---
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: 1.0196

## 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: 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8584        | 0.02  | 50   | 3.3010          |
| 2.7538        | 0.04  | 100  | 2.5486          |
| 2.2986        | 0.06  | 150  | 2.2761          |
| 2.0637        | 0.08  | 200  | 2.0595          |
| 1.9565        | 0.1   | 250  | 1.9289          |
| 1.9208        | 0.12  | 300  | 1.9521          |
| 1.9024        | 0.14  | 350  | 1.8841          |
| 1.8294        | 0.16  | 400  | 1.7362          |
| 1.7064        | 0.18  | 450  | 1.6461          |
| 1.6336        | 0.2   | 500  | 1.5706          |
| 1.5009        | 0.22  | 550  | 1.5610          |
| 1.639         | 0.24  | 600  | 1.5916          |
| 1.4837        | 0.26  | 650  | 1.4572          |
| 1.4384        | 0.28  | 700  | 1.4170          |
| 1.4366        | 0.3   | 750  | 1.4112          |
| 1.4204        | 0.32  | 800  | 1.3324          |
| 1.2496        | 0.34  | 850  | 1.2658          |
| 1.313         | 0.36  | 900  | 1.2854          |
| 1.2573        | 0.38  | 950  | 1.2485          |
| 1.2961        | 0.4   | 1000 | 1.2550          |
| 1.2419        | 0.42  | 1050 | 1.2334          |
| 1.2132        | 0.44  | 1100 | 1.2097          |
| 1.237         | 0.46  | 1150 | 1.1870          |
| 1.2395        | 0.48  | 1200 | 1.2277          |
| 1.2789        | 0.5   | 1250 | 1.2159          |
| 1.2264        | 0.52  | 1300 | 1.1848          |
| 1.2875        | 0.54  | 1350 | 1.1683          |
| 1.1939        | 0.56  | 1400 | 1.1437          |
| 1.1407        | 0.58  | 1450 | 1.1325          |
| 1.1727        | 0.6   | 1500 | 1.1204          |
| 1.1618        | 0.62  | 1550 | 1.1065          |
| 1.1374        | 0.64  | 1600 | 1.0942          |
| 1.1241        | 0.66  | 1650 | 1.0965          |
| 1.0826        | 0.68  | 1700 | 1.0910          |
| 1.1185        | 0.7   | 1750 | 1.0853          |
| 1.1238        | 0.72  | 1800 | 1.0656          |
| 1.1146        | 0.74  | 1850 | 1.0486          |
| 1.1339        | 0.76  | 1900 | 1.0652          |
| 1.0464        | 0.78  | 1950 | 1.0542          |
| 1.0563        | 0.8   | 2000 | 1.0534          |
| 1.0896        | 0.82  | 2050 | 1.0510          |
| 1.0753        | 0.84  | 2100 | 1.0300          |
| 1.0979        | 0.86  | 2150 | 1.0408          |
| 1.0831        | 0.88  | 2200 | 1.0328          |
| 1.0936        | 0.9   | 2250 | 1.0215          |
| 1.1161        | 0.92  | 2300 | 1.0238          |
| 1.0251        | 0.94  | 2350 | 1.0109          |
| 1.0676        | 0.96  | 2400 | 1.0147          |
| 1.0461        | 0.98  | 2450 | 1.0109          |
| 1.0386        | 1.0   | 2500 | 1.0196          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2