Model save

README.md

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+---
+library_name: transformers
+license: mit
+base_model: google/vivit-b-16x2-kinetics400
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: ViViT_BdSLW60_FrameRate_Corrected_without_Augment_20_epch
+  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_BdSLW60_FrameRate_Corrected_without_Augment_20_epch
+
+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: 0.6841
+- Accuracy: 0.86
+- Precision: 0.8823
+- Recall: 0.86
+- F1: 0.8452
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 18560
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 3.1124        | 0.0501 | 929  | 1.5155          | 0.705    | 0.7529    | 0.705  | 0.6760 |
+| 0.1216        | 1.0501 | 1858 | 0.6576          | 0.8167   | 0.8197    | 0.8167 | 0.7920 |
+| 0.0048        | 2.0501 | 2787 | 0.4630          | 0.86     | 0.8868    | 0.86   | 0.8338 |
+| 0.0086        | 3.0501 | 3716 | 0.4305          | 0.8733   | 0.8927    | 0.8733 | 0.8600 |
+| 0.2527        | 4.0501 | 4645 | 1.0763          | 0.755    | 0.8136    | 0.755  | 0.7172 |
+| 0.0681        | 5.0501 | 5574 | 0.7836          | 0.8283   | 0.8202    | 0.8283 | 0.8002 |
+| 0.0256        | 6.0501 | 6503 | 0.7197          | 0.8333   | 0.8667    | 0.8333 | 0.8153 |
+| 0.064         | 7.0501 | 7432 | 0.6918          | 0.8533   | 0.8730    | 0.8533 | 0.8374 |
+| 0.0001        | 8.0501 | 8361 | 0.6841          | 0.86     | 0.8823    | 0.86   | 0.8452 |
+
+
+### Framework versions
+
+- Transformers 4.46.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.1

model.safetensors

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