Instructions to use mruby/convnext-large-224-attempt-2253 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mruby/convnext-large-224-attempt-2253 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mruby/convnext-large-224-attempt-2253") 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("mruby/convnext-large-224-attempt-2253") model = AutoModelForImageClassification.from_pretrained("mruby/convnext-large-224-attempt-2253") - Notebooks
- Google Colab
- Kaggle
convnext-large-224-attempt-2253
This model is a fine-tuned version of facebook/convnext-large-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1280
- Accuracy: 0.9493
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4734 | 0.99 | 33 | 0.3361 | 0.9302 |
| 0.1529 | 1.98 | 66 | 0.1393 | 0.9450 |
| 0.1179 | 2.98 | 99 | 0.1280 | 0.9493 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
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