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README.md
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---
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license: apache-2.0
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base_model: microsoft/conditional-detr-resnet-50
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tags:
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- generated_from_trainer
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model-index:
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- name: queue_detection
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# queue_detection
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This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4686
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- Map: 0.2556
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- Map 50: 0.4262
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- Map 75: 0.2687
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- Map Small: -1.0
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- Map Medium: 0.0006
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- Map Large: 0.2572
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- Mar 1: 0.2033
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- Mar 10: 0.561
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- Mar 100: 0.715
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- Mar Small: -1.0
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- Mar Medium: 0.0036
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- Mar Large: 0.7212
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- Map Cashier: 0.3957
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- Mar 100 Cashier: 0.812
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- Map Cx: 0.1154
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- Mar 100 Cx: 0.618
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- num_epochs: 2
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- mixed_precision_training: Native AMP
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### Training results
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| 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 Cashier | Mar 100 Cashier | Map Cx | Mar 100 Cx |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:|:------:|:----------:|
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| No log | 1.0 | 218 | 1.6672 | 0.1422 | 0.2781 | 0.1441 | -1.0 | 0.0 | 0.1427 | 0.166 | 0.421 | 0.6116 | -1.0 | 0.0 | 0.6154 | 0.2345 | 0.7322 | 0.05 | 0.4911 |
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| No log | 2.0 | 436 | 1.4686 | 0.2556 | 0.4262 | 0.2687 | -1.0 | 0.0006 | 0.2572 | 0.2033 | 0.561 | 0.715 | -1.0 | 0.0036 | 0.7212 | 0.3957 | 0.812 | 0.1154 | 0.618 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.1+cpu
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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