File size: 2,776 Bytes
d7bc196 447e614 d7bc196 447e614 d7bc196 447e614 d7bc196 447e614 d7bc196 447e614 d7bc196 447e614 d7bc196 447e614 d7bc196 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
library_name: transformers
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: deit-tiny-patch16-224-finetuned-papsmear
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: 0.8235294117647058
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deit-tiny-patch16-224-finetuned-papsmear
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- Accuracy: 0.8235
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5381 | 0.9935 | 38 | 1.4222 | 0.3897 |
| 1.172 | 1.9869 | 76 | 1.1008 | 0.5882 |
| 0.8361 | 2.9804 | 114 | 0.8529 | 0.6618 |
| 0.6869 | 4.0 | 153 | 0.9582 | 0.6324 |
| 0.4995 | 4.9935 | 191 | 0.6926 | 0.7574 |
| 0.4576 | 5.9869 | 229 | 0.4967 | 0.8529 |
| 0.4187 | 6.9804 | 267 | 0.5350 | 0.8162 |
| 0.4075 | 8.0 | 306 | 0.4903 | 0.8088 |
| 0.3585 | 8.9935 | 344 | 0.5252 | 0.7868 |
| 0.3528 | 9.9869 | 382 | 0.5027 | 0.8088 |
| 0.2788 | 10.9804 | 420 | 0.4503 | 0.8456 |
| 0.2419 | 12.0 | 459 | 0.4857 | 0.8309 |
| 0.2544 | 12.9935 | 497 | 0.5543 | 0.7868 |
| 0.2591 | 13.9869 | 535 | 0.4839 | 0.8382 |
| 0.207 | 14.9020 | 570 | 0.4747 | 0.8235 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|