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# test_mae_flysheet

This model is a fine-tuned version of [facebook/vit-mae-base](https://huggingface.co/facebook/vit-mae-base) on the davanstrien/flysheet dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2675

## 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: 3.75e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.284         | 1.0   | 28   | 2.2812          |
| 2.137         | 2.0   | 56   | 2.0288          |
| 1.6016        | 3.0   | 84   | 1.2437          |
| 0.8055        | 4.0   | 112  | 0.7419          |
| 0.5304        | 5.0   | 140  | 0.5151          |
| 0.4873        | 6.0   | 168  | 0.4884          |
| 0.442         | 7.0   | 196  | 0.4441          |
| 0.4039        | 8.0   | 224  | 0.4159          |
| 0.3866        | 9.0   | 252  | 0.3975          |
| 0.391         | 10.0  | 280  | 0.3869          |
| 0.3549        | 11.0  | 308  | 0.3801          |
| 0.3462        | 12.0  | 336  | 0.3577          |
| 0.3402        | 13.0  | 364  | 0.3519          |
| 0.3357        | 14.0  | 392  | 0.3447          |
| 0.3474        | 15.0  | 420  | 0.3369          |
| 0.3254        | 16.0  | 448  | 0.3386          |
| 0.3033        | 17.0  | 476  | 0.3294          |
| 0.3047        | 18.0  | 504  | 0.3274          |
| 0.3103        | 19.0  | 532  | 0.3209          |
| 0.3067        | 20.0  | 560  | 0.3186          |
| 0.2959        | 21.0  | 588  | 0.3190          |
| 0.2899        | 22.0  | 616  | 0.3147          |
| 0.2872        | 23.0  | 644  | 0.3082          |
| 0.2956        | 24.0  | 672  | 0.3070          |
| 0.2865        | 25.0  | 700  | 0.3072          |
| 0.2947        | 26.0  | 728  | 0.3072          |
| 0.2811        | 27.0  | 756  | 0.3131          |
| 0.2935        | 28.0  | 784  | 0.3069          |
| 0.2814        | 29.0  | 812  | 0.3043          |
| 0.2753        | 30.0  | 840  | 0.2984          |
| 0.2823        | 31.0  | 868  | 0.2995          |
| 0.2962        | 32.0  | 896  | 0.3012          |
| 0.2869        | 33.0  | 924  | 0.3050          |
| 0.2833        | 34.0  | 952  | 0.2960          |
| 0.2892        | 35.0  | 980  | 0.3039          |
| 0.2764        | 36.0  | 1008 | 0.3010          |
| 0.2807        | 37.0  | 1036 | 0.2998          |
| 0.2843        | 38.0  | 1064 | 0.2989          |
| 0.2808        | 39.0  | 1092 | 0.2970          |
| 0.2862        | 40.0  | 1120 | 0.2940          |
| 0.2601        | 41.0  | 1148 | 0.2952          |
| 0.2742        | 42.0  | 1176 | 0.2940          |
| 0.2791        | 43.0  | 1204 | 0.2997          |
| 0.2759        | 44.0  | 1232 | 0.2951          |
| 0.2819        | 45.0  | 1260 | 0.2896          |
| 0.287         | 46.0  | 1288 | 0.2938          |
| 0.2711        | 47.0  | 1316 | 0.2973          |
| 0.2782        | 48.0  | 1344 | 0.2946          |
| 0.2674        | 49.0  | 1372 | 0.2913          |
| 0.268         | 50.0  | 1400 | 0.2944          |
| 0.2624        | 51.0  | 1428 | 0.2940          |
| 0.2842        | 52.0  | 1456 | 0.2978          |
| 0.2753        | 53.0  | 1484 | 0.2951          |
| 0.2733        | 54.0  | 1512 | 0.2880          |
| 0.2782        | 55.0  | 1540 | 0.2969          |
| 0.2789        | 56.0  | 1568 | 0.2919          |
| 0.2815        | 57.0  | 1596 | 0.2916          |
| 0.2629        | 58.0  | 1624 | 0.2947          |
| 0.2716        | 59.0  | 1652 | 0.2828          |
| 0.2623        | 60.0  | 1680 | 0.2924          |
| 0.2773        | 61.0  | 1708 | 0.2765          |
| 0.268         | 62.0  | 1736 | 0.2754          |
| 0.2839        | 63.0  | 1764 | 0.2744          |
| 0.2684        | 64.0  | 1792 | 0.2744          |
| 0.2865        | 65.0  | 1820 | 0.2716          |
| 0.2845        | 66.0  | 1848 | 0.2769          |
| 0.2663        | 67.0  | 1876 | 0.2754          |
| 0.269         | 68.0  | 1904 | 0.2737          |
| 0.2681        | 69.0  | 1932 | 0.2697          |
| 0.2748        | 70.0  | 1960 | 0.2779          |
| 0.2769        | 71.0  | 1988 | 0.2728          |
| 0.2805        | 72.0  | 2016 | 0.2729          |
| 0.2771        | 73.0  | 2044 | 0.2728          |
| 0.2717        | 74.0  | 2072 | 0.2749          |
| 0.267         | 75.0  | 2100 | 0.2732          |
| 0.2812        | 76.0  | 2128 | 0.2743          |
| 0.2749        | 77.0  | 2156 | 0.2739          |
| 0.2746        | 78.0  | 2184 | 0.2730          |
| 0.2707        | 79.0  | 2212 | 0.2743          |
| 0.2644        | 80.0  | 2240 | 0.2740          |
| 0.2691        | 81.0  | 2268 | 0.2727          |
| 0.2679        | 82.0  | 2296 | 0.2771          |
| 0.2748        | 83.0  | 2324 | 0.2744          |
| 0.2744        | 84.0  | 2352 | 0.2703          |
| 0.2715        | 85.0  | 2380 | 0.2733          |
| 0.2682        | 86.0  | 2408 | 0.2715          |
| 0.2641        | 87.0  | 2436 | 0.2722          |
| 0.274         | 88.0  | 2464 | 0.2748          |
| 0.2669        | 89.0  | 2492 | 0.2753          |
| 0.2707        | 90.0  | 2520 | 0.2724          |
| 0.2755        | 91.0  | 2548 | 0.2703          |
| 0.2769        | 92.0  | 2576 | 0.2737          |
| 0.2659        | 93.0  | 2604 | 0.2721          |
| 0.2674        | 94.0  | 2632 | 0.2763          |
| 0.2723        | 95.0  | 2660 | 0.2723          |
| 0.2723        | 96.0  | 2688 | 0.2744          |
| 0.272         | 97.0  | 2716 | 0.2686          |
| 0.27          | 98.0  | 2744 | 0.2728          |
| 0.2721        | 99.0  | 2772 | 0.2743          |
| 0.2692        | 100.0 | 2800 | 0.2748          |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["masked-auto-encoding", "generated_from_trainer"], "datasets": ["image_folder"], "base_model": "facebook/vit-mae-base", "model-index": [{"name": "test_mae_flysheet", "results": []}]}
transformers pytorch tensorboard vit_mae pretraining masked-auto-encoding generated_from_trainer dataset:image_folder base_model:facebook/vit-mae-base license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #dataset-image_folder #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us 

test\_mae\_flysheet
===================


This model is a fine-tuned version of facebook/vit-mae-base on the davanstrien/flysheet dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2675


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: 3.75e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.75e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #dataset-image_folder #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.75e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #vit_mae #pretraining #masked-auto-encoding #generated_from_trainer #dataset-image_folder #base_model-facebook/vit-mae-base #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3.75e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 1337
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.05
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6