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Action_model_ViT_384

Image Classification
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  1. README.md +182 -0
  2. config.json +48 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/May22_14-44-38_706557cbf509/events.out.tfevents.1716389079.706557cbf509.34.0 +3 -0
  6. runs/May22_15-13-46_706557cbf509/events.out.tfevents.1716390827.706557cbf509.34.1 +3 -0
  7. runs/May22_15-14-12_706557cbf509/events.out.tfevents.1716390853.706557cbf509.34.2 +3 -0
  8. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-384
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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: Action_model_ViT_384
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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: Accuracy
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+ type: accuracy
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+ value: 0.859402460456942
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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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+ # Action_model_ViT_384
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8201
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+ - Accuracy: 0.8594
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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: 0.0001
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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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+ - 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: 20
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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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+ | 0.946 | 0.19 | 100 | 0.7540 | 0.7803 |
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+ | 0.9248 | 0.37 | 200 | 0.6282 | 0.7961 |
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+ | 0.7968 | 0.56 | 300 | 0.5834 | 0.8102 |
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+ | 0.6992 | 0.75 | 400 | 0.5647 | 0.8330 |
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+ | 0.7331 | 0.93 | 500 | 0.5430 | 0.8295 |
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+ | 0.5822 | 1.12 | 600 | 0.5894 | 0.8172 |
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+ | 0.5906 | 1.31 | 700 | 0.6862 | 0.7909 |
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+ | 0.5911 | 1.49 | 800 | 0.5369 | 0.8313 |
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+ | 0.4564 | 1.68 | 900 | 0.4657 | 0.8576 |
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+ | 0.6416 | 1.87 | 1000 | 0.5697 | 0.8190 |
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+ | 0.5653 | 2.05 | 1100 | 0.6152 | 0.8102 |
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+ | 0.4145 | 2.24 | 1200 | 0.5793 | 0.8225 |
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+ | 0.4743 | 2.43 | 1300 | 0.4642 | 0.8576 |
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+ | 0.4908 | 2.61 | 1400 | 0.4520 | 0.8612 |
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+ | 0.523 | 2.8 | 1500 | 0.4989 | 0.8453 |
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+ | 0.3315 | 2.99 | 1600 | 0.4786 | 0.8576 |
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+ | 0.2779 | 3.17 | 1700 | 0.5546 | 0.8524 |
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+ | 0.2984 | 3.36 | 1800 | 0.4977 | 0.8576 |
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+ | 0.5914 | 3.54 | 1900 | 0.6296 | 0.8225 |
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+ | 0.3236 | 3.73 | 2000 | 0.7225 | 0.8172 |
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+ | 0.6194 | 3.92 | 2100 | 0.5783 | 0.8506 |
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+ | 0.5066 | 4.1 | 2200 | 0.5825 | 0.8260 |
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+ | 0.3532 | 4.29 | 2300 | 0.5606 | 0.8594 |
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+ | 0.3531 | 4.48 | 2400 | 0.5068 | 0.8699 |
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+ | 0.2573 | 4.66 | 2500 | 0.5632 | 0.8576 |
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+ | 0.2713 | 4.85 | 2600 | 0.5047 | 0.8612 |
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+ | 0.3538 | 5.04 | 2700 | 0.5988 | 0.8471 |
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+ | 0.2291 | 5.22 | 2800 | 0.5751 | 0.8453 |
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+ | 0.2976 | 5.41 | 2900 | 0.5781 | 0.8559 |
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+ | 0.296 | 5.6 | 3000 | 0.5499 | 0.8664 |
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+ | 0.3776 | 5.78 | 3100 | 0.5718 | 0.8612 |
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+ | 0.2213 | 5.97 | 3200 | 0.5421 | 0.8682 |
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+ | 0.325 | 6.16 | 3300 | 0.6453 | 0.8453 |
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+ | 0.1594 | 6.34 | 3400 | 0.5558 | 0.8647 |
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+ | 0.3377 | 6.53 | 3500 | 0.6619 | 0.8418 |
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+ | 0.3743 | 6.72 | 3600 | 0.5446 | 0.8717 |
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+ | 0.2327 | 6.9 | 3700 | 0.5484 | 0.8735 |
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+ | 0.1659 | 7.09 | 3800 | 0.6629 | 0.8471 |
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+ | 0.4036 | 7.28 | 3900 | 0.6510 | 0.8330 |
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+ | 0.2084 | 7.46 | 4000 | 0.5640 | 0.8629 |
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+ | 0.2251 | 7.65 | 4100 | 0.6379 | 0.8541 |
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+ | 0.192 | 7.84 | 4200 | 0.5897 | 0.8629 |
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+ | 0.1956 | 8.02 | 4300 | 0.5874 | 0.8699 |
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+ | 0.1446 | 8.21 | 4400 | 0.6462 | 0.8594 |
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+ | 0.2971 | 8.4 | 4500 | 0.5909 | 0.8735 |
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+ | 0.2665 | 8.58 | 4600 | 0.6769 | 0.8612 |
