metadata

library_name: transformers
license: apache-2.0
inference: true
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: plant_disease_detection(vriskharakshak)
    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: 0.9811046511627907

plant_disease_detection(vriksharakshak)

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.0880
Accuracy: 0.9811

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

use this model

from transformers import pipeline
from PIL import Image
import requests

# Load the image classification pipeline with a specific model
pipe = pipeline("image-classification", "ozair23/swin-tiny-patch4-window7-224-finetuned-plantdisease")

# Load the image from a URL
url = 'https://huggingface.co./nielsr/convnext-tiny-finetuned-eurostat/resolve/main/forest.png'
image = Image.open(requests.get(url, stream=True).raw)

# Classify the image
results = pipe(image)

# Display the results
print("Predictions:")
for result in results:
    print(f"Label: {result['label']}, Score: {result['score']:.4f}")

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1968	0.9983	145	0.0880	0.9811

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1