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dit-base_tobacco-small_tobacco3482_simkd

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Inference Endpoints
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  1. README.md +166 -0
  2. config.json +47 -0
  3. model.safetensors +3 -0
  4. test-logits.npz +3 -0
  5. test-references.npz +3 -0
  6. training_args.bin +3 -0
  7. validation-logits.npz +3 -0
  8. validation-references.npz +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: WinKawaks/vit-small-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: dit-base_tobacco-small_tobacco3482_simkd
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+ results: []
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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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+ # dit-base_tobacco-small_tobacco3482_simkd
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+
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+ This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6962
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+ - Accuracy: 0.85
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+ - Brier Loss: 0.2700
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+ - Nll: 0.9667
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+ - F1 Micro: 0.85
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+ - F1 Macro: 0.8241
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+ - Ece: 0.2479
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+ - Aurc: 0.0379
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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: 16
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+ - eval_batch_size: 16
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 100
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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 | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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+ | No log | 1.0 | 50 | 1.0013 | 0.18 | 0.8965 | 4.5407 | 0.18 | 0.1379 | 0.2160 | 0.6680 |
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+ | No log | 2.0 | 100 | 0.9916 | 0.3 | 0.8871 | 3.1090 | 0.3 | 0.1526 | 0.3057 | 0.4735 |
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+ | No log | 3.0 | 150 | 0.9644 | 0.51 | 0.8433 | 2.4502 | 0.51 | 0.3257 | 0.4499 | 0.2544 |
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+ | No log | 4.0 | 200 | 0.9207 | 0.575 | 0.7585 | 2.1964 | 0.575 | 0.3958 | 0.4563 | 0.2193 |
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+ | No log | 5.0 | 250 | 0.8726 | 0.635 | 0.6620 | 2.3923 | 0.635 | 0.5105 | 0.4321 | 0.1730 |
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+ | No log | 6.0 | 300 | 0.8303 | 0.665 | 0.5604 | 1.4922 | 0.665 | 0.5869 | 0.3717 | 0.1305 |
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+ | No log | 7.0 | 350 | 0.7994 | 0.745 | 0.4490 | 1.3772 | 0.745 | 0.6541 | 0.3557 | 0.0853 |
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+ | No log | 8.0 | 400 | 0.7822 | 0.79 | 0.4124 | 1.2076 | 0.79 | 0.7109 | 0.3035 | 0.0873 |
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+ | No log | 9.0 | 450 | 0.7808 | 0.78 | 0.3955 | 1.5529 | 0.78 | 0.7041 | 0.3123 | 0.0763 |
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+ | 0.8704 | 10.0 | 500 | 0.7923 | 0.695 | 0.4296 | 1.7171 | 0.695 | 0.6150 | 0.3012 | 0.1039 |
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+ | 0.8704 | 11.0 | 550 | 0.7848 | 0.745 | 0.4327 | 1.6327 | 0.745 | 0.6972 | 0.2800 | 0.1321 |
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+ | 0.8704 | 12.0 | 600 | 0.7600 | 0.825 | 0.3579 | 1.2569 | 0.825 | 0.7621 | 0.3015 | 0.0624 |
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+ | 0.8704 | 13.0 | 650 | 0.7570 | 0.79 | 0.3554 | 1.4638 | 0.79 | 0.7706 | 0.2964 | 0.0621 |
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+ | 0.8704 | 14.0 | 700 | 0.7504 | 0.81 | 0.3434 | 1.5597 | 0.81 | 0.7714 | 0.2930 | 0.0589 |
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+ | 0.8704 | 15.0 | 750 | 0.7481 | 0.8 | 0.3439 | 1.3827 | 0.8000 | 0.7641 | 0.2805 | 0.0675 |
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+ | 0.8704 | 16.0 | 800 | 0.7358 | 0.81 | 0.3357 | 1.4522 | 0.81 | 0.7889 | 0.3077 | 0.0610 |
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+ | 0.8704 | 17.0 | 850 | 0.7294 | 0.82 | 0.3179 | 1.0458 | 0.82 | 0.7820 | 0.2909 | 0.0564 |
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+ | 0.8704 | 18.0 | 900 | 0.7229 | 0.815 | 0.3092 | 1.2562 | 0.815 | 0.7862 | 0.2719 | 0.0496 |
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+ | 0.8704 | 19.0 | 950 | 0.7186 | 0.825 | 0.3069 | 1.0425 | 0.825 | 0.7977 | 0.2824 | 0.0558 |
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+ | 0.6968 | 20.0 | 1000 | 0.7156 | 0.83 | 0.3031 | 0.9897 | 0.83 | 0.8039 | 0.2660 | 0.0490 |
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+ | 0.6968 | 21.0 | 1050 | 0.7135 | 0.82 | 0.3014 | 1.0562 | 0.82 | 0.7887 | 0.2745 | 0.0462 |
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+ | 0.6968 | 22.0 | 1100 | 0.7116 | 0.835 | 0.2997 | 0.9822 | 0.835 | 0.8102 | 0.2817 | 0.0452 |
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+ | 0.6968 | 23.0 | 1150 | 0.7114 | 0.82 | 0.3047 | 0.9197 | 0.82 | 0.7937 | 0.2669 | 0.0484 |
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+ | 0.6968 | 24.0 | 1200 | 0.7111 | 0.8 | 0.3032 | 0.9744 | 0.8000 | 0.7690 | 0.2624 | 0.0504 |
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+ | 0.6968 | 25.0 | 1250 | 0.7076 | 0.805 | 0.3025 | 0.9884 | 0.805 | 0.7677 | 0.2538 | 0.0478 |
