Instructions to use hshankar113/region-id-pred-noCrops-convnext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hshankar113/region-id-pred-noCrops-convnext with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hshankar113/region-id-pred-noCrops-convnext") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hshankar113/region-id-pred-noCrops-convnext") model = AutoModelForImageClassification.from_pretrained("hshankar113/region-id-pred-noCrops-convnext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
region-id-pred-noCrops-convnext
This model is a fine-tuned version of facebook/convnext-large-224-22k-1k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1967
- Accuracy: 0.9485
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
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Model tree for hshankar113/region-id-pred-noCrops-convnext
Base model
facebook/convnext-large-224-22k-1kEvaluation results
- Accuracy on imagefoldervalidation set self-reported0.949