 100: 0.4224
- Mar Small: 0.4186
- Mar Medium: 0.7234
- Mar Large: -1.0
- Map Falciparum Trophozoite: 0.0341
- Mar 100 Falciparum Trophozoite: 0.1663
- Map Wbc: 0.599
- Mar 100 Wbc: 0.6785
## 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 | 1.1493 | 0.2788 | 0.5002 | 0.3024 | 0.274 | 0.6184 | -1.0 | 0.0918 | 0.3473 | 0.386 | 0.3823 | 0.6785 | -1.0 | 0.0196 | 0.1372 | 0.5381 | 0.6348 |
| No log | 2.0 | 172 | 1.1199 | 0.2924 | 0.5063 | 0.3371 | 0.2866 | 0.6545 | -1.0 | 0.0924 | 0.3509 | 0.3873 | 0.3805 | 0.729 | -1.0 | 0.0204 | 0.1264 | 0.5644 | 0.6483 |
| No log | 3.0 | 258 | 1.1616 | 0.2802 | 0.4941 | 0.3138 | 0.2746 | 0.6231 | -1.0 | 0.0891 | 0.3378 | 0.377 | 0.374 | 0.6598 | -1.0 | 0.0167 | 0.129 | 0.5438 | 0.6249 |
| No log | 4.0 | 344 | 1.1263 | 0.3014 | 0.517 | 0.3393 | 0.296 | 0.6609 | -1.0 | 0.0981 | 0.3588 | 0.3857 | 0.3806 | 0.7187 | -1.0 | 0.0258 | 0.1135 | 0.577 | 0.6579 |
| No log | 5.0 | 430 | 1.1219 | 0.2801 | 0.5117 | 0.297 | 0.2734 | 0.6555 | -1.0 | 0.0905 | 0.3458 | 0.3795 | 0.3737 | 0.7028 | -1.0 | 0.0218 | 0.1254 | 0.5385 | 0.6337 |
| 1.0831 | 6.0 | 516 | 1.1299 | 0.2646 | 0.485 | 0.2705 | 0.2581 | 0.6103 | -1.0 | 0.0885 | 0.3294 | 0.371 | 0.3636 | 0.7 | -1.0 | 0.0117 | 0.1288 | 0.5175 | 0.6131 |
| 1.0831 | 7.0 | 602 | 1.1003 | 0.2934 | 0.5064 | 0.3254 | 0.286 | 0.6706 | -1.0 | 0.0933 | 0.357 | 0.3962 | 0.3905 | 0.7206 | -1.0 | 0.0207 | 0.1397 | 0.5661 | 0.6528 |
| 1.0831 | 8.0 | 688 | 1.1063 | 0.2945 | 0.5028 | 0.3407 | 0.2871 | 0.6606 | -1.0 | 0.0946 | 0.3568 | 0.3975 | 0.3938 | 0.6925 | -1.0 | 0.0243 | 0.1454 | 0.5647 | 0.6495 |
| 1.0831 | 9.0 | 774 | 1.1364 | 0.2824 | 0.4979 | 0.3114 | 0.2774 | 0.622 | -1.0 | 0.0928 | 0.3445 | 0.3844 | 0.3818 | 0.671 | -1.0 | 0.017 | 0.1297 | 0.5479 | 0.6392 |
| 1.0831 | 10.0 | 860 | 1.0997 | 0.2904 | 0.501 | 0.3299 | 0.2841 | 0.6483 | -1.0 | 0.0908 | 0.3515 | 0.3917 | 0.387 | 0.7065 | -1.0 | 0.02 | 0.1329 | 0.5609 | 0.6505 |
| 1.0831 | 11.0 | 946 | 1.1198 | 0.2826 | 0.496 | 0.3186 | 0.277 | 0.6299 | -1.0 | 0.0915 | 0.3426 | 0.3822 | 0.3778 | 0.6832 | -1.0 | 0.0225 | 0.1342 | 0.5427 | 0.6303 |
| 1.0585 | 12.0 | 1032 | 1.0999 | 0.2921 | 0.5038 | 0.3196 | 0.2867 | 0.6334 | -1.0 | 0.0953 | 0.3556 | 0.3954 | 0.3916 | 0.6897 | -1.0 | 0.0244 | 0.1454 | 0.5599 | 0.6453 |
| 1.0585 | 13.0 | 1118 | 1.1097 | 0.2966 | 0.5183 | 0.3365 | 0.29 | 0.6667 | -1.0 | 0.0995 | 0.3549 | 0.3983 | 0.3923 | 0.7178 | -1.0 | 0.0297 | 0.1493 | 0.5636 | 0.6472 |
| 1.0585 | 14.0 | 1204 | 1.0932 | 0.2964 | 0.5113 | 0.335 | 0.2913 | 0.6494 | -1.0 | 0.0969 | 0.3556 | 0.396 | 0.391 | 0.7037 | -1.0 | 0.0279 | 0.1474 | 0.565 | 0.6447 |
| 1.0585 | 15.0 | 1290 | 1.0951 | 0.2958 | 0.5173 | 0.3287 | 0.2915 | 0.6321 | -1.0 | 0.0969 | 0.3596 | 0.4018 | 0.3962 | 0.7187 | -1.0 | 0.0321 | 0.1505 | 0.5595 | 0.653 |
