# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("prashanth0205/vit_spectrogram")
model = AutoModelForImageClassification.from_pretrained("prashanth0205/vit_spectrogram")Quick Links
vit_spectrogram
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on a dataset containing images of Mel spectrogram belonging to the classes 'Male' and 'Female'. This model is still being fine tuned and tested. It achieves the following results on the evaluation set:
- Train Loss: 0.2893
- Train Accuracy: 0.8757
- Train Top-3-accuracy: 1.0000
- Validation Loss: 0.8757
- Validation Accuracy: 0.9366
- Validation Top-3-accuracy: 1.0
- Epoch: 1
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:
- optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3032, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
Training results
Framework versions
- Transformers 4.18.0
- TensorFlow 2.4.0
- Datasets 2.0.0
- Tokenizers 0.11.6
- Downloads last month
- 6
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prashanth0205/vit_spectrogram") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")