# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("tcvrishank/histo_train")
model = AutoModelForImageClassification.from_pretrained("tcvrishank/histo_train")Quick Links
histo_train
This model is a fine-tuned version of google/vit-base-patch16-224 on the image_folder dataset.
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: 0.001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
- Downloads last month
- 4
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tcvrishank/histo_train") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")