Instructions to use alyzbane/vit-base-patch16-224-finetuned-barkley with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alyzbane/vit-base-patch16-224-finetuned-barkley with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="alyzbane/vit-base-patch16-224-finetuned-barkley") 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("alyzbane/vit-base-patch16-224-finetuned-barkley") model = AutoModelForImageClassification.from_pretrained("alyzbane/vit-base-patch16-224-finetuned-barkley", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- .gitattributes +1 -0
- README.md +33 -52
- all_results.json +16 -17
- classification_report.png +0 -0
- config.json +1 -1
- confusion_matrix.png +0 -0
- eval_results.json +11 -11
- integrated_gradients_grid.jpg +3 -0
- model.safetensors +1 -1
- train_and_eval.jpg +0 -0
- train_results.json +6 -6
- trainer_state.json +284 -209
- training_args.bin +1 -1
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README.md
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base_model: google/vit-base-patch16-224
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tags:
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- generated_from_trainer
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- imagefolder
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metrics:
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- precision
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- accuracy
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model-index:
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- name: vit-base-patch16-224-finetuned-barkley
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Precision
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type: precision
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value: 0.9936145510835913
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type: recall
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value: 0.993421052631579
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- name: F1
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type: f1
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value: 0.993419541966282
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type: accuracy
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value: 0.9939393939393939
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-finetuned-barkley
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Precision:
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- Recall:
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- F1:
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- Accuracy:
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- Error Rate: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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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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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Top1 Accuracy | Error Rate |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
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### Framework versions
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- Pytorch 2.3.1+cu121
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- Datasets 3.0.1
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base_model: google/vit-base-patch16-224
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- accuracy
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model-index:
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- name: vit-base-patch16-224-finetuned-barkley
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-finetuned-barkley
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0036
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- Precision: 1.0
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- Recall: 1.0
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- F1: 1.0
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- Accuracy: 1.0
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- Top1 Accuracy: 1.0
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- Error Rate: 0.0
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0005
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Top1 Accuracy | Error Rate |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|
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| 1.6093 | 1.0 | 38 | 1.4340 | 0.4769 | 0.4342 | 0.4066 | 0.4149 | 0.4342 | 0.5851 |
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| 1.2908 | 2.0 | 76 | 1.1747 | 0.6587 | 0.6118 | 0.6160 | 0.6161 | 0.6118 | 0.3839 |
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| 1.0409 | 3.0 | 114 | 0.9174 | 0.7382 | 0.7303 | 0.7293 | 0.7425 | 0.7303 | 0.2575 |
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| 0.781 | 4.0 | 152 | 0.6528 | 0.8632 | 0.8618 | 0.8622 | 0.8650 | 0.8618 | 0.1350 |
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| 0.5429 | 5.0 | 190 | 0.4112 | 0.9417 | 0.9408 | 0.9405 | 0.9443 | 0.9408 | 0.0557 |
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| 0.328 | 6.0 | 228 | 0.2229 | 0.9809 | 0.9803 | 0.9802 | 0.9811 | 0.9803 | 0.0189 |
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| 0.1837 | 7.0 | 266 | 0.1181 | 0.9871 | 0.9868 | 0.9868 | 0.9878 | 0.9868 | 0.0122 |
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| 0.1131 | 8.0 | 304 | 0.0680 | 0.9937 | 0.9934 | 0.9934 | 0.9944 | 0.9934 | 0.0056 |
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| 0.0526 | 9.0 | 342 | 0.0387 | 0.9937 | 0.9934 | 0.9934 | 0.9944 | 0.9934 | 0.0056 |
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| 0.0283 | 10.0 | 380 | 0.0328 | 0.9873 | 0.9868 | 0.9869 | 0.9878 | 0.9868 | 0.0122 |
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| 0.019 | 11.0 | 418 | 0.0224 | 0.9873 | 0.9868 | 0.9868 | 0.9889 | 0.9868 | 0.0111 |
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| 0.0148 | 12.0 | 456 | 0.0201 | 0.9873 | 0.9868 | 0.9868 | 0.9889 | 0.9868 | 0.0111 |
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| 0.0095 | 13.0 | 494 | 0.0396 | 0.9871 | 0.9868 | 0.9868 | 0.9878 | 0.9868 | 0.0122 |
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| 0.007 | 14.0 | 532 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 |
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| 0.011 | 15.0 | 570 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 |
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| 0.0071 | 16.0 | 608 | 0.0092 | 0.9936 | 0.9934 | 0.9934 | 0.9941 | 0.9934 | 0.0059 |
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| 0.0103 | 17.0 | 646 | 0.0148 | 0.9936 | 0.9934 | 0.9934 | 0.9944 | 0.9934 | 0.0056 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.3.1+cu121
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- Datasets 3.0.1
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- Tokenizers 0.19.1
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all_results.json
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classification_report.png
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config.json
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.
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"problem_type": "single_label_classification",
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"qkv_bias": true,
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"torch_dtype": "float32",
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version https://git-lfs.github.com/spec/v1
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