| | --- |
| | license: apache-2.0 |
| | base_model: microsoft/resnet-18 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: Action_agent_small_34_class |
| | results: |
| | - task: |
| | name: Image Classification |
| | type: image-classification |
| | dataset: |
| | name: imagefolder |
| | type: imagefolder |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.09523809523809523 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # Action_agent_small_34_class |
| |
|
| | This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: nan |
| | - Accuracy: 0.0952 |
| |
|
| | ## 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: 1e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 0.0 | 0.32 | 100 | nan | 0.0952 | |
| | | 0.0 | 0.64 | 200 | nan | 0.0952 | |
| | | 0.0 | 0.96 | 300 | nan | 0.0952 | |
| | | 0.0 | 1.27 | 400 | nan | 0.0952 | |
| | | 0.0 | 1.59 | 500 | nan | 0.0952 | |
| | | 0.0 | 1.91 | 600 | nan | 0.0952 | |
| | | 0.0 | 2.23 | 700 | nan | 0.0952 | |
| | | 0.0 | 2.55 | 800 | nan | 0.0952 | |
| | | 0.0 | 2.87 | 900 | nan | 0.0952 | |
| | | 0.0 | 3.18 | 1000 | nan | 0.0952 | |
| | | 0.0 | 3.5 | 1100 | nan | 0.0952 | |
| | | 0.0 | 3.82 | 1200 | nan | 0.0952 | |
| | | 0.0 | 4.14 | 1300 | nan | 0.0952 | |
| | | 0.0 | 4.46 | 1400 | nan | 0.0952 | |
| | | 0.0 | 4.78 | 1500 | nan | 0.0952 | |
| | | 0.0 | 5.1 | 1600 | nan | 0.0952 | |
| | | 0.0 | 5.41 | 1700 | nan | 0.0952 | |
| | | 0.0 | 5.73 | 1800 | nan | 0.0952 | |
| | | 0.0 | 6.05 | 1900 | nan | 0.0952 | |
| | | 0.0 | 6.37 | 2000 | nan | 0.0952 | |
| | | 0.0 | 6.69 | 2100 | nan | 0.0952 | |
| | | 0.0 | 7.01 | 2200 | nan | 0.0952 | |
| | | 0.0 | 7.32 | 2300 | nan | 0.0952 | |
| | | 0.0 | 7.64 | 2400 | nan | 0.0952 | |
| | | 0.0 | 7.96 | 2500 | nan | 0.0952 | |
| | | 0.0 | 8.28 | 2600 | nan | 0.0952 | |
| | | 0.0 | 8.6 | 2700 | nan | 0.0952 | |
| | | 0.0 | 8.92 | 2800 | nan | 0.0952 | |
| | | 0.0 | 9.24 | 2900 | nan | 0.0952 | |
| | | 0.0 | 9.55 | 3000 | nan | 0.0952 | |
| | | 0.0 | 9.87 | 3100 | nan | 0.0952 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.39.3 |
| | - Pytorch 2.1.2 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.15.2 |
| |
|
