# Load model directly
from transformers import AutoImageProcessor, AutoModel
processor = AutoImageProcessor.from_pretrained("fullstuck/transformers_resnet18_cifar100")
model = AutoModel.from_pretrained("fullstuck/transformers_resnet18_cifar100", device_map="auto")Quick Links
transformers_resnet18_cifar100
This model is a fine-tuned version of microsoft/resnet-18 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: 5e-05
- 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: 3.0
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.2
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Model tree for fullstuck/transformers_resnet18_cifar100
Base model
microsoft/resnet-18
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="fullstuck/transformers_resnet18_cifar100")