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---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-arrows
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-arrows
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0051
- Map: 0.0157
- Map 50: 0.0235
- Map 75: 0.017
- Map Small: 0.0157
- Map Medium: -1.0
- Map Large: -1.0
- Mar 1: 0.0854
- Mar 10: 0.0917
- Mar 100: 0.2917
- Mar Small: 0.2917
- Mar Medium: -1.0
- Mar Large: -1.0
- Map Left: 0.0
- Mar 100 Left: 0.0
- Map Right: -1.0
- Mar 100 Right: -1.0
- Map Up: 0.0385
- Mar 100 Up: 0.2667
- Map Down: 0.0146
- Mar 100 Down: 0.8
- Map ?: 0.0099
- Mar 100 ?: 0.1
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 Left | Mar 100 Left | Map Right | Mar 100 Right | Map Up | Mar 100 Up | Map Down | Mar 100 Down | Map ? | Mar 100 ? |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:--------:|:------------:|:---------:|:-------------:|:------:|:----------:|:--------:|:------------:|:------:|:---------:|
| No log | 1.0 | 8 | 2.9834 | 0.0001 | 0.0014 | 0.0 | 0.0002 | -1.0 | -1.0 | 0.0 | 0.0 | 0.025 | 0.025 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0006 | 0.1 | 0.0 | 0.0 |
| No log | 2.0 | 16 | 2.3564 | 0.0005 | 0.0023 | 0.0 | 0.0007 | -1.0 | -1.0 | 0.0 | 0.0 | 0.05 | 0.05 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0019 | 0.2 | 0.0 | 0.0 |
| No log | 3.0 | 24 | 2.1618 | 0.001 | 0.0025 | 0.0 | 0.0012 | -1.0 | -1.0 | 0.0 | 0.0 | 0.1 | 0.1 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.004 | 0.4 | 0.0 | 0.0 |
| No log | 4.0 | 32 | 2.0074 | 0.0005 | 0.0027 | 0.0 | 0.0007 | -1.0 | -1.0 | 0.0 | 0.0 | 0.05 | 0.05 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0022 | 0.2 | 0.0 | 0.0 |
| No log | 5.0 | 40 | 1.7452 | 0.0008 | 0.003 | 0.0 | 0.0009 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0812 | 0.0812 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0028 | 0.3 | 0.0003 | 0.025 |
| No log | 6.0 | 48 | 1.6746 | 0.0009 | 0.0024 | 0.0 | 0.0011 | -1.0 | -1.0 | 0.0 | 0.0 | 0.1 | 0.1 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0038 | 0.4 | 0.0 | 0.0 |
| No log | 7.0 | 56 | 1.5161 | 0.0014 | 0.0023 | 0.0023 | 0.0016 | -1.0 | -1.0 | 0.0 | 0.0 | 0.15 | 0.15 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0054 | 0.6 | 0.0 | 0.0 |
| No log | 8.0 | 64 | 1.5721 | 0.0009 | 0.0017 | 0.0 | 0.001 | -1.0 | -1.0 | 0.0 | 0.0 | 0.125 | 0.125 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0034 | 0.5 | 0.0 | 0.0 |
| No log | 9.0 | 72 | 1.4536 | 0.0014 | 0.0021 | 0.0021 | 0.0021 | -1.0 | -1.0 | 0.0 | 0.0 | 0.175 | 0.175 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0056 | 0.7 | 0.0 | 0.0 |
| No log | 10.0 | 80 | 1.2638 | 0.0013 | 0.0023 | 0.0014 | 0.0019 | -1.0 | -1.0 | 0.0 | 0.0 | 0.175 | 0.175 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0052 | 0.7 | 0.0 | 0.0 |
| No log | 11.0 | 88 | 1.2646 | 0.0015 | 0.0025 | 0.0014 | 0.0025 | -1.0 | -1.0 | 0.0 | 0.0 | 0.175 | 0.175 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0062 | 0.7 | 0.0 | 0.0 |
| No log | 12.0 | 96 | 1.2239 | 0.0023 | 0.003 | 0.003 | 0.0038 | -1.0 | -1.0 | 0.0 | 0.0 | 0.2 | 0.2 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0092 | 0.8 | 0.0 | 0.0 |
| No log | 13.0 | 104 | 1.1191 | 0.0023 | 0.0057 | 0.0025 | 0.0035 | -1.0 | -1.0 | 0.0 | 0.0125 | 0.1875 | 0.1875 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0068 | 0.7 | 0.0026 | 0.05 |
