File size: 1,416 Bytes
489fedc d05a9b0 2fe3f14 129b95e 2fe3f14 3ce49a3 2fe3f14 de8702e b9e62dc 2fe3f14 d05a9b0 |
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 |
---
metrics:
- accuracy
library_name: transformers
tags:
- vit
- image
- classification
- health
- cancer
datasets:
- emre570/breastcancer-ultrasound-images
language:
- en
---
This Vision Transformer model is a fine-tuned version of Google's "vit-large-patch16-224" model.
This model has been fine-tuned with a custom dataset as a finishing project for an academic study.
The aim of the project is to develop a model that achieves high consistency with a limited amount of data. The study uses a dataset consisting of breast cancer images of varying resolutions.
The dataset contains 780 MRI images with a total of 3 classes (benign, malignant, normal), separated into train and test.
**Distributions of images:**
**train:**
- benign: 350
- malignant: 168
- normal: 106
**test:**
- benign: 87
- malignant: 42
- normal: 27
Since the size of the images varies, the images were scaled down to the resolution specified by Google for the model (224x224) and given to the model for fine-tuning.
**Arguments used in fine-tuning:**
```py
trainArgs = TrainingArguments(
save_strategy="epoch",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=10,
per_device_eval_batch_size=4,
num_train_epochs=40,
weight_decay=0.01,
load_best_model_at_end=True,
metric_for_best_model="accuracy",
logging_dir='logs',
remove_unused_columns=False,
)
``` |