Image Classification
Transformers
Safetensors
vit
image-classification, screenshots detection
Generated from Trainer
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("al-css/Screenshots_detection_to_classification")
model = AutoModelForImageClassification.from_pretrained("al-css/Screenshots_detection_to_classification")Quick Links
Screenshots_detection_to_classification
This model is a fine-tuned version of google/vit-base-patch16-224 on the private_images_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.1192
- Accuracy: 0.9881
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Framework versions
- Transformers 4.44.1
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for al-css/Screenshots_detection_to_classification
Base model
google/vit-base-patch16-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="al-css/Screenshots_detection_to_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")