# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("majorSeaweed/SWIN_BASE_PRETRAINED")
model = AutoModelForImageClassification.from_pretrained("majorSeaweed/SWIN_BASE_PRETRAINED")Quick Links
SWIN_BASE_PRETRAINED
This model was trained from scratch on an unknown 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: 8e-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
- num_epochs: 10
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="majorSeaweed/SWIN_BASE_PRETRAINED") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")