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fotocopy-ori

Image Classification
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clip
Generated from Trainer
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  2. model.safetensors +1 -1
  3. runs/Jul09_03-23-54_35204c829297/events.out.tfevents.1720495467.35204c829297.34.0 +2 -2
README.md ADDED
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+ ---
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+ base_model: openai/clip-vit-base-patch32
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: fotocopy-ori
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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: validation
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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: 0.9491525423728814
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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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+ # fotocopy-ori
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+
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+ This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4776
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+ - Accuracy: 0.9492
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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: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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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+ - num_epochs: 25
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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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+ | No log | 0.9231 | 3 | 0.6343 | 0.4915 |
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+ | No log | 1.8462 | 6 | 0.2235 | 0.9322 |
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+ | No log | 2.7692 | 9 | 0.1887 | 0.9492 |
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+ | No log | 4.0 | 13 | 0.0278 | 0.9831 |
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+ | 0.4375 | 4.9231 | 16 | 1.6119 | 0.8475 |
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+ | 0.4375 | 5.8462 | 19 | 0.5158 | 0.8983 |
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+ | 0.4375 | 6.7692 | 22 | 0.0602 | 0.9661 |
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+ | 0.4375 | 8.0 | 26 | 0.3831 | 0.9492 |
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+ | 0.4375 | 8.9231 | 29 | 0.4555 | 0.9492 |
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+ | 0.1245 | 9.8462 | 32 | 0.9890 | 0.9153 |
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+ | 0.1245 | 10.7692 | 35 | 0.4632 | 0.9322 |
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+ | 0.1245 | 12.0 | 39 | 0.5992 | 0.9322 |
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+ | 0.1245 | 12.9231 | 42 | 0.6255 | 0.9322 |
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+ | 0.048 | 13.8462 | 45 | 0.5156 | 0.9492 |
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+ | 0.048 | 14.7692 | 48 | 0.6033 | 0.9492 |
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+ | 0.048 | 16.0 | 52 | 0.5978 | 0.9492 |
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+ | 0.048 | 16.9231 | 55 | 0.5747 | 0.9492 |
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+ | 0.048 | 17.8462 | 58 | 0.5635 | 0.9492 |
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+ | 0.0005 | 18.7692 | 61 | 0.5314 | 0.9492 |
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+ | 0.0005 | 20.0 | 65 | 0.5023 | 0.9492 |
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+ | 0.0005 | 20.9231 | 68 | 0.4886 | 0.9492 |
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+ | 0.0005 | 21.8462 | 71 | 0.4809 | 0.9492 |
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+ | 0.0005 | 22.7692 | 74 | 0.4779 | 0.9492 |
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+ | 0.0 | 23.0769 | 75 | 0.4776 | 0.9492 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.1.2
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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