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+ | 0.2937 | 8.77 | 4700 | 0.6760 | 0.8506 |
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+ | 0.1437 | 8.96 | 4800 | 0.6566 | 0.8489 |
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+ | 0.1433 | 9.14 | 4900 | 0.6659 | 0.8418 |
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+ | 0.2069 | 9.33 | 5000 | 0.6825 | 0.8541 |
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+ | 0.2095 | 9.51 | 5100 | 0.6157 | 0.8664 |
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+ | 0.1579 | 9.7 | 5200 | 0.6693 | 0.8629 |
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+ | 0.1962 | 9.89 | 5300 | 0.6911 | 0.8524 |
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+ | 0.3149 | 10.07 | 5400 | 0.6260 | 0.8559 |
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+ | 0.2166 | 10.26 | 5500 | 0.6200 | 0.8770 |
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+ | 0.1259 | 10.45 | 5600 | 0.7164 | 0.8576 |
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+ | 0.1892 | 10.63 | 5700 | 0.7182 | 0.8612 |
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+ | 0.1953 | 10.82 | 5800 | 0.7193 | 0.8418 |
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+ | 0.2392 | 11.01 | 5900 | 0.6621 | 0.8664 |
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+ | 0.1594 | 11.19 | 6000 | 0.7471 | 0.8489 |
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+ | 0.2156 | 11.38 | 6100 | 0.7316 | 0.8612 |
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+ | 0.137 | 11.57 | 6200 | 0.6837 | 0.8699 |
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+ | 0.181 | 11.75 | 6300 | 0.6595 | 0.8647 |
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+ | 0.2049 | 11.94 | 6400 | 0.6982 | 0.8506 |
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+ | 0.1028 | 12.13 | 6500 | 0.6771 | 0.8682 |
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+ | 0.1347 | 12.31 | 6600 | 0.6841 | 0.8699 |
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+ | 0.1269 | 12.5 | 6700 | 0.7226 | 0.8594 |
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+ | 0.2288 | 12.69 | 6800 | 0.7083 | 0.8629 |
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+ | 0.1094 | 12.87 | 6900 | 0.7455 | 0.8471 |
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+ | 0.0661 | 13.06 | 7000 | 0.7330 | 0.8541 |
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+ | 0.1811 | 13.25 | 7100 | 0.7363 | 0.8436 |
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+ | 0.2225 | 13.43 | 7200 | 0.7757 | 0.8453 |
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+ | 0.1619 | 13.62 | 7300 | 0.7361 | 0.8576 |
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+ | 0.2032 | 13.81 | 7400 | 0.7656 | 0.8576 |
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+ | 0.0216 | 13.99 | 7500 | 0.7760 | 0.8629 |
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+ | 0.2476 | 14.18 | 7600 | 0.7723 | 0.8612 |
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+ | 0.1616 | 14.37 | 7700 | 0.7247 | 0.8787 |
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+ | 0.1142 | 14.55 | 7800 | 0.7907 | 0.8699 |
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+ | 0.0906 | 14.74 | 7900 | 0.7829 | 0.8647 |
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+ | 0.2199 | 14.93 | 8000 | 0.7427 | 0.8717 |
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+ | 0.0643 | 15.11 | 8100 | 0.7280 | 0.8699 |
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+ | 0.1685 | 15.3 | 8200 | 0.8381 | 0.8541 |
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+ | 0.1677 | 15.49 | 8300 | 0.8638 | 0.8506 |
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+ | 0.1399 | 15.67 | 8400 | 0.8423 | 0.8612 |
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+ | 0.1041 | 15.86 | 8500 | 0.8051 | 0.8541 |
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+ | 0.2223 | 16.04 | 8600 | 0.7768 | 0.8647 |
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+ | 0.1016 | 16.23 | 8700 | 0.7965 | 0.8647 |
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+ | 0.065 | 16.42 | 8800 | 0.8331 | 0.8418 |
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+ | 0.1156 | 16.6 | 8900 | 0.8023 | 0.8629 |
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+ | 0.2263 | 16.79 | 9000 | 0.8116 | 0.8594 |
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+ | 0.1197 | 16.98 | 9100 | 0.8490 | 0.8576 |
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+ | 0.1931 | 17.16 | 9200 | 0.8194 | 0.8612 |
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+ | 0.1289 | 17.35 | 9300 | 0.8353 | 0.8489 |
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+ | 0.2039 | 17.54 | 9400 | 0.8163 | 0.8453 |
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+ | 0.0825 | 17.72 | 9500 | 0.7942 | 0.8524 |
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+ | 0.0712 | 17.91 | 9600 | 0.8027 | 0.8559 |
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+ | 0.244 | 18.1 | 9700 | 0.7803 | 0.8664 |
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+ | 0.1482 | 18.28 | 9800 | 0.7754 | 0.8629 |
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+ | 0.1829 | 18.47 | 9900 | 0.7810 | 0.8594 |
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+ | 0.019 | 18.66 | 10000 | 0.7972 | 0.8559 |
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+ | 0.061 | 18.84 | 10100 | 0.8180 | 0.8576 |
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+ | 0.117 | 19.03 | 10200 | 0.8319 | 0.8559 |
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+ | 0.1858 | 19.22 | 10300 | 0.8432 | 0.8559 |
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+ | 0.1087 | 19.4 | 10400 | 0.8273 | 0.8594 |
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+ | 0.1983 | 19.59 | 10500 | 0.8257 | 0.8612 |
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+ | 0.2453 | 19.78 | 10600 | 0.8177 | 0.8576 |
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+ | 0.1189 | 19.96 | 10700 | 0.8201 | 0.8594 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.1.2
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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