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jjmcarrascosa
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vit_receipts_classifier

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
generated_from_trainer
Inference Endpoints
Model card Files Metrics Community
2
jjmcarrascosa commited on
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add model

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  1. README.md +63 -0
  2. all_results.json +13 -0
  3. checkpoint-100/config.json +32 -0
  4. checkpoint-100/optimizer.pt +3 -0
  5. checkpoint-100/preprocessor_config.json +17 -0
  6. checkpoint-100/pytorch_model.bin +3 -0
  7. checkpoint-100/rng_state.pth +3 -0
  8. checkpoint-100/scaler.pt +3 -0
  9. checkpoint-100/scheduler.pt +3 -0
  10. checkpoint-100/trainer_state.json +85 -0
  11. checkpoint-100/training_args.bin +3 -0
  12. checkpoint-1000/config.json +32 -0
  13. checkpoint-1000/optimizer.pt +3 -0
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  19. checkpoint-1000/trainer_state.json +634 -0
  20. checkpoint-1000/training_args.bin +3 -0
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  22. eval_results.json +8 -0
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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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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ model-index:
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+ - name: vit_tickers_binaryclf
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+ results: []
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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_tickers_binaryclf
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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 cord dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0116
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+ - F1: 0.9991
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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: 1
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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 | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.0026 | 0.28 | 500 | 0.0187 | 0.9982 |
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+ | 0.0186 | 0.56 | 1000 | 0.0116 | 0.9991 |
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+ | 0.0006 | 0.84 | 1500 | 0.0044 | 0.9997 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.21.2
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+ - Pytorch 1.11.0+cu102
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1
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+ }
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