# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("HedronCreeper/results")
model = AutoModelForImageClassification.from_pretrained("HedronCreeper/results")Quick Links
results
This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the imagefolder 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
- Downloads last month
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Model tree for HedronCreeper/results
Base model
google/mobilenet_v2_1.0_224
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HedronCreeper/results") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")