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visual-emotion-recognition

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224-in21k
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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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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: visual-emotion-recognition
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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: 0.6171875
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+ - name: Precision
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+ type: precision
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+ value: 0.6123019520308124
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+ - name: Recall
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+ type: recall
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+ value: 0.6171875
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+ - name: F1
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+ type: f1
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+ value: 0.6099565615619817
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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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+ # visual-emotion-recognition
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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 imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2563
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+ - Accuracy: 0.6172
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+ - Precision: 0.6123
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+ - Recall: 0.6172
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+ - F1: 0.6100
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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: 1e-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: 3
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+ - total_train_batch_size: 96
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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: 100
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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 | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 2.0811 | 0.94 | 5 | 2.0911 | 0.0859 | 0.0534 | 0.0859 | 0.0658 |
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+ | 2.0668 | 1.88 | 10 | 2.0830 | 0.1016 | 0.0654 | 0.1016 | 0.0758 |
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+ | 2.057 | 3.0 | 16 | 2.0733 | 0.1328 | 0.1119 | 0.1328 | 0.1066 |
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+ | 2.0445 | 3.94 | 21 | 2.0643 | 0.1328 | 0.0965 | 0.1328 | 0.1000 |
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+ | 2.0198 | 4.88 | 26 | 2.0537 | 0.1797 | 0.1911 | 0.1797 | 0.1604 |
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+ | 2.008 | 6.0 | 32 | 2.0387 | 0.1797 | 0.1669 | 0.1797 | 0.1513 |
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+ | 1.9937 | 6.94 | 37 | 2.0241 | 0.1875 | 0.1773 | 0.1875 | 0.1595 |
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+ | 1.9711 | 7.88 | 42 | 2.0078 | 0.2031 | 0.1939 | 0.2031 | 0.1737 |
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+ | 1.9468 | 9.0 | 48 | 1.9872 | 0.2578 | 0.2619 | 0.2578 | 0.2231 |
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+ | 1.9184 | 9.94 | 53 | 1.9663 | 0.2969 | 0.3203 | 0.2969 | 0.2609 |
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+ | 1.9042 | 10.88 | 58 | 1.9428 | 0.3047 | 0.3410 | 0.3047 | 0.2711 |
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+ | 1.8673 | 12.0 | 64 | 1.9127 | 0.3047 | 0.3731 | 0.3047 | 0.2730 |
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+ | 1.8449 | 12.94 | 69 | 1.8858 | 0.3203 | 0.4648 | 0.3203 | 0.2835 |
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+ | 1.8019 | 13.88 | 74 | 1.8572 | 0.3203 | 0.4856 | 0.3203 | 0.2924 |
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+ | 1.7438 | 15.0 | 80 | 1.8182 | 0.3203 | 0.4643 | 0.3203 | 0.3016 |
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+ | 1.7037 | 15.94 | 85 | 1.7909 | 0.3438 | 0.4862 | 0.3438 | 0.3339 |
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+ | 1.6787 | 16.88 | 90 | 1.7651 | 0.3438 | 0.4510 | 0.3438 | 0.3339 |
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+ | 1.6514 | 18.0 | 96 | 1.7360 | 0.3672 | 0.4630 | 0.3672 | 0.3641 |
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+ | 1.6322 | 18.94 | 101 | 1.7153 | 0.3828 | 0.4710 | 0.3828 | 0.3783 |
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+ | 1.5861 | 19.88 | 106 | 1.6980 | 0.4062 | 0.5040 | 0.4062 | 0.3963 |
