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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Human-Action-Recognition-VIT-Base-patch16-224 |
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results: [] |
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datasets: |
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- Bingsu/Human_Action_Recognition |
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language: |
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- en |
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pipeline_tag: image-classification |
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--- |
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# Human-Action-Recognition-VIT-Base-patch16-224 |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on [Bingsu/Human_Action_Recognition](https://huggingface.co/datasets/Bingsu/Human_Action_Recognition) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4005 |
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- Accuracy: 0.8786 |
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## Model description |
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The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. |
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Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. |
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By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. |
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## Intended uses & limitations |
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You can use the model for image classification. |
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### How to use |
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Here is how to use this model to classify an image of the human action into one of the following categories: |
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calling, clapping, cycling, dancing, drinking, eating, fighting, hugging, laughing, listening_to_music, running, sitting, sleeping, texting, using_laptop |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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pipe = pipeline("image-classification", "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224") |
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url = "https://images.pexels.com/photos/175658/pexels-photo-175658.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500" |
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image = Image.open(requests.get(url, stream=True).raw) |
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pipe(image) |
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# Output: |
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# [{'score': 0.9918079972267151, 'label': 'dancing'}, |
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# {'score': 0.00207977625541389, 'label': 'clapping'}, |
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# {'score': 0.0015223610680550337, 'label': 'running'}, |
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# {'score': 0.0009153694845736027, 'label': 'fighting'}, |
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# {'score': 0.0006987180095165968, 'label': 'sitting'}] |
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``` |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.6396 | 0.99 | 39 | 2.0436 | 0.4425 | |
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| 1.4579 | 2.0 | 79 | 0.7553 | 0.7917 | |
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| 0.8342 | 2.99 | 118 | 0.5296 | 0.8417 | |
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| 0.6649 | 4.0 | 158 | 0.4978 | 0.8496 | |
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| 0.6137 | 4.99 | 197 | 0.4460 | 0.8595 | |
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| 0.5374 | 6.0 | 237 | 0.4356 | 0.8627 | |
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| 0.514 | 6.99 | 276 | 0.4349 | 0.8615 | |
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| 0.475 | 8.0 | 316 | 0.4005 | 0.8786 | |
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| 0.4663 | 8.99 | 355 | 0.4164 | 0.8659 | |
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| 0.4178 | 10.0 | 395 | 0.4128 | 0.8738 | |
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| 0.4226 | 10.99 | 434 | 0.4115 | 0.8690 | |
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| 0.3896 | 12.0 | 474 | 0.4112 | 0.875 | |
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| 0.3866 | 12.99 | 513 | 0.4072 | 0.8714 | |
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| 0.3632 | 14.0 | 553 | 0.4106 | 0.8718 | |
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| 0.3596 | 14.99 | 592 | 0.4043 | 0.8714 | |
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| 0.3421 | 16.0 | 632 | 0.4128 | 0.8675 | |
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| 0.344 | 16.99 | 671 | 0.4181 | 0.8643 | |
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| 0.3447 | 18.0 | 711 | 0.4128 | 0.8687 | |
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| 0.3407 | 18.99 | 750 | 0.4097 | 0.8714 | |
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| 0.3267 | 19.75 | 780 | 0.4097 | 0.8683 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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## Fine-tuning script |
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[Google Colaboratory Notebook](https://colab.research.google.com/drive/1YELczSv8r0znzcOKJ4Lt-ecP-aNqk7NV?usp=sharing) |