| No log | 14.0 | 112 | 1.1491 | 0.0029 | 0.0054 | 0.002 | 0.0038 | -1.0 | -1.0 | 0.0312 | 0.0312 | 0.1813 | 0.1813 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0 | 0.0 | 0.0047 | 0.6 | 0.0068 | 0.125 |
| No log | 15.0 | 120 | 1.1062 | 0.02 | 0.0349 | 0.0297 | 0.0287 | -1.0 | -1.0 | 0.05 | 0.075 | 0.25 | 0.25 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0673 | 0.2 | 0.0053 | 0.7 | 0.0074 | 0.1 |
| No log | 16.0 | 128 | 1.0963 | 0.0705 | 0.0925 | 0.086 | 0.0705 | -1.0 | -1.0 | 0.0854 | 0.0854 | 0.2354 | 0.2354 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.2693 | 0.2667 | 0.0058 | 0.6 | 0.007 | 0.075 |
| No log | 17.0 | 136 | 1.0369 | 0.0349 | 0.051 | 0.0434 | 0.0349 | -1.0 | -1.0 | 0.075 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.1234 | 0.2667 | 0.0049 | 0.8 | 0.0114 | 0.1 |
| No log | 18.0 | 144 | 1.0721 | 0.0324 | 0.05 | 0.0435 | 0.0324 | -1.0 | -1.0 | 0.0771 | 0.0771 | 0.2521 | 0.2521 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.1178 | 0.2333 | 0.004 | 0.7 | 0.0077 | 0.075 |
| No log | 19.0 | 152 | 1.0081 | 0.0623 | 0.0922 | 0.0858 | 0.0625 | -1.0 | -1.0 | 0.0646 | 0.0833 | 0.2833 | 0.2833 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.2356 | 0.2333 | 0.0051 | 0.8 | 0.0085 | 0.1 |
| No log | 20.0 | 160 | 1.0390 | 0.0367 | 0.0504 | 0.044 | 0.0367 | -1.0 | -1.0 | 0.0729 | 0.0854 | 0.2604 | 0.2604 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.1347 | 0.2667 | 0.0053 | 0.7 | 0.0071 | 0.075 |
| No log | 21.0 | 168 | 1.0262 | 0.0255 | 0.0371 | 0.0299 | 0.0255 | -1.0 | -1.0 | 0.0729 | 0.0854 | 0.2604 | 0.2604 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0898 | 0.2667 | 0.0052 | 0.7 | 0.0068 | 0.075 |
| No log | 22.0 | 176 | 0.9972 | 0.0134 | 0.0223 | 0.0159 | 0.0134 | -1.0 | -1.0 | 0.0646 | 0.0833 | 0.2833 | 0.2833 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0393 | 0.2333 | 0.0058 | 0.8 | 0.0086 | 0.1 |
| No log | 23.0 | 184 | 1.0195 | 0.0135 | 0.0204 | 0.014 | 0.0135 | -1.0 | -1.0 | 0.0729 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0061 | 0.8 | 0.0092 | 0.1 |
| No log | 24.0 | 192 | 1.0240 | 0.0145 | 0.023 | 0.0139 | 0.0145 | -1.0 | -1.0 | 0.0792 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0101 | 0.8 | 0.0095 | 0.1 |
| No log | 25.0 | 200 | 1.0151 | 0.0157 | 0.024 | 0.0176 | 0.0157 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0147 | 0.8 | 0.0096 | 0.1 |
| No log | 26.0 | 208 | 1.0143 | 0.0153 | 0.0233 | 0.0168 | 0.0153 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.013 | 0.8 | 0.0099 | 0.1 |
| No log | 27.0 | 216 | 1.0092 | 0.0157 | 0.0235 | 0.017 | 0.0157 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0146 | 0.8 | 0.0099 | 0.1 |
| No log | 28.0 | 224 | 1.0051 | 0.0157 | 0.0235 | 0.017 | 0.0157 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0146 | 0.8 | 0.0099 | 0.1 |
| No log | 29.0 | 232 | 1.0046 | 0.0157 | 0.0235 | 0.017 | 0.0157 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0146 | 0.8 | 0.0099 | 0.1 |
| No log | 30.0 | 240 | 1.0051 | 0.0157 | 0.0235 | 0.017 | 0.0157 | -1.0 | -1.0 | 0.0854 | 0.0917 | 0.2917 | 0.2917 | -1.0 | -1.0 | 0.0 | 0.0 | -1.0 | -1.0 | 0.0385 | 0.2667 | 0.0146 | 0.8 | 0.0099 | 0.1 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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