End of training

README.md

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+---
+license: mit
+base_model: google/vivit-b-16x2-kinetics400
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: vivit-b-16x2-kinetics400-0513-O_M
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vivit-b-16x2-kinetics400-0513-O_M
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 1.0697
+- Accuracy: 0.805
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 2900
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.5951        | 0.1   | 290  | 1.5856          | 0.45     |
+| 1.1484        | 1.1   | 580  | 0.9889          | 0.65     |
+| 0.436         | 2.1   | 870  | 0.7230          | 0.77     |
+| 0.1011        | 3.1   | 1160 | 1.0218          | 0.78     |
+| 0.0631        | 4.1   | 1450 | 1.0562          | 0.805    |
+| 0.0005        | 5.1   | 1740 | 1.0855          | 0.805    |
+| 0.0004        | 6.1   | 2030 | 1.2053          | 0.785    |
+| 0.0005        | 7.1   | 2320 | 1.1131          | 0.8      |
+| 0.1483        | 8.1   | 2610 | 1.0447          | 0.81     |
+| 0.0013        | 9.1   | 2900 | 1.0697          | 0.805    |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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