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# efficientnet-b0-accidents

This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3330
- Accuracy: 0.8367

## 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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.4780          | 0.7959   |
| No log        | 2.0   | 2    | 0.4610          | 0.7959   |
| No log        | 3.0   | 3    | 0.4518          | 0.7755   |
| No log        | 4.0   | 4    | 0.4450          | 0.8163   |
| No log        | 5.0   | 5    | 0.4424          | 0.8265   |
| No log        | 6.0   | 6    | 0.4483          | 0.7959   |
| No log        | 7.0   | 7    | 0.4533          | 0.7959   |
| No log        | 8.0   | 8    | 0.4557          | 0.7959   |
| No log        | 9.0   | 9    | 0.4556          | 0.8265   |
| 0.4528        | 10.0  | 10   | 0.4453          | 0.8163   |
| 0.4528        | 11.0  | 11   | 0.4558          | 0.7755   |
| 0.4528        | 12.0  | 12   | 0.4390          | 0.8265   |
| 0.4528        | 13.0  | 13   | 0.4322          | 0.7959   |
| 0.4528        | 14.0  | 14   | 0.4323          | 0.8163   |
| 0.4528        | 15.0  | 15   | 0.4127          | 0.8061   |
| 0.4528        | 16.0  | 16   | 0.4341          | 0.8061   |
| 0.4528        | 17.0  | 17   | 0.4144          | 0.8265   |
| 0.4528        | 18.0  | 18   | 0.4275          | 0.8265   |
| 0.4528        | 19.0  | 19   | 0.3988          | 0.8673   |
| 0.4233        | 20.0  | 20   | 0.4210          | 0.7959   |
| 0.4233        | 21.0  | 21   | 0.4223          | 0.7755   |
| 0.4233        | 22.0  | 22   | 0.4288          | 0.8265   |
| 0.4233        | 23.0  | 23   | 0.3851          | 0.8571   |
| 0.4233        | 24.0  | 24   | 0.3956          | 0.8061   |
| 0.4233        | 25.0  | 25   | 0.4159          | 0.8367   |
| 0.4233        | 26.0  | 26   | 0.4055          | 0.8163   |
| 0.4233        | 27.0  | 27   | 0.3861          | 0.8163   |
| 0.4233        | 28.0  | 28   | 0.3751          | 0.8469   |
| 0.4233        | 29.0  | 29   | 0.3915          | 0.8367   |
| 0.3846        | 30.0  | 30   | 0.3705          | 0.8571   |
| 0.3846        | 31.0  | 31   | 0.3868          | 0.8367   |
| 0.3846        | 32.0  | 32   | 0.3710          | 0.8469   |
| 0.3846        | 33.0  | 33   | 0.3770          | 0.8469   |
| 0.3846        | 34.0  | 34   | 0.3903          | 0.8265   |
| 0.3846        | 35.0  | 35   | 0.3864          | 0.8469   |
| 0.3846        | 36.0  | 36   | 0.3728          | 0.8265   |
| 0.3846        | 37.0  | 37   | 0.3772          | 0.8367   |
| 0.3846        | 38.0  | 38   | 0.3633          | 0.8163   |
| 0.3846        | 39.0  | 39   | 0.3824          | 0.8469   |
| 0.3714        | 40.0  | 40   | 0.3520          | 0.8571   |
| 0.3714        | 41.0  | 41   | 0.3844          | 0.8469   |
| 0.3714        | 42.0  | 42   | 0.3564          | 0.8469   |
| 0.3714        | 43.0  | 43   | 0.3747          | 0.8673   |
| 0.3714        | 44.0  | 44   | 0.3395          | 0.8571   |
| 0.3714        | 45.0  | 45   | 0.3871          | 0.8163   |
| 0.3714        | 46.0  | 46   | 0.3487          | 0.8367   |
| 0.3714        | 47.0  | 47   | 0.3798          | 0.8163   |
| 0.3714        | 48.0  | 48   | 0.3848          | 0.8367   |
| 0.3714        | 49.0  | 49   | 0.3978          | 0.8265   |
