# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("sooks/idbwtiny")
model = AutoModelForImageClassification.from_pretrained("sooks/idbwtiny")Quick Links
idbwtiny
This model is a fine-tuned version of facebook/convnext-tiny-224 on the sooks/id2 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0155
- eval_accuracy: 0.9953
- eval_runtime: 301.1388
- eval_samples_per_second: 178.967
- eval_steps_per_second: 22.372
- epoch: 6.37
- step: 12963
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: 0.0002
- train_batch_size: 150
- 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
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
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Model tree for sooks/idbwtiny
Base model
facebook/convnext-tiny-224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sooks/idbwtiny") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")