--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224-pt22k-ft22k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: finetuned-FER2013 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7081156391501219 --- # finetuned-FER2013 This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8366 - Accuracy: 0.7081 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.8119 | 1.0 | 202 | 1.7993 | 0.3079 | | 1.6155 | 2.0 | 404 | 1.5446 | 0.4302 | | 1.4279 | 3.0 | 606 | 1.3084 | 0.5301 | | 1.3222 | 4.0 | 808 | 1.1817 | 0.5590 | | 1.2532 | 5.0 | 1010 | 1.1026 | 0.5789 | | 1.2019 | 6.0 | 1212 | 1.0432 | 0.5998 | | 1.2037 | 7.0 | 1414 | 1.0030 | 0.6137 | | 1.1757 | 8.0 | 1616 | 0.9873 | 0.6235 | | 1.1359 | 9.0 | 1818 | 0.9377 | 0.6423 | | 1.1282 | 10.0 | 2020 | 0.9231 | 0.6486 | | 1.1019 | 11.0 | 2222 | 0.9011 | 0.6562 | | 1.0494 | 12.0 | 2424 | 0.8968 | 0.6545 | | 0.9951 | 13.0 | 2626 | 0.8876 | 0.6607 | | 1.0121 | 14.0 | 2828 | 0.8720 | 0.6695 | | 1.0571 | 15.0 | 3030 | 0.8776 | 0.6691 | | 1.0049 | 16.0 | 3232 | 0.8627 | 0.6733 | | 0.988 | 17.0 | 3434 | 0.8639 | 0.6719 | | 0.9955 | 18.0 | 3636 | 0.8397 | 0.6806 | | 0.9381 | 19.0 | 3838 | 0.8430 | 0.6820 | | 0.9911 | 20.0 | 4040 | 0.8370 | 0.6837 | | 0.9305 | 21.0 | 4242 | 0.8373 | 0.6837 | | 0.9653 | 22.0 | 4444 | 0.8283 | 0.6883 | | 0.9134 | 23.0 | 4646 | 0.8289 | 0.6879 | | 0.9098 | 24.0 | 4848 | 0.8365 | 0.6837 | | 0.8761 | 25.0 | 5050 | 0.8190 | 0.6869 | | 0.9067 | 26.0 | 5252 | 0.8303 | 0.6876 | | 0.8765 | 27.0 | 5454 | 0.8188 | 0.6942 | | 0.8486 | 28.0 | 5656 | 0.8142 | 0.6959 | | 0.9357 | 29.0 | 5858 | 0.8114 | 0.6984 | | 0.9037 | 30.0 | 6060 | 0.8150 | 0.6917 | | 0.8758 | 31.0 | 6262 | 0.8165 | 0.6931 | | 0.8688 | 32.0 | 6464 | 0.8061 | 0.6994 | | 0.8736 | 33.0 | 6666 | 0.8056 | 0.6994 | | 0.8785 | 34.0 | 6868 | 0.8045 | 0.6991 | | 0.8292 | 35.0 | 7070 | 0.8095 | 0.6987 | | 0.8407 | 36.0 | 7272 | 0.8096 | 0.6956 | | 0.8609 | 37.0 | 7474 | 0.8137 | 0.6984 | | 0.9055 | 38.0 | 7676 | 0.8054 | 0.7018 | | 0.8355 | 39.0 | 7878 | 0.8080 | 0.6980 | | 0.8391 | 40.0 | 8080 | 0.8087 | 0.6966 | | 0.7987 | 41.0 | 8282 | 0.8041 | 0.6998 | | 0.818 | 42.0 | 8484 | 0.8070 | 0.7039 | | 0.7836 | 43.0 | 8686 | 0.8091 | 0.7025 | | 0.8348 | 44.0 | 8888 | 0.8047 | 0.7025 | | 0.8205 | 45.0 | 9090 | 0.8076 | 0.7025 | | 0.8023 | 46.0 | 9292 | 0.8056 | 0.7053 | | 0.8241 | 47.0 | 9494 | 0.8022 | 0.7039 | | 0.763 | 48.0 | 9696 | 0.8079 | 0.6994 | | 0.7422 | 49.0 | 9898 | 0.8062 | 0.7039 | | 0.7762 | 50.0 | 10100 | 0.8090 | 0.6998 | | 0.7786 | 51.0 | 10302 | 0.8122 | 0.6994 | | 0.8027 | 52.0 | 10504 | 0.8129 | 0.7043 | | 0.7966 | 53.0 | 10706 | 0.8094 | 0.7039 | | 0.8103 | 54.0 | 10908 | 0.8107 | 0.7039 | | 0.7827 | 55.0 | 11110 | 0.8126 | 0.7057 | | 0.7949 | 56.0 | 11312 | 0.8104 | 0.7119 | | 0.7511 | 57.0 | 11514 | 0.8122 | 0.7050 | | 0.7727 | 58.0 | 11716 | 0.8123 | 0.7078 | | 0.7723 | 59.0 | 11918 | 0.8194 | 0.7015 | | 0.7796 | 60.0 | 12120 | 0.8193 | 0.7053 | | 0.7768 | 61.0 | 12322 | 0.8159 | 0.7029 | | 0.7604 | 62.0 | 12524 | 0.8081 | 0.7085 | | 0.7784 | 63.0 | 12726 | 0.8169 | 0.7106 | | 0.7235 | 64.0 | 12928 | 0.8131 | 0.7015 | | 0.7384 | 65.0 | 13130 | 0.8149 | 0.7085 | | 0.6638 | 66.0 | 13332 | 0.8192 | 0.7078 | | 0.6998 | 67.0 | 13534 | 0.8243 | 0.7113 | | 0.7249 | 68.0 | 13736 | 0.8200 | 0.7015 | | 0.6809 | 69.0 | 13938 | 0.8140 | 0.7081 | | 0.701 | 70.0 | 14140 | 0.8177 | 0.7095 | | 0.7122 | 71.0 | 14342 | 0.8245 | 0.7053 | | 0.7269 | 72.0 | 14544 | 0.8245 | 0.7050 | | 0.6973 | 73.0 | 14746 | 0.8207 | 0.7095 | | 0.7241 | 74.0 | 14948 | 0.8210 | 0.7057 | | 0.7397 | 75.0 | 15150 | 0.8230 | 0.7060 | | 0.6832 | 76.0 | 15352 | 0.8308 | 0.7057 | | 0.7213 | 77.0 | 15554 | 0.8256 | 0.7025 | | 0.7115 | 78.0 | 15756 | 0.8291 | 0.7057 | | 0.688 | 79.0 | 15958 | 0.8337 | 0.7088 | | 0.6997 | 80.0 | 16160 | 0.8312 | 0.7060 | | 0.6924 | 81.0 | 16362 | 0.8321 | 0.7053 | | 0.7382 | 82.0 | 16564 | 0.8340 | 0.7050 | | 0.7513 | 83.0 | 16766 | 0.8320 | 0.7015 | | 0.656 | 84.0 | 16968 | 0.8389 | 0.7053 | | 0.6503 | 85.0 | 17170 | 0.8321 | 0.7085 | | 0.6661 | 86.0 | 17372 | 0.8355 | 0.7092 | | 0.7026 | 87.0 | 17574 | 0.8339 | 0.7088 | | 0.76 | 88.0 | 17776 | 0.8361 | 0.7092 | | 0.696 | 89.0 | 17978 | 0.8343 | 0.7106 | | 0.6713 | 90.0 | 18180 | 0.8337 | 0.7106 | | 0.6621 | 91.0 | 18382 | 0.8349 | 0.7057 | | 0.7042 | 92.0 | 18584 | 0.8360 | 0.7085 | | 0.7087 | 93.0 | 18786 | 0.8353 | 0.7085 | | 0.64 | 94.0 | 18988 | 0.8371 | 0.7088 | | 0.659 | 95.0 | 19190 | 0.8376 | 0.7071 | | 0.6246 | 96.0 | 19392 | 0.8376 | 0.7088 | | 0.6797 | 97.0 | 19594 | 0.8368 | 0.7092 | | 0.6652 | 98.0 | 19796 | 0.8376 | 0.7092 | | 0.629 | 99.0 | 19998 | 0.8370 | 0.7088 | | 0.6762 | 100.0 | 20200 | 0.8366 | 0.7081 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0