| 1.0585 | 16.0 | 1376 | 1.1036 | 0.3048 | 0.5215 | 0.3482 | 0.2997 | 0.6588 | -1.0 | 0.102 | 0.3633 | 0.4029 | 0.3974 | 0.7234 | -1.0 | 0.0321 | 0.1481 | 0.5775 | 0.6578 |
| 1.0585 | 17.0 | 1462 | 1.0973 | 0.2997 | 0.5169 | 0.3437 | 0.2943 | 0.6445 | -1.0 | 0.1006 | 0.3589 | 0.3994 | 0.3963 | 0.6907 | -1.0 | 0.0306 | 0.1448 | 0.5688 | 0.654 |
| 0.968 | 18.0 | 1548 | 1.1322 | 0.3029 | 0.5193 | 0.3482 | 0.2973 | 0.6525 | -1.0 | 0.0983 | 0.3636 | 0.4015 | 0.3977 | 0.6991 | -1.0 | 0.032 | 0.1499 | 0.5738 | 0.6532 |
| 0.968 | 19.0 | 1634 | 1.0698 | 0.3049 | 0.5114 | 0.3471 | 0.2989 | 0.6757 | -1.0 | 0.0992 | 0.3665 | 0.4138 | 0.4084 | 0.729 | -1.0 | 0.0288 | 0.1634 | 0.581 | 0.6643 |
| 0.968 | 20.0 | 1720 | 1.0780 | 0.3093 | 0.516 | 0.3556 | 0.3036 | 0.6647 | -1.0 | 0.101 | 0.3694 | 0.4145 | 0.4096 | 0.7252 | -1.0 | 0.0299 | 0.1618 | 0.5887 | 0.6673 |
| 0.968 | 21.0 | 1806 | 1.0825 | 0.3044 | 0.522 | 0.3357 | 0.2981 | 0.6642 | -1.0 | 0.0982 | 0.3653 | 0.4071 | 0.4029 | 0.7075 | -1.0 | 0.0319 | 0.1564 | 0.5768 | 0.6578 |
| 0.968 | 22.0 | 1892 | 1.0660 | 0.3142 | 0.5195 | 0.3691 | 0.3096 | 0.6679 | -1.0 | 0.1028 | 0.3764 | 0.4195 | 0.4158 | 0.7187 | -1.0 | 0.0352 | 0.164 | 0.5933 | 0.675 |
| 0.968 | 23.0 | 1978 | 1.0604 | 0.3145 | 0.5256 | 0.3633 | 0.3093 | 0.674 | -1.0 | 0.1031 | 0.3774 | 0.4199 | 0.4152 | 0.729 | -1.0 | 0.0368 | 0.1669 | 0.5922 | 0.6729 |
| 0.9092 | 24.0 | 2064 | 1.0607 | 0.3168 | 0.5266 | 0.3768 | 0.3114 | 0.6848 | -1.0 | 0.1039 | 0.3785 | 0.4233 | 0.4186 | 0.7374 | -1.0 | 0.034 | 0.1654 | 0.5996 | 0.6812 |
| 0.9092 | 25.0 | 2150 | 1.0681 | 0.3163 | 0.5283 | 0.3656 | 0.3113 | 0.6751 | -1.0 | 0.1053 | 0.3769 | 0.4185 | 0.4148 | 0.7196 | -1.0 | 0.0352 | 0.1616 | 0.5975 | 0.6755 |
| 0.9092 | 26.0 | 2236 | 1.0641 | 0.3158 | 0.5239 | 0.3708 | 0.3106 | 0.6715 | -1.0 | 0.1045 | 0.378 | 0.4217 | 0.4181 | 0.7196 | -1.0 | 0.0339 | 0.1656 | 0.5977 | 0.6777 |
| 0.9092 | 27.0 | 2322 | 1.0644 | 0.3162 | 0.526 | 0.3721 | 0.311 | 0.6785 | -1.0 | 0.1035 | 0.3775 | 0.42 | 0.4164 | 0.7206 | -1.0 | 0.0336 | 0.1624 | 0.5988 | 0.6777 |
| 0.9092 | 28.0 | 2408 | 1.0606 | 0.3165 | 0.5241 | 0.374 | 0.3114 | 0.6784 | -1.0 | 0.1052 | 0.3794 | 0.4223 | 0.4184 | 0.7252 | -1.0 | 0.0343 | 0.1665 | 0.5988 | 0.6782 |
| 0.9092 | 29.0 | 2494 | 1.0600 | 0.3161 | 0.5249 | 0.3728 | 0.311 | 0.6744 | -1.0 | 0.1043 | 0.3795 | 0.4219 | 0.418 | 0.7234 | -1.0 | 0.0341 | 0.1661 | 0.5981 | 0.6777 |
| 0.8509 | 30.0 | 2580 | 1.0598 | 0.3166 | 0.5255 | 0.3725 | 0.3115 | 0.6744 | -1.0 | 0.1043 | 0.3801 | 0.4224 | 0.4186 | 0.7234 | -1.0 | 0.0341 | 0.1663 | 0.599 | 0.6785 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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