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+ | 1.5871 | 21.0 | 112 | 1.6797 | 0.4219 | 0.4768 | 0.4219 | 0.4134 |
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+ | 1.5709 | 21.94 | 117 | 1.6635 | 0.4062 | 0.4665 | 0.4062 | 0.4038 |
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+ | 1.5296 | 22.88 | 122 | 1.6470 | 0.4297 | 0.4772 | 0.4297 | 0.4213 |
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+ | 1.5168 | 24.0 | 128 | 1.6318 | 0.4297 | 0.4712 | 0.4297 | 0.4234 |
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+ | 1.5105 | 24.94 | 133 | 1.6174 | 0.4609 | 0.4858 | 0.4609 | 0.4478 |
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+ | 1.485 | 25.88 | 138 | 1.6024 | 0.4766 | 0.5290 | 0.4766 | 0.4717 |
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+ | 1.4565 | 27.0 | 144 | 1.5929 | 0.4609 | 0.4800 | 0.4609 | 0.4517 |
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+ | 1.4273 | 27.94 | 149 | 1.5803 | 0.4688 | 0.4800 | 0.4688 | 0.4581 |
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+ | 1.4375 | 28.88 | 154 | 1.5650 | 0.5234 | 0.5527 | 0.5234 | 0.5134 |
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+ | 1.3806 | 30.0 | 160 | 1.5563 | 0.4688 | 0.5052 | 0.4688 | 0.4651 |
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+ | 1.3686 | 30.94 | 165 | 1.5443 | 0.5 | 0.5381 | 0.5 | 0.4969 |
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+ | 1.3636 | 31.88 | 170 | 1.5273 | 0.5234 | 0.5459 | 0.5234 | 0.5152 |
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+ | 1.3295 | 33.0 | 176 | 1.5175 | 0.5234 | 0.5444 | 0.5234 | 0.5160 |
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+ | 1.3426 | 33.94 | 181 | 1.5115 | 0.5078 | 0.5179 | 0.5078 | 0.5030 |
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+ | 1.2963 | 34.88 | 186 | 1.4918 | 0.5234 | 0.5399 | 0.5234 | 0.5133 |
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+ | 1.2917 | 36.0 | 192 | 1.4832 | 0.5391 | 0.5436 | 0.5391 | 0.5294 |
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+ | 1.2733 | 36.94 | 197 | 1.4718 | 0.5547 | 0.5730 | 0.5547 | 0.5475 |
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+ | 1.2398 | 37.88 | 202 | 1.4556 | 0.5703 | 0.5996 | 0.5703 | 0.5642 |
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+ | 1.2472 | 39.0 | 208 | 1.4575 | 0.5625 | 0.5820 | 0.5625 | 0.5600 |
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+ | 1.2286 | 39.94 | 213 | 1.4426 | 0.5781 | 0.6024 | 0.5781 | 0.5728 |
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+ | 1.1882 | 40.88 | 218 | 1.4277 | 0.5625 | 0.5787 | 0.5625 | 0.5532 |
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+ | 1.1833 | 42.0 | 224 | 1.4209 | 0.5625 | 0.5857 | 0.5625 | 0.5579 |
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+ | 1.1592 | 42.94 | 229 | 1.4171 | 0.5781 | 0.6089 | 0.5781 | 0.5766 |
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+ | 1.1386 | 43.88 | 234 | 1.4046 | 0.5859 | 0.6053 | 0.5859 | 0.5790 |
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+ | 1.118 | 45.0 | 240 | 1.3985 | 0.5547 | 0.5772 | 0.5547 | 0.5507 |
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+ | 1.1151 | 45.94 | 245 | 1.3996 | 0.5703 | 0.6026 | 0.5703 | 0.5701 |
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+ | 1.0848 | 46.88 | 250 | 1.3782 | 0.5703 | 0.5885 | 0.5703 | 0.5667 |
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+ | 1.0729 | 48.0 | 256 | 1.3891 | 0.5703 | 0.5809 | 0.5703 | 0.5641 |
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+ | 1.0702 | 48.94 | 261 | 1.3749 | 0.5625 | 0.5861 | 0.5625 | 0.5586 |
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+ | 1.0408 | 49.88 | 266 | 1.3725 | 0.5625 | 0.5732 | 0.5625 | 0.5561 |
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+ | 1.0274 | 51.0 | 272 | 1.3644 | 0.5547 | 0.5572 | 0.5547 | 0.5461 |
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+ | 1.0321 | 51.94 | 277 | 1.3651 | 0.5625 | 0.5841 | 0.5625 | 0.5587 |
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+ | 0.9872 | 52.88 | 282 | 1.3617 | 0.5547 | 0.5670 | 0.5547 | 0.5480 |
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+ | 0.9991 | 54.0 | 288 | 1.3496 | 0.5859 | 0.5902 | 0.5859 | 0.5774 |
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+ | 0.9891 | 54.94 | 293 | 1.3619 | 0.5781 | 0.5990 | 0.5781 | 0.5770 |
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+ | 0.9654 | 55.88 | 298 | 1.3322 | 0.5625 | 0.5830 | 0.5625 | 0.5609 |
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+ | 0.9489 | 57.0 | 304 | 1.3338 | 0.5781 | 0.5968 | 0.5781 | 0.5762 |
