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jaffe_V2_200_1

Image Classification
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vit
Generated from Trainer
Eval Results (legacy)
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: WinKawaks/vit-tiny-patch16-224
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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: jaffe_V2_200_1
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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: 0.9
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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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+ # jaffe_V2_200_1
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+
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+ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3747
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+ - Accuracy: 0.9
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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: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 200
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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 | 1.0 | 1 | 2.4997 | 0.0667 |
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+ | No log | 2.0 | 2 | 2.6037 | 0.1 |
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+ | No log | 3.0 | 3 | 2.3924 | 0.0667 |
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+ | No log | 4.0 | 4 | 2.3152 | 0.1 |
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+ | No log | 5.0 | 5 | 2.1146 | 0.1667 |
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+ | No log | 6.0 | 6 | 2.1610 | 0.2333 |
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+ | No log | 7.0 | 7 | 2.1346 | 0.1333 |
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+ | No log | 8.0 | 8 | 2.1400 | 0.1 |
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+ | No log | 9.0 | 9 | 2.1422 | 0.0667 |
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+ | 2.3217 | 10.0 | 10 | 2.0948 | 0.1333 |
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+ | 2.3217 | 11.0 | 11 | 2.0994 | 0.2 |
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+ | 2.3217 | 12.0 | 12 | 1.8570 | 0.3333 |
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+ | 2.3217 | 13.0 | 13 | 1.9750 | 0.2667 |
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+ | 2.3217 | 14.0 | 14 | 1.8089 | 0.3 |
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+ | 2.3217 | 15.0 | 15 | 1.8738 | 0.3 |
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+ | 2.3217 | 16.0 | 16 | 1.7751 | 0.3333 |
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+ | 2.3217 | 17.0 | 17 | 1.7744 | 0.2 |
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+ | 2.3217 | 18.0 | 18 | 1.7998 | 0.3333 |
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+ | 2.3217 | 19.0 | 19 | 1.7048 | 0.2667 |
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+ | 1.798 | 20.0 | 20 | 1.6367 | 0.4 |
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+ | 1.798 | 21.0 | 21 | 1.6092 | 0.3 |
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+ | 1.798 | 22.0 | 22 | 1.5605 | 0.3667 |
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+ | 1.798 | 23.0 | 23 | 1.4219 | 0.5 |
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+ | 1.798 | 24.0 | 24 | 1.5037 | 0.4 |
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+ | 1.798 | 25.0 | 25 | 1.3966 | 0.4333 |
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+ | 1.798 | 26.0 | 26 | 1.4327 | 0.4 |
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+ | 1.798 | 27.0 | 27 | 1.3484 | 0.4 |
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+ | 1.798 | 28.0 | 28 | 1.3958 | 0.4 |
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+ | 1.798 | 29.0 | 29 | 1.2789 | 0.4667 |
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+ | 1.1133 | 30.0 | 30 | 1.2002 | 0.4333 |
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+ | 1.1133 | 31.0 | 31 | 1.1080 | 0.4667 |
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+ | 1.1133 | 32.0 | 32 | 0.9814 | 0.6 |
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+ | 1.1133 | 33.0 | 33 | 1.0498 | 0.5667 |
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+ | 1.1133 | 34.0 | 34 | 0.9709 | 0.6333 |
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+ | 1.1133 | 35.0 | 35 | 0.9985 | 0.5333 |
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+ | 1.1133 | 36.0 | 36 | 0.8779 | 0.6667 |
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+ | 1.1133 | 37.0 | 37 | 0.7959 | 0.7 |
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+ | 1.1133 | 38.0 | 38 | 0.7583 | 0.7 |
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+ | 1.1133 | 39.0 | 39 | 1.0074 | 0.5667 |
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+ | 0.5945 | 40.0 | 40 | 0.6441 | 0.6667 |
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+ | 0.5945 | 41.0 | 41 | 0.7701 | 0.6667 |
