metadata
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: deit-base-distilled-patch16-224-hasta-55-fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6111111111111112
deit-base-distilled-patch16-224-hasta-55-fold2
This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0385
- Accuracy: 0.6111
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.5714 | 1 | 1.3241 | 0.3611 |
No log | 1.7143 | 3 | 1.1598 | 0.3056 |
No log | 2.8571 | 5 | 1.1928 | 0.3889 |
No log | 4.0 | 7 | 1.1229 | 0.3333 |
No log | 4.5714 | 8 | 1.1111 | 0.3333 |
1.13 | 5.7143 | 10 | 1.1062 | 0.3611 |
1.13 | 6.8571 | 12 | 1.0655 | 0.4722 |
1.13 | 8.0 | 14 | 1.0527 | 0.3889 |
1.13 | 8.5714 | 15 | 1.0448 | 0.4444 |
1.13 | 9.7143 | 17 | 0.9955 | 0.5556 |
1.13 | 10.8571 | 19 | 0.9819 | 0.5556 |
0.9629 | 12.0 | 21 | 0.9722 | 0.5 |
0.9629 | 12.5714 | 22 | 0.9777 | 0.4444 |
0.9629 | 13.7143 | 24 | 0.9126 | 0.5556 |
0.9629 | 14.8571 | 26 | 0.9932 | 0.5556 |
0.9629 | 16.0 | 28 | 0.9946 | 0.5833 |
0.9629 | 16.5714 | 29 | 0.9690 | 0.5556 |
0.822 | 17.7143 | 31 | 0.9163 | 0.5833 |
0.822 | 18.8571 | 33 | 0.9799 | 0.6389 |
0.822 | 20.0 | 35 | 0.9273 | 0.6389 |
0.822 | 20.5714 | 36 | 0.9008 | 0.6389 |
0.822 | 21.7143 | 38 | 1.0305 | 0.5 |
0.7066 | 22.8571 | 40 | 0.9330 | 0.5833 |
0.7066 | 24.0 | 42 | 0.9151 | 0.5833 |
0.7066 | 24.5714 | 43 | 0.9214 | 0.6111 |
0.7066 | 25.7143 | 45 | 0.9322 | 0.6111 |
0.7066 | 26.8571 | 47 | 1.0534 | 0.5833 |
0.7066 | 28.0 | 49 | 1.1223 | 0.5556 |
0.5702 | 28.5714 | 50 | 1.0081 | 0.5833 |
0.5702 | 29.7143 | 52 | 0.8680 | 0.6389 |
0.5702 | 30.8571 | 54 | 0.9259 | 0.6111 |
0.5702 | 32.0 | 56 | 0.9936 | 0.6111 |
0.5702 | 32.5714 | 57 | 0.9762 | 0.6111 |
0.5702 | 33.7143 | 59 | 0.9298 | 0.6111 |
0.4903 | 34.8571 | 61 | 0.9352 | 0.6111 |
0.4903 | 36.0 | 63 | 0.9919 | 0.5833 |
0.4903 | 36.5714 | 64 | 0.9661 | 0.5833 |
0.4903 | 37.7143 | 66 | 0.9764 | 0.6389 |
0.4903 | 38.8571 | 68 | 0.9909 | 0.6389 |
0.3959 | 40.0 | 70 | 0.9906 | 0.6389 |
0.3959 | 40.5714 | 71 | 0.9789 | 0.6389 |
0.3959 | 41.7143 | 73 | 0.9529 | 0.6389 |
0.3959 | 42.8571 | 75 | 0.9729 | 0.6389 |
0.3959 | 44.0 | 77 | 1.0425 | 0.6111 |
0.3959 | 44.5714 | 78 | 1.0531 | 0.6111 |
0.3792 | 45.7143 | 80 | 0.9800 | 0.5833 |
0.3792 | 46.8571 | 82 | 0.9609 | 0.5833 |
0.3792 | 48.0 | 84 | 0.9820 | 0.5833 |
0.3792 | 48.5714 | 85 | 1.0252 | 0.6389 |
0.3792 | 49.7143 | 87 | 1.0592 | 0.6111 |
0.3792 | 50.8571 | 89 | 1.0232 | 0.6667 |
0.3267 | 52.0 | 91 | 0.9972 | 0.6111 |
0.3267 | 52.5714 | 92 | 0.9925 | 0.6111 |
0.3267 | 53.7143 | 94 | 1.0007 | 0.5833 |
0.3267 | 54.8571 | 96 | 1.0169 | 0.5833 |
0.3267 | 56.0 | 98 | 1.0304 | 0.5833 |
0.3267 | 56.5714 | 99 | 1.0360 | 0.6111 |
0.2959 | 57.1429 | 100 | 1.0385 | 0.6111 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1