---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr
  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

This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7117

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0345        | 0.08  | 200   | 1.8447          |
| 1.5511        | 0.16  | 400   | 1.4217          |
| 1.444         | 0.24  | 600   | 1.3814          |
| 1.3746        | 0.32  | 800   | 1.3241          |
| 1.2361        | 0.4   | 1000  | 1.2589          |
| 1.3506        | 0.48  | 1200  | 1.2441          |
| 1.2833        | 0.56  | 1400  | 1.2052          |
| 1.1051        | 0.64  | 1600  | 1.0607          |
| 1.1091        | 0.72  | 1800  | 1.0610          |
| 1.0295        | 0.8   | 2000  | 1.0241          |
| 1.1376        | 0.88  | 2200  | 1.0846          |
| 1.1172        | 0.96  | 2400  | 1.1095          |
| 1.0186        | 1.04  | 2600  | 0.9978          |
| 1.0775        | 1.12  | 2800  | 1.0225          |
| 0.9973        | 1.2   | 3000  | 0.9934          |
| 1.006         | 1.28  | 3200  | 0.9886          |
| 0.9814        | 1.36  | 3400  | 0.9256          |
| 1.0253        | 1.44  | 3600  | 0.9209          |
| 0.9932        | 1.52  | 3800  | 0.9159          |
| 0.9307        | 1.6   | 4000  | 0.9058          |
| 0.9103        | 1.68  | 4200  | 0.9049          |
| 0.9034        | 1.76  | 4400  | 0.8643          |
| 0.9544        | 1.84  | 4600  | 0.9114          |
| 0.889         | 1.92  | 4800  | 0.8880          |
| 0.8888        | 2.0   | 5000  | 0.8515          |
| 0.8877        | 2.08  | 5200  | 0.8707          |
| 0.8799        | 2.16  | 5400  | 0.8458          |
| 0.8398        | 2.24  | 5600  | 0.8292          |
| 0.8181        | 2.32  | 5800  | 0.8226          |
| 0.8876        | 2.4   | 6000  | 0.8021          |
| 0.8893        | 2.48  | 6200  | 0.8173          |
| 0.8497        | 2.56  | 6400  | 0.7870          |
| 0.8369        | 2.64  | 6600  | 0.7719          |
| 0.8213        | 2.72  | 6800  | 0.7877          |
| 0.8044        | 2.8   | 7000  | 0.7763          |
| 0.8087        | 2.88  | 7200  | 0.7702          |
| 0.7616        | 2.96  | 7400  | 0.7570          |
| 0.7901        | 3.04  | 7600  | 0.7451          |
| 0.8454        | 3.12  | 7800  | 0.7560          |
| 0.7428        | 3.2   | 8000  | 0.7455          |
| 0.822         | 3.28  | 8200  | 0.7390          |
| 0.8293        | 3.36  | 8400  | 0.7324          |
| 0.7196        | 3.44  | 8600  | 0.7270          |
| 0.7508        | 3.52  | 8800  | 0.7357          |
| 0.783         | 3.6   | 9000  | 0.7293          |
| 0.7094        | 3.68  | 9200  | 0.7276          |
| 0.7811        | 3.76  | 9400  | 0.7178          |
| 0.7765        | 3.84  | 9600  | 0.7129          |
| 0.7542        | 3.92  | 9800  | 0.7165          |
| 0.756         | 4.0   | 10000 | 0.7117          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2