Instructions to use JOECHAN890/beitv2-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JOECHAN890/beitv2-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JOECHAN890/beitv2-base-beans") 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("JOECHAN890/beitv2-base-beans") model = AutoModelForImageClassification.from_pretrained("JOECHAN890/beitv2-base-beans") - timm
How to use JOECHAN890/beitv2-base-beans with timm:
import timm model = timm.create_model("hf_hub:JOECHAN890/beitv2-base-beans", pretrained=True) - Notebooks
- Google Colab
- Kaggle
beitv2-base-beans
This model is a fine-tuned version of timm/beitv2_base_patch16_224.in1k_ft_in22k on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9232
- Loss: 0.3161
- Num Input Tokens Seen: 0
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: 7.8125e-05
- train_batch_size: 160
- eval_batch_size: 160
- seed: 1337
- 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
- lr_scheduler_warmup_steps: 20360
- num_epochs: 100.0
Training results
Framework versions
- Transformers 4.57.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
- Downloads last month
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Model tree for JOECHAN890/beitv2-base-beans
Base model
timm/beitv2_base_patch16_224.in1k_ft_in22k