| 0.3618        | 50.0  | 50   | 0.3384          | 0.8571   |
| 0.3618        | 51.0  | 51   | 0.3647          | 0.8265   |
| 0.3618        | 52.0  | 52   | 0.3544          | 0.8571   |
| 0.3618        | 53.0  | 53   | 0.4289          | 0.8163   |
| 0.3618        | 54.0  | 54   | 0.3568          | 0.8673   |
| 0.3618        | 55.0  | 55   | 0.3727          | 0.8673   |
| 0.3618        | 56.0  | 56   | 0.3796          | 0.8265   |
| 0.3618        | 57.0  | 57   | 0.3678          | 0.8571   |
| 0.3618        | 58.0  | 58   | 0.3719          | 0.8469   |
| 0.3618        | 59.0  | 59   | 0.3808          | 0.8878   |
| 0.327         | 60.0  | 60   | 0.3783          | 0.8163   |
| 0.327         | 61.0  | 61   | 0.3637          | 0.8367   |
| 0.327         | 62.0  | 62   | 0.3743          | 0.8367   |
| 0.327         | 63.0  | 63   | 0.3554          | 0.8571   |
| 0.327         | 64.0  | 64   | 0.3544          | 0.8265   |
| 0.327         | 65.0  | 65   | 0.3615          | 0.8469   |
| 0.327         | 66.0  | 66   | 0.3503          | 0.8673   |
| 0.327         | 67.0  | 67   | 0.3914          | 0.7959   |
| 0.327         | 68.0  | 68   | 0.3687          | 0.8367   |
| 0.327         | 69.0  | 69   | 0.3296          | 0.8878   |
| 0.3136        | 70.0  | 70   | 0.3548          | 0.8571   |
| 0.3136        | 71.0  | 71   | 0.3810          | 0.8265   |
| 0.3136        | 72.0  | 72   | 0.3522          | 0.8469   |
| 0.3136        | 73.0  | 73   | 0.3852          | 0.8367   |
| 0.3136        | 74.0  | 74   | 0.3434          | 0.8571   |
| 0.3136        | 75.0  | 75   | 0.3596          | 0.8571   |
| 0.3136        | 76.0  | 76   | 0.3551          | 0.8367   |
| 0.3136        | 77.0  | 77   | 0.4257          | 0.8163   |
| 0.3136        | 78.0  | 78   | 0.3554          | 0.8367   |
| 0.3136        | 79.0  | 79   | 0.3352          | 0.8265   |
| 0.316         | 80.0  | 80   | 0.3773          | 0.8367   |
| 0.316         | 81.0  | 81   | 0.3305          | 0.8469   |
| 0.316         | 82.0  | 82   | 0.3614          | 0.8571   |
| 0.316         | 83.0  | 83   | 0.3491          | 0.8265   |
| 0.316         | 84.0  | 84   | 0.3479          | 0.8571   |
| 0.316         | 85.0  | 85   | 0.3684          | 0.8367   |
| 0.316         | 86.0  | 86   | 0.3511          | 0.8571   |
| 0.316         | 87.0  | 87   | 0.3658          | 0.8265   |
| 0.316         | 88.0  | 88   | 0.3333          | 0.8367   |
| 0.316         | 89.0  | 89   | 0.3584          | 0.8776   |
| 0.3089        | 90.0  | 90   | 0.3277          | 0.8571   |
| 0.3089        | 91.0  | 91   | 0.3875          | 0.8367   |
| 0.3089        | 92.0  | 92   | 0.3757          | 0.8367   |
| 0.3089        | 93.0  | 93   | 0.3488          | 0.8367   |
| 0.3089        | 94.0  | 94   | 0.3282          | 0.8571   |
| 0.3089        | 95.0  | 95   | 0.3613          | 0.8571   |
| 0.3089        | 96.0  | 96   | 0.3753          | 0.8469   |
| 0.3089        | 97.0  | 97   | 0.3625          | 0.8469   |
| 0.3089        | 98.0  | 98   | 0.3930          | 0.8265   |
| 0.3089        | 99.0  | 99   | 0.3338          | 0.8469   |
| 0.3131        | 100.0 | 100  | 0.3330          | 0.8367   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

accident non accident