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-kinetics400-0511-mediapipe
+  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-kinetics400-0511-mediapipe
+
+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: 0.9073
+- Accuracy: 0.82
+
+## 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: 1400
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.2076        | 0.1   | 140  | 2.4158          | 0.15     |
+| 1.184         | 1.1   | 280  | 1.2269          | 0.6      |
+| 0.6475        | 2.1   | 420  | 0.7247          | 0.76     |
+| 0.3137        | 3.1   | 560  | 0.7076          | 0.78     |
+| 0.0356        | 4.1   | 700  | 0.7347          | 0.82     |
+| 0.0017        | 5.1   | 840  | 0.8888          | 0.83     |
+| 0.0007        | 6.1   | 980  | 0.9464          | 0.8      |
+| 0.265         | 7.1   | 1120 | 1.0068          | 0.8      |
+| 0.0012        | 8.1   | 1260 | 0.8982          | 0.82     |
+| 0.0007        | 9.1   | 1400 | 0.9073          | 0.82     |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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