--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: meat_calssify_fresh_crop_fixed_epoch100_V_0_2 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: 0.7974683544303798 --- # meat_calssify_fresh_crop_fixed_epoch100_V_0_2 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. It achieves the following results on the evaluation set: - Loss: 0.7219 - Accuracy: 0.7975 ## 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-05 - train_batch_size: 64 - eval_batch_size: 1 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0968 | 1.0 | 10 | 1.0907 | 0.3797 | | 1.0804 | 2.0 | 20 | 1.0759 | 0.3924 | | 1.0578 | 3.0 | 30 | 1.0750 | 0.4241 | | 1.0273 | 4.0 | 40 | 1.0443 | 0.4684 | | 0.9866 | 5.0 | 50 | 1.0325 | 0.4747 | | 0.9234 | 6.0 | 60 | 0.9837 | 0.5886 | | 0.8597 | 7.0 | 70 | 0.9564 | 0.5443 | | 0.8042 | 8.0 | 80 | 0.9315 | 0.5633 | | 0.8463 | 9.0 | 90 | 0.9334 | 0.5380 | | 0.7795 | 10.0 | 100 | 0.9305 | 0.5443 | | 0.7375 | 11.0 | 110 | 0.8787 | 0.6076 | | 0.6489 | 12.0 | 120 | 0.8685 | 0.6392 | | 0.5958 | 13.0 | 130 | 0.8133 | 0.6582 | | 0.5308 | 14.0 | 140 | 0.8563 | 0.6519 | | 0.5206 | 15.0 | 150 | 0.7902 | 0.6709 | | 0.4617 | 16.0 | 160 | 0.8114 | 0.6456 | | 0.4338 | 17.0 | 170 | 0.8134 | 0.6646 | | 0.454 | 18.0 | 180 | 0.7283 | 0.6772 | | 0.5094 | 19.0 | 190 | 0.7035 | 0.6962 | | 0.4133 | 20.0 | 200 | 0.7652 | 0.6835 | | 0.3504 | 21.0 | 210 | 0.7225 | 0.7089 | | 0.3602 | 22.0 | 220 | 0.8140 | 0.6582 | | 0.32 | 23.0 | 230 | 0.7057 | 0.7278 | | 0.2849 | 24.0 | 240 | 0.7051 | 0.6899 | | 0.3051 | 25.0 | 250 | 0.7805 | 0.7025 | | 0.3099 | 26.0 | 260 | 0.7456 | 0.6772 | | 0.3305 | 27.0 | 270 | 0.7802 | 0.6646 | | 0.2508 | 28.0 | 280 | 0.7222 | 0.7152 | | 0.2842 | 29.0 | 290 | 0.6745 | 0.7278 | | 0.2584 | 30.0 | 300 | 0.6029 | 0.7658 | | 0.2324 | 31.0 | 310 | 0.6066 | 0.7911 | | 0.3014 | 32.0 | 320 | 0.7253 | 0.7215 | | 0.2279 | 33.0 | 330 | 0.7050 | 0.7089 | | 0.2363 | 34.0 | 340 | 0.7361 | 0.7785 | | 0.2085 | 35.0 | 350 | 0.6596 | 0.7658 | | 0.1808 | 36.0 | 360 | 0.7104 | 0.7532 | | 0.2051 | 37.0 | 370 | 0.7471 | 0.7152 | | 0.1911 | 38.0 | 380 | 0.8262 | 0.7025 | | 0.2027 | 39.0 | 390 | 0.7785 | 0.7532 | | 0.1944 | 40.0 | 400 | 0.8136 | 0.6835 | | 0.1627 | 41.0 | 410 | 0.8254 | 0.7152 | | 0.1619 | 42.0 | 420 | 0.8766 | 0.6772 | | 0.1619 | 43.0 | 430 | 0.6940 | 0.7405 | | 0.1635 | 44.0 | 440 | 0.8477 | 0.7215 | | 0.1323 | 45.0 | 450 | 0.6644 | 0.7848 | | 0.1253 | 46.0 | 460 | 0.7747 | 0.7468 | | 0.1254 | 47.0 | 470 | 0.9075 | 0.6962 | | 0.1494 | 48.0 | 480 | 0.8104 | 0.7405 | | 0.1702 | 49.0 | 490 | 0.7167 | 0.7532 | | 0.1591 | 50.0 | 500 | 0.8214 | 0.6962 | | 0.1105 | 51.0 | 510 | 0.9359 | 0.7152 | | 0.1354 | 52.0 | 520 | 0.7214 | 0.7342 | | 0.119 | 53.0 | 530 | 0.7825 | 0.7342 | | 0.0841 | 54.0 | 540 | 0.7528 | 0.7595 | | 0.12 | 55.0 | 550 | 0.7002 | 0.7658 | | 0.1096 | 56.0 | 560 | 0.7747 | 0.7785 | | 0.1192 | 57.0 | 570 | 0.7368 | 0.7532 | | 0.1268 | 58.0 | 580 | 0.7098 | 0.7722 | | 0.1351 | 59.0 | 590 | 0.6097 | 0.7848 | | 0.1248 | 60.0 | 600 | 0.8102 | 0.7215 | | 0.1378 | 61.0 | 610 | 0.6786 | 0.7405 | | 0.1208 | 62.0 | 620 | 0.5467 | 0.8101 | | 0.0786 | 63.0 | 630 | 0.7059 | 0.7785 | | 0.1048 | 64.0 | 640 | 0.7945 | 0.7278 | | 0.0954 | 65.0 | 650 | 0.8258 | 0.7278 | | 0.121 | 66.0 | 660 | 0.7267 | 0.7532 | | 0.0921 | 67.0 | 670 | 0.5914 | 0.7911 | | 0.092 | 68.0 | 680 | 0.6923 | 0.7722 | | 0.1153 | 69.0 | 690 | 0.6655 | 0.8038 | | 0.0987 | 70.0 | 700 | 0.6774 | 0.7722 | | 0.0797 | 71.0 | 710 | 0.6143 | 0.7975 | | 0.0842 | 72.0 | 720 | 0.7301 | 0.7595 | | 0.0707 | 73.0 | 730 | 0.7614 | 0.7405 | | 0.0848 | 74.0 | 740 | 0.7578 | 0.7785 | | 0.0853 | 75.0 | 750 | 0.7785 | 0.7405 | | 0.0761 | 76.0 | 760 | 0.8719 | 0.7532 | | 0.1019 | 77.0 | 770 | 0.5698 | 0.8165 | | 0.0747 | 78.0 | 780 | 0.7956 | 0.7278 | | 0.0657 | 79.0 | 790 | 0.5792 | 0.7975 | | 0.0969 | 80.0 | 800 | 0.5721 | 0.8101 | | 0.0597 | 81.0 | 810 | 0.7171 | 0.7785 | | 0.0787 | 82.0 | 820 | 0.7493 | 0.7595 | | 0.0823 | 83.0 | 830 | 0.6758 | 0.8038 | | 0.0828 | 84.0 | 840 | 0.8082 | 0.7722 | | 0.0693 | 85.0 | 850 | 0.7310 | 0.7911 | | 0.074 | 86.0 | 860 | 0.6492 | 0.8228 | | 0.0736 | 87.0 | 870 | 0.7373 | 0.7785 | | 0.0763 | 88.0 | 880 | 0.7254 | 0.7848 | | 0.0823 | 89.0 | 890 | 0.8261 | 0.7785 | | 0.0614 | 90.0 | 900 | 0.6919 | 0.7911 | | 0.0916 | 91.0 | 910 | 0.5884 | 0.7975 | | 0.0539 | 92.0 | 920 | 0.6960 | 0.7658 | | 0.0604 | 93.0 | 930 | 0.6502 | 0.7975 | | 0.0596 | 94.0 | 940 | 0.6058 | 0.7975 | | 0.0599 | 95.0 | 950 | 0.7166 | 0.7785 | | 0.0452 | 96.0 | 960 | 0.8093 | 0.7658 | | 0.0556 | 97.0 | 970 | 0.6589 | 0.8354 | | 0.0675 | 98.0 | 980 | 0.7471 | 0.8101 | | 0.0581 | 99.0 | 990 | 0.6568 | 0.8038 | | 0.0515 | 100.0 | 1000 | 0.7219 | 0.7975 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0 - Datasets 2.19.2 - Tokenizers 0.19.1