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+ | 0.5945 | 42.0 | 42 | 0.8433 | 0.6667 |
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+ | 0.5945 | 43.0 | 43 | 0.7998 | 0.6667 |
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+ | 0.5945 | 44.0 | 44 | 0.7087 | 0.7 |
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+ | 0.5945 | 45.0 | 45 | 0.5793 | 0.8333 |
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+ | 0.5945 | 46.0 | 46 | 0.5024 | 0.8 |
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+ | 0.5945 | 47.0 | 47 | 0.8088 | 0.7 |
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+ | 0.5945 | 48.0 | 48 | 0.7690 | 0.7 |
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+ | 0.5945 | 49.0 | 49 | 0.8561 | 0.6667 |
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+ | 0.3008 | 50.0 | 50 | 0.4728 | 0.8667 |
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+ | 0.3008 | 51.0 | 51 | 0.5935 | 0.6667 |
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+ | 0.3008 | 52.0 | 52 | 0.3772 | 0.9 |
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+ | 0.3008 | 53.0 | 53 | 0.6337 | 0.6333 |
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+ | 0.3008 | 54.0 | 54 | 0.6097 | 0.7 |
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+ | 0.3008 | 55.0 | 55 | 0.4838 | 0.8333 |
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+ | 0.3008 | 56.0 | 56 | 0.5487 | 0.8333 |
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+ | 0.3008 | 57.0 | 57 | 0.5395 | 0.8 |
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+ | 0.3008 | 58.0 | 58 | 0.5078 | 0.7667 |
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+ | 0.3008 | 59.0 | 59 | 0.4211 | 0.8 |
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+ | 0.1792 | 61.0 | 61 | 0.4603 | 0.8333 |
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+ | 0.1792 | 62.0 | 62 | 0.2765 | 0.9 |
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+ | 0.1792 | 64.0 | 64 | 0.3247 | 0.9 |
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+ | 0.1792 | 65.0 | 65 | 0.6290 | 0.6667 |
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+ | 0.1792 | 66.0 | 66 | 0.5741 | 0.8 |
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+ | 0.1158 | 72.0 | 72 | 0.3892 | 0.8667 |
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+ | 0.1158 | 73.0 | 73 | 0.5258 | 0.8 |
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+ | 0.1158 | 75.0 | 75 | 0.5055 | 0.7667 |
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+ | 0.1158 | 79.0 | 79 | 0.3794 | 0.8333 |
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+ | 0.0936 | 80.0 | 80 | 0.2896 | 0.9 |
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+ | 0.0473 | 90.0 | 90 | 0.5298 | 0.8 |
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+ | 0.0473 | 97.0 | 97 | 0.3308 | 0.8667 |
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+ | 0.0473 | 98.0 | 98 | 0.4839 | 0.8 |
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+ | 0.0473 | 99.0 | 99 | 0.4554 | 0.8 |
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+ | 0.0443 | 100.0 | 100 | 0.2150 | 0.9667 |
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+ | 0.0443 | 102.0 | 102 | 0.2575 | 0.9 |
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+ | 0.0443 | 103.0 | 103 | 0.3313 | 0.8667 |
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+ | 0.0443 | 105.0 | 105 | 0.3910 | 0.8667 |
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+ | 0.0443 | 106.0 | 106 | 0.5569 | 0.8333 |
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+ | 0.0443 | 107.0 | 107 | 0.4688 | 0.8667 |
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+ | 0.0443 | 108.0 | 108 | 0.2292 | 0.9333 |
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+ | 0.0443 | 109.0 | 109 | 0.4958 | 0.8 |
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+ | 0.0353 | 111.0 | 111 | 0.6191 | 0.7333 |
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+ | 0.0353 | 112.0 | 112 | 0.5096 | 0.8 |
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+ | 0.0353 | 113.0 | 113 | 0.3478 | 0.9 |
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+ | 0.0353 | 114.0 | 114 | 0.3585 | 0.8667 |
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+ | 0.0353 | 115.0 | 115 | 0.3859 | 0.8 |
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+ | 0.0353 | 118.0 | 118 | 0.4710 | 0.8 |
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+ | 0.0292 | 120.0 | 120 | 0.6853 | 0.8333 |
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+ | 0.0292 | 121.0 | 121 | 0.4836 | 0.8 |
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+ | 0.0292 | 122.0 | 122 | 0.5246 | 0.8 |
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+ | 0.0292 | 123.0 | 123 | 0.4446 | 0.8667 |
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+ | 0.0292 | 124.0 | 124 | 0.4238 | 0.8 |
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+ | 0.0292 | 125.0 | 125 | 0.3543 | 0.8333 |
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+ | 0.0292 | 126.0 | 126 | 0.2007 | 0.9333 |
