Harsha901
/

vit-human-pose-classification-model

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+- image-classification
+- vision-transformer
+- computer-vision
+- human-action-recognition
+license: mit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google/vit-base-patch16-224-in21k
+pipeline_tag: image-classification
 ---
+# ViT Human Action Recognition Model
+This is a fine-tuned Vision Transformer (ViT) model for image-based multi-class human action recognition. The model predicts one of 15 common human activities such as "running", "eating", "texting", or "using a laptop" from static images.
+---
 ## Model Details
+- **Base Model:** [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k)
+- **Developed by:** Harsha Vardhan Mannem
+- **Task:** Image Classification
+- **Architecture:** Vision Transformer (ViT)
+- **Total Classes:** 15
+- **Language:** N/A (Vision Model)
+- **License:** MIT
+---
+## Supported Classes
+The model predicts the following 15 activities:
+- `calling`
+- `clapping`
+- `cycling`
+- `dancing`
+- `drinking`
+- `eating`
+- `fighting`
+- `hugging`
+- `laughing`
+- `listening_to_music`
+- `running`
+- `sitting`
+- `sleeping`
+- `texting`
+- `using_laptop`
+---
+## Intended Use
 ### Direct Use
+- Predicts human activity from a single image
+- Useful for research, prototypes, computer vision pipelines involving human action detection
+### Limitations & Risks
+- Not designed for multi-label scenarios (only one action per image)
+- Not intended for video recognition tasks
+- Accuracy may degrade with poor lighting, extreme angles, or occlusions
+- Model may reflect biases present in the training data
+---
+## Quickstart Example
+```python
+from transformers import pipeline
+pipe = pipeline("image-classification", model="harsha90145/vit-human-pose-classification-model")
+url = "https://images.pexels.com/photos/1755385/pexels-photo-1755385.jpeg"
+output = pipe(url)
+print(output)
+Example Output:
+[{'label': 'running', 'score': 0.92}]
+## Evaluation Results
+Test set size: 2520 samples
+Class	Precision	Recall	F1-Score	Support
+calling	0.66	0.69	0.67	159
+clapping	0.82	0.81	0.82	191
+cycling	0.94	0.92	0.93	167
+dancing	0.91	0.83	0.87	155
+drinking	0.79	0.82	0.81	170
+eating	0.83	0.86	0.85	169
+fighting	0.85	0.89	0.87	154
+hugging	0.77	0.81	0.79	149
+laughing	0.80	0.77	0.78	176
+listening_to_music	0.75	0.65	0.70	179
+running	0.80	0.88	0.84	159
+sitting	0.65	0.67	0.66	166
+sleeping	0.80	0.81	0.81	167
+texting	0.67	0.66	0.66	179
+using_laptop	0.72	0.69	0.71	180
+Overall Accuracy: 78%
+Macro F1 Score: 78%
+Weighted F1 Score: 78%
+@misc{harsha2024humanactionvit,
+  title={ViT-based Human Action Recognition Model},
+  author={Harsha Vardhan Mannem},
+  howpublished={\url{https://huggingface.co/harsha90145/vit-human-pose-classification-model}},
+  year={2024}
+}