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# detr-resnet-50_fine_tuned_loc-2023

This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the loc_beyond_words dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8784

## 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: 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: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.731         | 0.16  | 50   | 2.6356          |
| 2.4875        | 0.31  | 100  | 2.2348          |
| 2.1786        | 0.47  | 150  | 2.1148          |
| 1.9845        | 0.62  | 200  | 1.8847          |
| 1.8507        | 0.78  | 250  | 1.8331          |
| 1.6813        | 0.94  | 300  | 1.5620          |
| 1.5613        | 1.09  | 350  | 1.5898          |
| 1.4966        | 1.25  | 400  | 1.4161          |
| 1.4831        | 1.41  | 450  | 1.4831          |
| 1.4587        | 1.56  | 500  | 1.3218          |
| 1.433         | 1.72  | 550  | 1.3529          |
| 1.33          | 1.88  | 600  | 1.2453          |
| 1.2842        | 2.03  | 650  | 1.2956          |
| 1.2807        | 2.19  | 700  | 1.1993          |
| 1.1767        | 2.34  | 750  | 1.1557          |
| 1.2134        | 2.5   | 800  | 1.1393          |
| 1.1897        | 2.66  | 850  | 1.2016          |
| 1.1784        | 2.81  | 900  | 1.1235          |
| 1.2016        | 2.97  | 950  | 1.1378          |
| 1.06          | 3.12  | 1000 | 1.0803          |
| 1.1124        | 3.28  | 1050 | 1.1145          |
| 1.1191        | 3.44  | 1100 | 1.0523          |
| 1.0819        | 3.59  | 1150 | 1.0165          |
| 1.1196        | 3.75  | 1200 | 1.0349          |
| 1.0534        | 3.91  | 1250 | 1.0441          |
| 1.0365        | 4.06  | 1300 | 1.1177          |
| 0.9853        | 4.22  | 1350 | 1.0721          |
| 0.9984        | 4.38  | 1400 | 0.9923          |
| 0.9802        | 4.53  | 1450 | 1.0079          |
| 1.04          | 4.69  | 1500 | 1.0198          |
| 1.098         | 4.84  | 1550 | 0.9788          |
| 1.079         | 5.0   | 1600 | 1.0291          |
| 1.0664        | 5.16  | 1650 | 0.9691          |
| 0.9715        | 5.31  | 1700 | 0.9380          |
| 0.9723        | 5.47  | 1750 | 1.0164          |
| 1.0019        | 5.62  | 1800 | 1.0064          |
| 0.9895        | 5.78  | 1850 | 1.0364          |
| 0.9835        | 5.94  | 1900 | 0.9848          |
| 0.994         | 6.09  | 1950 | 0.9353          |
| 0.9693        | 6.25  | 2000 | 0.9425          |
| 0.9413        | 6.41  | 2050 | 0.9173          |
| 0.9375        | 6.56  | 2100 | 0.9663          |
| 0.952         | 6.72  | 2150 | 0.8951          |
| 0.8927        | 6.88  | 2200 | 0.9099          |
| 0.8777        | 7.03  | 2250 | 0.9238          |
| 0.8976        | 7.19  | 2300 | 0.9715          |
| 0.9451        | 7.34  | 2350 | 0.9373          |
| 0.8972        | 7.5   | 2400 | 0.8959          |
| 0.9393        | 7.66  | 2450 | 1.0062          |
| 0.9           | 7.81  | 2500 | 0.8920          |
| 0.915         | 7.97  | 2550 | 0.8833          |
| 0.9018        | 8.12  | 2600 | 0.8671          |
| 0.8272        | 8.28  | 2650 | 0.9304          |
| 0.943         | 8.44  | 2700 | 0.8593          |
| 0.8667        | 8.59  | 2750 | 0.8875          |
| 0.871         | 8.75  | 2800 | 0.8457          |
| 0.9023        | 8.91  | 2850 | 0.8448          |
| 0.8733        | 9.06  | 2900 | 0.8261          |
| 0.8686        | 9.22  | 2950 | 0.8489          |
| 0.8412        | 9.38  | 3000 | 0.8244          |
| 0.8385        | 9.53  | 3050 | 0.8830          |
| 0.891         | 9.69  | 3100 | 0.8349          |
| 0.8692        | 9.84  | 3150 | 0.8672          |
| 0.8247        | 10.0  | 3200 | 0.8811          |
| 0.799         | 10.16 | 3250 | 0.8784          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
{"license": "apache-2.0", "library_name": "transformers", "tags": ["lam", "newspapers"], "datasets": ["biglam/loc_beyond_words"], "pipeline_tag": "object-detection", "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr-resnet-50_fine_tuned_loc-2023", "results": []}]}
transformers pytorch tensorboard detr object-detection lam newspapers dataset:biglam/loc_beyond_words base_model:facebook/detr-resnet-50 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #lam #newspapers #dataset-biglam/loc_beyond_words #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us 

detr-resnet-50\_fine\_tuned\_loc-2023
=====================================


This model is a fine-tuned version of facebook/detr-resnet-50 on the loc\_beyond\_words dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8784


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: 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: 100


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 100 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #lam #newspapers #dataset-biglam/loc_beyond_words #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 100 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #detr #object-detection #lam #newspapers #dataset-biglam/loc_beyond_words #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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: 100### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3