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+ | 0.0292 | 127.0 | 127 | 0.2274 | 0.9333 |
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+ | 0.0292 | 128.0 | 128 | 0.3778 | 0.8333 |
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+ | 0.0292 | 129.0 | 129 | 0.4544 | 0.8333 |
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+ | 0.0296 | 130.0 | 130 | 0.2613 | 0.8667 |
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+ | 0.0296 | 131.0 | 131 | 0.3248 | 0.9 |
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+ | 0.0296 | 132.0 | 132 | 0.4552 | 0.8 |
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+ | 0.0296 | 133.0 | 133 | 0.4356 | 0.8333 |
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+ | 0.0296 | 134.0 | 134 | 0.3427 | 0.9 |
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+ | 0.0296 | 135.0 | 135 | 0.1513 | 1.0 |
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+ | 0.0296 | 139.0 | 139 | 0.4339 | 0.9333 |
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+ | 0.0178 | 140.0 | 140 | 0.2465 | 0.9 |
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+ | 0.0178 | 149.0 | 149 | 0.3456 | 0.8333 |
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+ | 0.0103 | 151.0 | 151 | 0.3841 | 0.8333 |
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+ | 0.0103 | 158.0 | 158 | 0.3021 | 0.9 |
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+ | 0.0103 | 159.0 | 159 | 0.4477 | 0.9 |
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+ | 0.0075 | 161.0 | 161 | 0.2648 | 0.9333 |
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+ | 0.0075 | 162.0 | 162 | 0.3269 | 0.9333 |
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+ | 0.0075 | 163.0 | 163 | 0.5231 | 0.8 |
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+ | 0.0075 | 164.0 | 164 | 0.2841 | 0.8667 |
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+ | 0.0075 | 168.0 | 168 | 0.3873 | 0.9 |
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+ | 0.0075 | 169.0 | 169 | 0.3150 | 0.9333 |
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+ | 0.0062 | 170.0 | 170 | 0.3809 | 0.9 |
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+ | 0.0062 | 171.0 | 171 | 0.2062 | 0.9 |
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+ | 0.0062 | 172.0 | 172 | 0.3242 | 0.8667 |
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+ | 0.0062 | 175.0 | 175 | 0.2553 | 0.8667 |
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+ | 0.0062 | 176.0 | 176 | 0.4475 | 0.9 |
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+ | 0.0062 | 177.0 | 177 | 0.3598 | 0.9333 |
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+ | 0.0062 | 178.0 | 178 | 0.3488 | 0.8333 |
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+ | 0.0062 | 179.0 | 179 | 0.2966 | 0.8333 |
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+ | 0.0056 | 180.0 | 180 | 0.4635 | 0.8 |
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+ | 0.0056 | 181.0 | 181 | 0.2402 | 0.9 |
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+ | 0.0056 | 182.0 | 182 | 0.3984 | 0.8667 |
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+ | 0.0056 | 183.0 | 183 | 0.2032 | 0.9 |
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+ | 0.0056 | 184.0 | 184 | 0.2633 | 0.8333 |
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+ | 0.0056 | 185.0 | 185 | 0.3015 | 0.9333 |
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+ | 0.0056 | 186.0 | 186 | 0.3774 | 0.9 |
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+ | 0.0056 | 187.0 | 187 | 0.5716 | 0.8333 |
258
+ | 0.0056 | 188.0 | 188 | 0.3961 | 0.8667 |
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+ | 0.0056 | 189.0 | 189 | 0.3915 | 0.9 |
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+ | 0.0048 | 190.0 | 190 | 0.3788 | 0.8333 |
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+ | 0.0048 | 191.0 | 191 | 0.4823 | 0.8667 |
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+ | 0.0048 | 192.0 | 192 | 0.3158 | 0.8667 |
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+ | 0.0048 | 193.0 | 193 | 0.2184 | 0.8667 |
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+ | 0.0048 | 194.0 | 194 | 0.3363 | 0.8667 |
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+ | 0.0048 | 195.0 | 195 | 0.3996 | 0.9 |
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+ | 0.0048 | 196.0 | 196 | 0.2263 | 0.8333 |
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+ | 0.0048 | 197.0 | 197 | 0.4634 | 0.8333 |
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+ | 0.0048 | 198.0 | 198 | 0.3492 | 0.8667 |
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+ | 0.0048 | 199.0 | 199 | 0.3086 | 0.9 |
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+ | 0.0034 | 200.0 | 200 | 0.3747 | 0.9 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.0
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.3.1
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+ - Tokenizers 0.21.0
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