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
Model save
Browse files
README.md
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Logloss: 1.
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- Accuracy: {'accuracy': 0.
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------------------------------:|
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| No log | 0.9412 | 8 | 1.
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### Framework versions
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This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1657
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- Logloss: 1.1657
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- Accuracy: {'accuracy': 0.5808823529411765}
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## Model description
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:-------:|:---------------------------------:|
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| No log | 0.9412 | 8 | 1.6072 | 1.6072 | {'accuracy': 0.18382352941176472} |
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| 1.6101 | 2.0 | 17 | 1.5668 | 1.5668 | {'accuracy': 0.31985294117647056} |
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| 1.5645 | 2.9412 | 25 | 1.5246 | 1.5246 | {'accuracy': 0.33455882352941174} |
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| 1.4902 | 4.0 | 34 | 1.4774 | 1.4774 | {'accuracy': 0.4007352941176471} |
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| 1.4243 | 4.9412 | 42 | 1.4283 | 1.4283 | {'accuracy': 0.44485294117647056} |
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| 1.3502 | 6.0 | 51 | 1.3747 | 1.3747 | {'accuracy': 0.48161764705882354} |
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| 1.3502 | 6.9412 | 59 | 1.3332 | 1.3332 | {'accuracy': 0.48161764705882354} |
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| 1.2906 | 8.0 | 68 | 1.2978 | 1.2978 | {'accuracy': 0.5036764705882353} |
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| 1.2371 | 8.9412 | 76 | 1.2702 | 1.2702 | {'accuracy': 0.5147058823529411} |
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| 1.1856 | 10.0 | 85 | 1.2434 | 1.2434 | {'accuracy': 0.5404411764705882} |
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| 1.1506 | 10.9412 | 93 | 1.2300 | 1.2300 | {'accuracy': 0.5477941176470589} |
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| 1.0987 | 12.0 | 102 | 1.2088 | 1.2088 | {'accuracy': 0.5588235294117647} |
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| 1.0758 | 12.9412 | 110 | 1.1949 | 1.1949 | {'accuracy': 0.5514705882352942} |
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| 1.0758 | 14.0 | 119 | 1.1896 | 1.1896 | {'accuracy': 0.5588235294117647} |
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| 1.0483 | 14.9412 | 127 | 1.1773 | 1.1773 | {'accuracy': 0.5698529411764706} |
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| 1.0346 | 16.0 | 136 | 1.1719 | 1.1719 | {'accuracy': 0.5735294117647058} |
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| 1.0215 | 16.9412 | 144 | 1.1702 | 1.1702 | {'accuracy': 0.5698529411764706} |
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| 1.0177 | 18.0 | 153 | 1.1666 | 1.1666 | {'accuracy': 0.5772058823529411} |
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| 0.9956 | 18.8235 | 160 | 1.1657 | 1.1657 | {'accuracy': 0.5808823529411765} |
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### Framework versions
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runs/Sep04_15-28-02_0ea876ca526a/events.out.tfevents.1725463694.0ea876ca526a.551.0
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