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# detr-resnet-50_finetuned_bbatch

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: 2.3729

## 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: 1e-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: linear
- num_epochs: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.0202        | 4.17   | 50   | 3.2912          |
| 3.2043        | 8.33   | 100  | 3.1033          |
| 3.0326        | 12.5   | 150  | 3.0882          |
| 2.957         | 16.67  | 200  | 2.9938          |
| 2.8396        | 20.83  | 250  | 2.8404          |
| 2.7485        | 25.0   | 300  | 2.8785          |
| 2.6515        | 29.17  | 350  | 2.7908          |
| 2.5651        | 33.33  | 400  | 2.7162          |
| 2.5172        | 37.5   | 450  | 2.6793          |
| 2.434         | 41.67  | 500  | 2.6200          |
| 2.3921        | 45.83  | 550  | 2.6078          |
| 2.37          | 50.0   | 600  | 2.5634          |
| 2.3211        | 54.17  | 650  | 2.5730          |
| 2.2658        | 58.33  | 700  | 2.4883          |
| 2.2487        | 62.5   | 750  | 2.6039          |
| 2.2001        | 66.67  | 800  | 2.4645          |
| 2.1767        | 70.83  | 850  | 2.5015          |
| 2.1533        | 75.0   | 900  | 2.4727          |
| 2.1489        | 79.17  | 950  | 2.4663          |
| 2.1181        | 83.33  | 1000 | 2.4865          |
| 2.0739        | 87.5   | 1050 | 2.4522          |
| 2.0664        | 91.67  | 1100 | 2.4344          |
| 2.0347        | 95.83  | 1150 | 2.4347          |
| 2.0175        | 100.0  | 1200 | 2.4883          |
| 1.9989        | 104.17 | 1250 | 2.4523          |
| 1.9773        | 108.33 | 1300 | 2.4971          |
| 1.9473        | 112.5  | 1350 | 2.5026          |
| 1.9431        | 116.67 | 1400 | 2.4029          |
| 1.9274        | 120.83 | 1450 | 2.4887          |
| 1.907         | 125.0  | 1500 | 2.4216          |
| 1.9006        | 129.17 | 1550 | 2.4143          |
| 1.8775        | 133.33 | 1600 | 2.4107          |
| 1.8795        | 137.5  | 1650 | 2.3722          |
| 1.8582        | 141.67 | 1700 | 2.3952          |
| 1.8538        | 145.83 | 1750 | 2.3964          |
| 1.8409        | 150.0  | 1800 | 2.4083          |
| 1.8302        | 154.17 | 1850 | 2.4013          |
| 1.8203        | 158.33 | 1900 | 2.3768          |
| 1.8112        | 162.5  | 1950 | 2.4538          |
| 1.7935        | 166.67 | 2000 | 2.3903          |
| 1.8035        | 170.83 | 2050 | 2.3707          |
| 1.7955        | 175.0  | 2100 | 2.3588          |
| 1.7786        | 179.17 | 2150 | 2.3775          |
| 1.7988        | 183.33 | 2200 | 2.3376          |
| 1.7808        | 187.5  | 2250 | 2.3629          |
| 1.7802        | 191.67 | 2300 | 2.4191          |
| 1.769         | 195.83 | 2350 | 2.3897          |
| 1.7677        | 200.0  | 2400 | 2.3729          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "detr-resnet-50_finetuned_bbatch", "results": []}]}
transformers pytorch tensorboard detr object-detection generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

detr-resnet-50\_finetuned\_bbatch
=================================


This model is a fine-tuned version of facebook/detr-resnet-50 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.3729


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: 1e-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: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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: linear
* num\_epochs: 200
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3