nateraw
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🍻 cheers

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.gitignore ADDED
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+ checkpoint-*/
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - image-classification
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+ - other-image-classification
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+ - generated_from_trainer
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+ datasets:
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+ - beans
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vit-base-beans-demo-v3
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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: beans
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+ type: beans
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9849624060150376
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vit-base-beans-demo-v3
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+
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+ 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 beans dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0645
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+ - Accuracy: 0.9850
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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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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+ - num_epochs: 2
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.0397 | 1.54 | 100 | 0.0645 | 0.9850 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.10.0.dev0
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+ - Pytorch 1.9.0+cu102
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+ - Datasets 1.11.0
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+ - Tokenizers 0.10.3
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+ "ViTForImageClassification"
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+ "id2label": {
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+ "0": "angular_leaf_spot",
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+ "1": "bean_rust",
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+ "2": "healthy"
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+ },
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+ "angular_leaf_spot": "0",
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+ "healthy": "2"
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ }
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