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+ | 0.6968 | 26.0 | 1300 | 0.7074 | 0.82 | 0.3037 | 0.9954 | 0.82 | 0.7877 | 0.2592 | 0.0496 |
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+ | 0.6968 | 27.0 | 1350 | 0.7053 | 0.825 | 0.2998 | 0.9712 | 0.825 | 0.7885 | 0.2628 | 0.0454 |
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+ | 0.6968 | 28.0 | 1400 | 0.7046 | 0.82 | 0.2936 | 0.9780 | 0.82 | 0.7886 | 0.2573 | 0.0438 |
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+ | 0.6968 | 29.0 | 1450 | 0.7068 | 0.82 | 0.3000 | 0.9943 | 0.82 | 0.7895 | 0.2382 | 0.0447 |
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+ | 0.6551 | 30.0 | 1500 | 0.7045 | 0.83 | 0.2881 | 0.9107 | 0.83 | 0.8010 | 0.2363 | 0.0439 |
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+ | 0.6551 | 31.0 | 1550 | 0.7033 | 0.825 | 0.2936 | 0.9794 | 0.825 | 0.7858 | 0.2556 | 0.0433 |
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+ | 0.6551 | 32.0 | 1600 | 0.7014 | 0.82 | 0.2890 | 0.9799 | 0.82 | 0.7895 | 0.2495 | 0.0418 |
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+ | 0.6551 | 33.0 | 1650 | 0.7020 | 0.815 | 0.2921 | 0.9658 | 0.815 | 0.7820 | 0.2556 | 0.0449 |
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+ | 0.6551 | 34.0 | 1700 | 0.7012 | 0.835 | 0.2885 | 1.0419 | 0.835 | 0.8042 | 0.2581 | 0.0417 |
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+ | 0.6551 | 35.0 | 1750 | 0.7013 | 0.835 | 0.2902 | 0.9773 | 0.835 | 0.8035 | 0.2522 | 0.0435 |
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+ | 0.6551 | 36.0 | 1800 | 0.7016 | 0.825 | 0.2884 | 0.9815 | 0.825 | 0.7851 | 0.2518 | 0.0432 |
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+ | 0.6551 | 37.0 | 1850 | 0.7007 | 0.835 | 0.2888 | 0.9724 | 0.835 | 0.8133 | 0.2486 | 0.0438 |
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+ | 0.6551 | 38.0 | 1900 | 0.6984 | 0.825 | 0.2847 | 0.9650 | 0.825 | 0.7897 | 0.2487 | 0.0415 |
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+ | 0.6551 | 39.0 | 1950 | 0.7001 | 0.84 | 0.2843 | 1.0535 | 0.8400 | 0.8104 | 0.2566 | 0.0418 |
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+ | 0.6381 | 40.0 | 2000 | 0.6990 | 0.825 | 0.2843 | 0.9673 | 0.825 | 0.7963 | 0.2396 | 0.0429 |
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+ | 0.6381 | 49.0 | 2450 | 0.6969 | 0.825 | 0.2747 | 0.9606 | 0.825 | 0.7871 | 0.2478 | 0.0394 |
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+ | 0.6274 | 50.0 | 2500 | 0.6954 | 0.835 | 0.2746 | 0.9572 | 0.835 | 0.8070 | 0.2395 | 0.0406 |
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+ | 0.6274 | 51.0 | 2550 | 0.6972 | 0.835 | 0.2755 | 1.0383 | 0.835 | 0.8070 | 0.2484 | 0.0391 |
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+ | 0.6274 | 52.0 | 2600 | 0.6955 | 0.83 | 0.2752 | 0.9699 | 0.83 | 0.7998 | 0.2562 | 0.0406 |
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+ | 0.6274 | 54.0 | 2700 | 0.6960 | 0.83 | 0.2727 | 0.9646 | 0.83 | 0.7977 | 0.2347 | 0.0399 |
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+ | 0.6207 | 60.0 | 3000 | 0.6939 | 0.84 | 0.2697 | 0.9574 | 0.8400 | 0.8161 | 0.2339 | 0.0378 |
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+ | 0.6207 | 61.0 | 3050 | 0.6952 | 0.84 | 0.2706 | 0.9611 | 0.8400 | 0.8080 | 0.2306 | 0.0379 |
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+ | 0.6207 | 62.0 | 3100 | 0.6940 | 0.835 | 0.2691 | 0.9523 | 0.835 | 0.8086 | 0.2451 | 0.0382 |
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+ | 0.6097 | 90.0 | 4500 | 0.6961 | 0.835 | 0.2701 | 0.9645 | 0.835 | 0.8042 | 0.2444 | 0.0386 |
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+ | 0.6083 | 100.0 | 5000 | 0.6962 | 0.85 | 0.2700 | 0.9667 | 0.85 | 0.8241 | 0.2479 | 0.0379 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.0.dev0
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+ - Pytorch 2.2.0.dev20231112+cu118
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+ - Datasets 2.14.5
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+ - Tokenizers 0.14.1
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+ {
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+ "_name_or_path": "WinKawaks/vit-small-patch16-224",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "hidden_act": "gelu",
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+ "id2label": {
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+ "0": "ADVE",
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+ "1": "Email",
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+ "2": "Form",
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+ "3": "Letter",
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+ "4": "Memo",
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+ "5": "News",
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+ "6": "Note",
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+ "7": "Report",
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+ "8": "Resume",
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+ "9": "Scientific"
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+ },
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+ "Memo": 4,
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+ "News": 5,
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+ "Note": 6,
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+ "Report": 7,
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+ "Resume": 8,
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+ "Scientific": 9
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 6,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.36.0.dev0"
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+ }
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