


# Human-Action-Recognition-VIT-Base-patch16-224

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.  
It achieves the following results on the evaluation set:
- Loss: 0.4005
- Accuracy: 0.8786

## Model description

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.

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.

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.

## Intended uses & limitations

You can use the model for image classification.

### How to use

Here is how to use this model to classify an image of the human action into one of the following categories:  
calling, clapping, cycling, dancing, drinking, eating, fighting, hugging, laughing, listening_to_music, running, sitting, sleeping, texting, using_laptop

```python
from transformers import pipeline
from PIL import Image
import requests

pipe = pipeline("image-classification", "rvv-karma/Human-Action-Recognition-VIT-Base-patch16-224")
url = "https://images.pexels.com/photos/175658/pexels-photo-175658.jpeg?auto=compress&cs=tinysrgb&dpr=1&w=500"
image = Image.open(requests.get(url, stream=True).raw)
pipe(image)

# Output:
# [{'score': 0.9918079972267151, 'label': 'dancing'},
#  {'score': 0.00207977625541389, 'label': 'clapping'},
#  {'score': 0.0015223610680550337, 'label': 'running'},
#  {'score': 0.0009153694845736027, 'label': 'fighting'},
#  {'score': 0.0006987180095165968, 'label': 'sitting'}]
```

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.6396        | 0.99  | 39   | 2.0436          | 0.4425   |
| 1.4579        | 2.0   | 79   | 0.7553          | 0.7917   |
| 0.8342        | 2.99  | 118  | 0.5296          | 0.8417   |
| 0.6649        | 4.0   | 158  | 0.4978          | 0.8496   |
| 0.6137        | 4.99  | 197  | 0.4460          | 0.8595   |
| 0.5374        | 6.0   | 237  | 0.4356          | 0.8627   |
| 0.514         | 6.99  | 276  | 0.4349          | 0.8615   |
| 0.475         | 8.0   | 316  | 0.4005          | 0.8786   |
| 0.4663        | 8.99  | 355  | 0.4164          | 0.8659   |
| 0.4178        | 10.0  | 395  | 0.4128          | 0.8738   |
| 0.4226        | 10.99 | 434  | 0.4115          | 0.8690   |
| 0.3896        | 12.0  | 474  | 0.4112          | 0.875    |
| 0.3866        | 12.99 | 513  | 0.4072          | 0.8714   |
| 0.3632        | 14.0  | 553  | 0.4106          | 0.8718   |
| 0.3596        | 14.99 | 592  | 0.4043          | 0.8714   |
| 0.3421        | 16.0  | 632  | 0.4128          | 0.8675   |
| 0.344         | 16.99 | 671  | 0.4181          | 0.8643   |
| 0.3447        | 18.0  | 711  | 0.4128          | 0.8687   |
| 0.3407        | 18.99 | 750  | 0.4097          | 0.8714   |
| 0.3267        | 19.75 | 780  | 0.4097          | 0.8683   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0


## Fine-tuning script

[Google Colaboratory Notebook](https://colab.research.google.com/drive/1YELczSv8r0znzcOKJ4Lt-ecP-aNqk7NV?usp=sharing)
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Bingsu/Human_Action_Recognition"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "pipeline_tag": "image-classification", "model-index": [{"name": "Human-Action-Recognition-VIT-Base-patch16-224", "results": []}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer en dataset:Bingsu/Human_Action_Recognition base_model:google/vit-base-patch16-224 license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us