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HumaP/vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt

sourceHugging Faceupdated 3y agoView on Hugging Face

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Model Card

Description

Google ViT model is finetuned on lung and colon histopathology image classification dataset. The dataset is available on Kaggle.

About Dataset

It is a multiclass image classification dataset. This dataset contains 25,000 histopathological images with 5 classes.

There are five classes in the dataset, each with 5,000 images, being:

—Lung benign tissue
—Lung adenocarcinoma
—Lung squamous cell carcinoma
—Colon adenocarcinoma
—Colon benign tissue

Training Results

Epoch	Training Loss	Validation Loss	Validation Accuracy
1	0.075800	0.029827	0.994600
2	0.039400	0.008961	0.998800
3	0.017400	0.005077	0.999200

How to use

python

from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image

imageurl = 'test images/lung01.jpg'
image = Image.open(imageurl)

processor = ViTImageProcessor.from_pretrained('HumaP/vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt')
model = ViTForImageClassification.from_pretrained('HumaP/vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt')

inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])