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+ | 0.9346 | 57.94 | 309 | 1.3332 | 0.5781 | 0.6057 | 0.5781 | 0.5796 |
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+ | 0.8965 | 58.88 | 314 | 1.3239 | 0.5781 | 0.6057 | 0.5781 | 0.5796 |
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+ | 0.8809 | 60.0 | 320 | 1.3269 | 0.5938 | 0.6005 | 0.5938 | 0.5885 |
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+ | 0.8928 | 60.94 | 325 | 1.3168 | 0.5703 | 0.5873 | 0.5703 | 0.5687 |
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+ | 0.8662 | 61.88 | 330 | 1.3241 | 0.5625 | 0.5889 | 0.5625 | 0.5641 |
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+ | 0.8496 | 63.0 | 336 | 1.3062 | 0.5703 | 0.5832 | 0.5703 | 0.5648 |
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+ | 0.8485 | 63.94 | 341 | 1.2968 | 0.5859 | 0.5776 | 0.5859 | 0.5734 |
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+ | 0.8425 | 64.88 | 346 | 1.3093 | 0.5781 | 0.5775 | 0.5781 | 0.5683 |
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+ | 0.8175 | 66.0 | 352 | 1.2888 | 0.5859 | 0.6029 | 0.5859 | 0.5851 |
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+ | 0.7942 | 66.94 | 357 | 1.3084 | 0.5781 | 0.5764 | 0.5781 | 0.5674 |
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+ | 0.7865 | 67.88 | 362 | 1.3040 | 0.5938 | 0.6029 | 0.5938 | 0.5897 |
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+ | 0.7376 | 69.0 | 368 | 1.2982 | 0.5781 | 0.5968 | 0.5781 | 0.5773 |
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+ | 0.7838 | 69.94 | 373 | 1.2960 | 0.5703 | 0.5851 | 0.5703 | 0.5676 |
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+ | 0.7779 | 70.88 | 378 | 1.2876 | 0.6016 | 0.5996 | 0.6016 | 0.5925 |
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+ | 0.7259 | 72.0 | 384 | 1.2898 | 0.5781 | 0.5805 | 0.5781 | 0.5716 |
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+ | 0.7242 | 72.94 | 389 | 1.2891 | 0.5859 | 0.6073 | 0.5859 | 0.5869 |
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+ | 0.7185 | 73.88 | 394 | 1.2800 | 0.6094 | 0.6131 | 0.6094 | 0.6048 |
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+ | 0.7366 | 75.0 | 400 | 1.2762 | 0.5781 | 0.5807 | 0.5781 | 0.5721 |
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+ | 0.7194 | 75.94 | 405 | 1.2847 | 0.5938 | 0.6019 | 0.5938 | 0.5898 |
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+ | 0.6699 | 76.88 | 410 | 1.2563 | 0.6172 | 0.6123 | 0.6172 | 0.6100 |
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+ | 0.6958 | 78.0 | 416 | 1.2937 | 0.5703 | 0.5764 | 0.5703 | 0.5609 |
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+ | 0.6673 | 78.94 | 421 | 1.2626 | 0.6094 | 0.6008 | 0.6094 | 0.5998 |
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+ | 0.6443 | 79.88 | 426 | 1.2561 | 0.5781 | 0.5820 | 0.5781 | 0.5734 |
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+ | 0.642 | 81.0 | 432 | 1.2654 | 0.5938 | 0.6009 | 0.5938 | 0.5910 |
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+ | 0.6536 | 81.94 | 437 | 1.2604 | 0.5781 | 0.5938 | 0.5781 | 0.5773 |
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+ | 0.5973 | 82.88 | 442 | 1.2783 | 0.5938 | 0.6081 | 0.5938 | 0.5927 |
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+ | 0.6074 | 84.0 | 448 | 1.2709 | 0.5938 | 0.6041 | 0.5938 | 0.5865 |
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+ | 0.6419 | 84.94 | 453 | 1.2820 | 0.5781 | 0.5815 | 0.5781 | 0.5680 |
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+ | 0.611 | 85.88 | 458 | 1.2447 | 0.5625 | 0.5678 | 0.5625 | 0.5601 |
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+ | 0.606 | 87.0 | 464 | 1.3020 | 0.5781 | 0.5889 | 0.5781 | 0.5711 |
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+ | 0.5996 | 87.94 | 469 | 1.2690 | 0.5859 | 0.6016 | 0.5859 | 0.5862 |
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+ | 0.5962 | 88.88 | 474 | 1.2713 | 0.5781 | 0.5787 | 0.5781 | 0.5699 |
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+ | 0.5423 | 90.0 | 480 | 1.2856 | 0.5703 | 0.5803 | 0.5703 | 0.5688 |
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+ | 0.5693 | 90.94 | 485 | 1.2512 | 0.5703 | 0.5886 | 0.5703 | 0.5724 |
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+ | 0.5426 | 91.88 | 490 | 1.2654 | 0.5859 | 0.5881 | 0.5859 | 0.5808 |
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+ | 0.5676 | 93.0 | 496 | 1.2829 | 0.5703 | 0.5818 | 0.5703 | 0.5702 |
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+ | 0.5275 | 93.75 | 500 | 1.2630 | 0.5391 | 0.5541 | 0.5391 | 0.5428 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.1
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