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vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt
HumaP/vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt
vit_base_patch16_224_in21k_lung_and_colon_histopathology_pt is a image classification model from HumaP. Use it when you need a label for an image. It is set up for transformers.
Google ViT model is finetuned on lung and colon histopathology image classification dataset. The dataset is available on Kaggle.
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- vit
- Access
- Public
- Created
- Dec 7, 2023
- Updated
- Dec 7, 2023
- SHA
- eab6a53c
Model card
From the Hugging Face model README
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
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])
- Task
- Image Classification
- Library
- transformers
- Type
- vit
- Created
- Dec 7, 2023
- Updated
- Dec 7, 2023