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vivit-b-16x2-kinetics400-ft-3620

Video Classification
Transformers
Safetensors
vivit
Generated from Trainer
Inference Endpoints
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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: google/vivit-b-16x2-kinetics400
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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: vivit-b-16x2-kinetics400-ft-3620
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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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+ # vivit-b-16x2-kinetics400-ft-3620
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+
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+ This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9281
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+ - Accuracy: 0.5566
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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: 8
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+ - eval_batch_size: 8
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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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+ - training_steps: 5500
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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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+ | 1.0684 | 0.0202 | 111 | 1.1114 | 0.3799 |
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+ | 1.0415 | 1.0202 | 222 | 1.0135 | 0.5249 |
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+ | 1.0271 | 2.0202 | 333 | 1.0630 | 0.4857 |
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+ | 1.1609 | 3.0202 | 444 | 1.0203 | 0.4222 |
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+ | 0.9824 | 4.0202 | 555 | 1.0219 | 0.5249 |
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+ | 1.0247 | 5.0202 | 666 | 1.0210 | 0.5026 |
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+ | 1.0824 | 6.0202 | 777 | 0.9947 | 0.4720 |
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+ | 0.943 | 7.0202 | 888 | 1.1085 | 0.4381 |
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+ | 0.8807 | 8.0202 | 999 | 0.9345 | 0.5767 |
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+ | 1.1009 | 9.0202 | 1110 | 0.9855 | 0.5164 |
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+ | 1.0292 | 10.0202 | 1221 | 1.0506 | 0.4339 |
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+ | 0.9071 | 11.0202 | 1332 | 0.9926 | 0.5143 |
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+ | 1.0001 | 12.0202 | 1443 | 1.0406 | 0.4931 |
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+ | 0.9698 | 13.0202 | 1554 | 0.9440 | 0.5598 |
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+ | 0.9405 | 14.0202 | 1665 | 0.9667 | 0.5323 |
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+ | 0.8802 | 15.0202 | 1776 | 0.9011 | 0.5862 |
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+ | 0.9154 | 16.0202 | 1887 | 0.9429 | 0.5598 |
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+ | 0.929 | 17.0202 | 1998 | 0.9948 | 0.5132 |
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+ | 0.9112 | 18.0202 | 2109 | 0.9056 | 0.5852 |
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+ | 0.9202 | 19.0202 | 2220 | 0.9489 | 0.5524 |
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+ | 0.9004 | 20.0202 | 2331 | 0.8995 | 0.5820 |
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+ | 0.9318 | 21.0202 | 2442 | 0.9032 | 0.5958 |
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+ | 0.8493 | 22.0202 | 2553 | 0.9975 | 0.5238 |
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+ | 0.8587 | 23.0202 | 2664 | 1.0142 | 0.5259 |
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+ | 0.958 | 24.0202 | 2775 | 0.9665 | 0.5376 |
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+ | 0.996 | 25.0202 | 2886 | 0.9391 | 0.5704 |
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+ | 0.823 | 26.0202 | 2997 | 0.9171 | 0.5778 |
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+ | 0.8834 | 27.0202 | 3108 | 0.8923 | 0.5873 |
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+ | 0.8615 | 28.0202 | 3219 | 0.9577 | 0.5471 |
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+ | 0.9462 | 29.0202 | 3330 | 0.9468 | 0.5630 |
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+ | 0.8909 | 30.0202 | 3441 | 0.9343 | 0.5672 |
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+ | 0.8048 | 31.0202 | 3552 | 0.9107 | 0.5778 |
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+ | 0.8109 | 32.0202 | 3663 | 0.9547 | 0.5492 |
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+ | 0.9242 | 33.0202 | 3774 | 0.9275 | 0.5598 |
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+ | 0.9046 | 34.0202 | 3885 | 0.9290 | 0.5831 |
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+ | 0.7677 | 35.0202 | 3996 | 0.9208 | 0.5725 |
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+ | 0.8501 | 36.0202 | 4107 | 0.9126 | 0.5810 |
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+ | 0.8468 | 37.0202 | 4218 | 0.9053 | 0.5862 |
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+ | 0.7814 | 38.0202 | 4329 | 0.8858 | 0.5905 |
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+ | 0.9354 | 39.0202 | 4440 | 0.9207 | 0.5725 |
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+ | 0.8849 | 40.0202 | 4551 | 0.9277 | 0.5651 |
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+ | 0.7856 | 41.0202 | 4662 | 0.9130 | 0.5915 |
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+ | 0.7133 | 42.0202 | 4773 | 0.9080 | 0.5884 |
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+ | 0.932 | 43.0202 | 4884 | 0.9388 | 0.5577 |
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+ | 0.6883 | 44.0202 | 4995 | 0.8925 | 0.5937 |
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+ | 0.9944 | 45.0202 | 5106 | 0.9143 | 0.5820 |
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+ | 0.8892 | 46.0202 | 5217 | 0.9103 | 0.5884 |
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+ | 0.9071 | 47.0202 | 5328 | 0.9018 | 0.5905 |
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+ | 0.7943 | 48.0202 | 5439 | 0.9022 | 0.5905 |
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+ | 0.8034 | 49.0111 | 5500 | 0.9004 | 0.5947 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 1.13.0+cu117
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
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