Instructions to use karim155/convnext-tiny-224-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use karim155/convnext-tiny-224-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="karim155/convnext-tiny-224-finetuned") 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("karim155/convnext-tiny-224-finetuned") model = AutoModelForImageClassification.from_pretrained("karim155/convnext-tiny-224-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned
results: []
convnext-tiny-224-finetuned
This model is a fine-tuned version of facebook/convnext-tiny-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5789
- Logloss: 1.5789
- Accuracy: {'accuracy': 0.275}
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 | Logloss | Accuracy |
|---|---|---|---|---|---|
| No log | 0.8 | 1 | 1.6327 | 1.6327 | {'accuracy': 0.225} |
| No log | 1.6 | 2 | 1.5966 | 1.5966 | {'accuracy': 0.275} |
| No log | 2.4 | 3 | 1.5789 | 1.5789 | {'accuracy': 0.275} |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1