Instructions to use Alimuddin/amazon_fish_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alimuddin/amazon_fish_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Alimuddin/amazon_fish_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Alimuddin/amazon_fish_classification") model = AutoModelForImageClassification.from_pretrained("Alimuddin/amazon_fish_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Alimuddin/amazon_fish_classification: direct link, hf CLI and curl.
- Browser
- Download file 2.58 kB
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https://huggingface.co/Alimuddin/amazon_fish_classification/resolve/d97a0aa2d4da1c4e90f4fc0f43d03c8fe66ebd65/README.md
- Command line
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hf download hf://Alimuddin/amazon_fish_classification@d97a0aa2d4da1c4e90f4fc0f43d03c8fe66ebd65/README.md
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curl -L -o README.md https://huggingface.co/Alimuddin/amazon_fish_classification/resolve/d97a0aa2d4da1c4e90f4fc0f43d03c8fe66ebd65/README.md
2.58 kB
| license: apache-2.0 | |
| base_model: google/vit-base-patch16-224-in21k | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - imagefolder | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: image_classification | |
| results: | |
| - task: | |
| name: Image Classification | |
| type: image-classification | |
| dataset: | |
| name: imagefolder | |
| type: imagefolder | |
| config: default | |
| split: train | |
| args: default | |
| metrics: | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.4375 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # image_classification | |
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.5566 | |
| - Accuracy: 0.4375 | |
| ## 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: 1e-05 | |
| - train_batch_size: 15 | |
| - eval_batch_size: 15 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 15 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 43 | 2.0423 | 0.2562 | | |
| | No log | 2.0 | 86 | 1.9764 | 0.2812 | | |
| | No log | 3.0 | 129 | 1.8803 | 0.3125 | | |
| | No log | 4.0 | 172 | 1.7690 | 0.3187 | | |
| | No log | 5.0 | 215 | 1.6910 | 0.375 | | |
| | No log | 6.0 | 258 | 1.6397 | 0.3688 | | |
| | No log | 7.0 | 301 | 1.6053 | 0.4688 | | |
| | No log | 8.0 | 344 | 1.5674 | 0.4875 | | |
| | No log | 9.0 | 387 | 1.5714 | 0.4625 | | |
| | No log | 10.0 | 430 | 1.5394 | 0.4938 | | |
| | No log | 11.0 | 473 | 1.5183 | 0.4375 | | |
| | 1.6941 | 12.0 | 516 | 1.5211 | 0.4938 | | |
| | 1.6941 | 13.0 | 559 | 1.4997 | 0.4562 | | |
| | 1.6941 | 14.0 | 602 | 1.5191 | 0.4375 | | |
| | 1.6941 | 15.0 | 645 | 1.4892 | 0.4875 | | |
| ### Framework versions | |
| - Transformers 4.33.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |