Instructions to use axelit64/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use axelit64/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="axelit64/image_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("axelit64/image_classification") model = AutoModelForImageClassification.from_pretrained("axelit64/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from axelit64/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 2.89 kB
-
https://huggingface.co/axelit64/image_classification/resolve/main/README.md
- Command line
-
hf download hf://axelit64/image_classification/README.md
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curl -L -o README.md https://huggingface.co/axelit64/image_classification/resolve/main/README.md
2.89 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: 0.575 | |
| <!-- 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: 1.3340 | |
| - Accuracy: 0.575 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 20 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| | |
| | No log | 1.0 | 40 | 1.5156 | 0.45 | | |
| | No log | 2.0 | 80 | 1.4200 | 0.4562 | | |
| | No log | 3.0 | 120 | 1.3790 | 0.5 | | |
| | No log | 4.0 | 160 | 1.2859 | 0.525 | | |
| | No log | 5.0 | 200 | 1.2592 | 0.5125 | | |
| | No log | 6.0 | 240 | 1.3145 | 0.55 | | |
| | No log | 7.0 | 280 | 1.3267 | 0.4813 | | |
| | No log | 8.0 | 320 | 1.3288 | 0.5 | | |
| | No log | 9.0 | 360 | 1.3073 | 0.5 | | |
| | No log | 10.0 | 400 | 1.3066 | 0.5188 | | |
| | No log | 11.0 | 440 | 1.2691 | 0.5563 | | |
| | No log | 12.0 | 480 | 1.2809 | 0.5437 | | |
| | 0.876 | 13.0 | 520 | 1.2963 | 0.5625 | | |
| | 0.876 | 14.0 | 560 | 1.2965 | 0.5312 | | |
| | 0.876 | 15.0 | 600 | 1.3542 | 0.5188 | | |
| | 0.876 | 16.0 | 640 | 1.3489 | 0.5125 | | |
| | 0.876 | 17.0 | 680 | 1.3146 | 0.5687 | | |
| | 0.876 | 18.0 | 720 | 1.2442 | 0.575 | | |
| | 0.876 | 19.0 | 760 | 1.3497 | 0.575 | | |
| | 0.876 | 20.0 | 800 | 1.3316 | 0.5437 | | |
| ### Framework versions | |
| - Transformers 4.33.2 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |