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# conditional-detr-resnet-50_fine_tuned_beyond_words

This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the loc_beyond_words dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892

## 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: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.674         | 0.28  | 100   | 1.7571          |
| 1.4721        | 0.56  | 200   | 1.2737          |
| 1.2557        | 0.84  | 300   | 1.1037          |
| 1.0781        | 1.12  | 400   | 1.0184          |
| 1.0353        | 1.4   | 500   | 0.9988          |
| 1.0324        | 1.69  | 600   | 0.9951          |
| 0.9131        | 1.97  | 700   | 0.9224          |
| 0.8724        | 2.25  | 800   | 0.9692          |
| 0.8129        | 2.53  | 900   | 0.8670          |
| 0.9           | 2.81  | 1000  | 0.8326          |
| 0.7993        | 3.09  | 1100  | 0.7875          |
| 0.7907        | 3.37  | 1200  | 0.7517          |
| 0.8424        | 3.65  | 1300  | 0.9088          |
| 0.7808        | 3.93  | 1400  | 0.8506          |
| 0.7469        | 4.21  | 1500  | 0.7928          |
| 0.7582        | 4.49  | 1600  | 0.7228          |
| 0.7546        | 4.78  | 1700  | 0.7588          |
| 0.7842        | 5.06  | 1800  | 0.7726          |
| 0.775         | 5.34  | 1900  | 0.7676          |
| 0.7263        | 5.62  | 2000  | 0.7164          |
| 0.7209        | 5.9   | 2100  | 0.7061          |
| 0.7259        | 6.18  | 2200  | 0.7579          |
| 0.7701        | 6.46  | 2300  | 0.8184          |
| 0.7391        | 6.74  | 2400  | 0.6684          |
| 0.6834        | 7.02  | 2500  | 0.7042          |
| 0.7098        | 7.3   | 2600  | 0.7166          |
| 0.7498        | 7.58  | 2700  | 0.6752          |
| 0.7056        | 7.87  | 2800  | 0.7064          |
| 0.7004        | 8.15  | 2900  | 0.7090          |
| 0.6964        | 8.43  | 3000  | 0.7318          |
| 0.682         | 8.71  | 3100  | 0.7216          |
| 0.7309        | 8.99  | 3200  | 0.6545          |
| 0.6576        | 9.27  | 3300  | 0.6478          |
| 0.7014        | 9.55  | 3400  | 0.6814          |
| 0.673         | 9.83  | 3500  | 0.6783          |
| 0.6455        | 10.11 | 3600  | 0.7248          |
| 0.7041        | 10.39 | 3700  | 0.7729          |
| 0.6664        | 10.67 | 3800  | 0.6746          |
| 0.6161        | 10.96 | 3900  | 0.6414          |
| 0.6975        | 11.24 | 4000  | 0.6637          |
| 0.6751        | 11.52 | 4100  | 0.6570          |
| 0.6092        | 11.8  | 4200  | 0.6691          |
| 0.6593        | 12.08 | 4300  | 0.6276          |
| 0.6449        | 12.36 | 4400  | 0.6388          |
| 0.6136        | 12.64 | 4500  | 0.6711          |
| 0.6521        | 12.92 | 4600  | 0.6768          |
| 0.6162        | 13.2  | 4700  | 0.6427          |
| 0.7083        | 13.48 | 4800  | 0.6492          |
| 0.6407        | 13.76 | 4900  | 0.6213          |
| 0.6371        | 14.04 | 5000  | 0.6674          |
| 0.626         | 14.33 | 5100  | 0.6185          |
| 0.6442        | 14.61 | 5200  | 0.7180          |
| 0.5981        | 14.89 | 5300  | 0.6441          |
| 0.629         | 15.17 | 5400  | 0.6262          |
| 0.625         | 15.45 | 5500  | 0.6397          |
| 0.6123        | 15.73 | 5600  | 0.6440          |
| 0.6084        | 16.01 | 5700  | 0.6493          |
| 0.6021        | 16.29 | 5800  | 0.6263          |
| 0.6502        | 16.57 | 5900  | 0.6254          |
| 0.6339        | 16.85 | 6000  | 0.7043          |
| 0.5925        | 17.13 | 6100  | 0.8014          |
| 0.6453        | 17.42 | 6200  | 0.6385          |
| 0.6143        | 17.7  | 6300  | 0.6033          |
| 0.6057        | 17.98 | 6400  | 0.6881          |
| 0.6386        | 18.26 | 6500  | 0.6366          |
| 0.5839        | 18.54 | 6600  | 0.6563          |
| 0.6013        | 18.82 | 6700  | 0.5982          |
| 0.5999        | 19.1  | 6800  | 0.6064          |
| 0.6023        | 19.38 | 6900  | 0.5795          |
| 0.5593        | 19.66 | 7000  | 0.6538          |
| 0.6375        | 19.94 | 7100  | 0.6991          |
| 0.6073        | 20.22 | 7200  | 0.7117          |
| 0.596         | 20.51 | 7300  | 0.6034          |
| 0.5987        | 20.79 | 7400  | 0.6489          |
| 0.5922        | 21.07 | 7500  | 0.6216          |
| 0.589         | 21.35 | 7600  | 0.6257          |
| 0.6047        | 21.63 | 7700  | 0.6415          |
| 0.5775        | 21.91 | 7800  | 0.6159          |
| 0.588         | 22.19 | 7900  | 0.6095          |
| 0.5844        | 22.47 | 8000  | 0.6373          |
| 0.5964        | 22.75 | 8100  | 0.6022          |
| 0.5987        | 23.03 | 8200  | 0.6050          |
| 0.5605        | 23.31 | 8300  | 0.6083          |
| 0.5835        | 23.6  | 8400  | 0.7823          |
| 0.5816        | 23.88 | 8500  | 0.6417          |
| 0.5757        | 24.16 | 8600  | 0.6324          |
| 0.5997        | 24.44 | 8700  | 0.6046          |
| 0.5674        | 24.72 | 8800  | 0.6558          |
| 0.5703        | 25.0  | 8900  | 0.5819          |
| 0.5766        | 25.28 | 9000  | 0.6116          |
| 0.5548        | 25.56 | 9100  | 0.5877          |
| 0.564         | 25.84 | 9200  | 0.5672          |
| 0.548         | 26.12 | 9300  | 0.6073          |
| 0.5436        | 26.4  | 9400  | 0.5739          |
| 0.6006        | 26.69 | 9500  | 0.6101          |
| 0.5519        | 26.97 | 9600  | 0.5869          |
| 0.5432        | 27.25 | 9700  | 0.5721          |
| 0.5597        | 27.53 | 9800  | 0.5807          |
| 0.5254        | 27.81 | 9900  | 0.5849          |
| 0.5366        | 28.09 | 10000 | 0.5831          |
| 0.5654        | 28.37 | 10100 | 0.5993          |
| 0.57          | 28.65 | 10200 | 0.5892          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["biglam/loc_beyond_words"], "base_model": "microsoft/conditional-detr-resnet-50", "model-index": [{"name": "conditional-detr-resnet-50_fine_tuned_beyond_words", "results": []}]}
transformers pytorch tensorboard conditional_detr object-detection generated_from_trainer dataset:biglam/loc_beyond_words base_model:microsoft/conditional-detr-resnet-50 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #conditional_detr #object-detection #generated_from_trainer #dataset-biglam/loc_beyond_words #base_model-microsoft/conditional-detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us 

conditional-detr-resnet-50\_fine\_tuned\_beyond\_words
======================================================


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


* Loss: 0.5892


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: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### 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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #conditional_detr #object-detection #generated_from_trainer #dataset-biglam/loc_beyond_words #base_model-microsoft/conditional-detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #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: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #conditional_detr #object-detection #generated_from_trainer #dataset-biglam/loc_beyond_words #base_model-microsoft/conditional-detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #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: 200
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.0+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2