Model save

README.md

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+---
+license: cc-by-nc-4.0
+base_model: MCG-NJU/videomae-base-finetuned-kinetics
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: videomae-base-finetuned-kinetics-finetuned-conflab-traj-direction-lh-v1
+  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. -->
+
+# videomae-base-finetuned-kinetics-finetuned-conflab-traj-direction-lh-v1
+
+This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.3609
+- Accuracy: 0.6096
+
+## 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: 8
+- eval_batch_size: 8
+- 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: 728
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 1.6395        | 0.1442 | 105  | 1.6255          | 0.3723   |
+| 1.4009        | 1.1442 | 210  | 1.4288          | 0.5053   |
+| 1.0856        | 2.1442 | 315  | 1.2735          | 0.5745   |
+| 0.6975        | 3.1442 | 420  | 1.2629          | 0.6064   |
+| 0.3574        | 4.1442 | 525  | 1.2030          | 0.6170   |
+| 0.1654        | 5.1442 | 630  | 1.2649          | 0.6436   |
+| 0.1179        | 6.1346 | 728  | 1.2939          | 0.6330   |
+
+
+### Framework versions
+
+- Transformers 4.41.0
+- Pytorch 1.12.0+cu116
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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