File size: 2,849 Bytes
a162aac |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_08
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: 0.9591516103692066
- name: Precision
type: precision
value: 0.9627515459909033
---
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_08
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210
- Accuracy: 0.9592
- F1 Score: 0.9600
- Precision: 0.9628
## 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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2882 | 0.99 | 19 | 0.5469 | 0.7962 | 0.7863 | 0.8077 |
| 0.3491 | 1.97 | 38 | 0.3030 | 0.8861 | 0.8878 | 0.8981 |
| 0.1791 | 2.96 | 57 | 0.2077 | 0.9211 | 0.9229 | 0.9307 |
| 0.122 | 4.0 | 77 | 0.2007 | 0.9254 | 0.9272 | 0.9369 |
| 0.0671 | 4.99 | 96 | 0.2073 | 0.9269 | 0.9294 | 0.9401 |
| 0.0474 | 5.97 | 115 | 0.1384 | 0.9482 | 0.9494 | 0.9547 |
| 0.032 | 6.96 | 134 | 0.1683 | 0.9430 | 0.9447 | 0.9511 |
| 0.0225 | 8.0 | 154 | 0.1101 | 0.9650 | 0.9657 | 0.9671 |
| 0.0193 | 8.99 | 173 | 0.1372 | 0.9533 | 0.9544 | 0.9585 |
| 0.0193 | 9.87 | 190 | 0.1210 | 0.9592 | 0.9600 | 0.9628 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|