Instructions to use julianz1/axis-inference-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use julianz1/axis-inference-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="julianz1/axis-inference-v0") 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("julianz1/axis-inference-v0") model = AutoModelForImageClassification.from_pretrained("julianz1/axis-inference-v0") - Notebooks
- Google Colab
- Kaggle
axis-inference-v0
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7092
- Accuracy: 0.5243
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.6101 | 0.94 | 12 | 0.9202 | 0.4701 |
| 0.8441 | 1.96 | 25 | 0.7214 | 0.5410 |
| 0.7249 | 2.98 | 38 | 0.7014 | 0.5131 |
| 0.6997 | 3.76 | 48 | 0.7092 | 0.5243 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
- 5
Model tree for julianz1/axis-inference-v0
Base model
facebook/convnextv2-tiny-1k-224