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
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