Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ChasingMercer/beit-base")
model = AutoModelForImageClassification.from_pretrained("ChasingMercer/beit-base")Quick Links
beit-base
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the cats_vs_dogs dataset. It achieves the following results on the evaluation set:
- Loss: 0.0116
- Accuracy: 0.9977
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0303 | 1.0 | 585 | 0.0186 | 0.9942 |
| 0.0374 | 2.0 | 1170 | 0.0150 | 0.9955 |
| 0.0559 | 3.0 | 1755 | 0.0116 | 0.9977 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Downloads last month
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Dataset used to train ChasingMercer/beit-base
Evaluation results
- Accuracy on cats_vs_dogsself-reported0.998
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ChasingMercer/beit-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")