🧠
Vit Base Patch16 224 In21k Landscape Recognition model by DunnBC22
⭐ 37.5
🧠
Model

Vit Base Patch16 224 In21k Landscape Recognition

by DunnBC22 hf-model--dunnbc22--vit-base-patch16-224-in21k-landscape_recognition
Nexus Index
37.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 4
R: Recency 95
Q: Quality 50
Tech Context
21.504K Ctx
Vital Performance
37 DL / 30D
0.0%
Source →
Audited 37.5 FNI Score
Tiny - Params
21k Context
37 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--dunnbc22--vit-base-patch16-224-in21k-landscape_recognition
License Apache-2.0
Provider huggingface
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__dunnbc22__vit_base_patch16_224_in21k_landscape_recognition,
  author = {DunnBC22},
  title = {Vit Base Patch16 224 In21k Landscape Recognition Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/dunnbc22/vit-base-patch16-224-in21k-landscape_recognition}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
DunnBC22. (2026). Vit Base Patch16 224 In21k Landscape Recognition [Model]. Free2AITools. https://huggingface.co/dunnbc22/vit-base-patch16-224-in21k-landscape_recognition

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download dunnbc22/vit-base-patch16-224-in21k-landscape_recognition
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 4
Recency (R) 95
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Vit Base Patch16 224 In21k Landscape Recognition: Semantic (S:50), Authority (A:0), Popularity (P:4), Recency (R:95), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Technical Deep Dive

vit-base-patch16-224-in21k-Landscape_Recognition

This model is a fine-tuned version of google/vit-base-patch16-224-in21k.

It achieves the following results on the evaluation set:

  • Loss: 0.4648
  • Accuracy: 0.8687
  • F1
    • Weighted: 0.8694
    • Micro: 0.8687
    • Macro: 0.8694
  • Recall
    • Weighted: 0.8687
    • Micro: 0.8687
    • Macro: 0.8687
  • Precision
    • Weighted: 0.8714
    • Micro: 0.8687
    • Macro: 0.8714

Model description

This is a multiclass image classification model of different types of landscaping.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Landscape%20Recognition/Landscape_Recognition_ViT.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/utkarshsaxenadn/landscape-recognition-image-dataset-12k-images

Sample Images From Dataset:

Sample Images

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy Weighted F1 Micro F1 Macro F1 Weighted Recall Micro Recall Macro Recall Weighted Precision Micro Precision Macro Precision
0.2866 1.0 625 0.4308 0.8487 0.8538 0.8487 0.8538 0.8487 0.8487 0.8487 0.8700 0.8487 0.8700
0.1522 2.0 1250 0.4648 0.8687 0.8694 0.8687 0.8694 0.8687 0.8687 0.8687 0.8714 0.8687 0.8714
0.0609 3.0 1875 0.5122 0.866 0.8678 0.866 0.8678 0.866 0.866 0.866 0.8710 0.866 0.8710

Framework versions

  • Transformers 4.27.4
  • Pytorch 2.0.0
  • Datasets 2.11.0
  • Tokenizers 0.13.3

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

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37Downloads
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--dunnbc22--vit-base-patch16-224-in21k-landscape_recognition
slug
dunnbc22--vit-base-patch16-224-in21k-landscape_recognition
source
huggingface
author
DunnBC22
license
Apache-2.0
tags
transformers, pytorch, tensorboard, vit, image-classification, generated_from_trainer, landscape, en, license:apache-2.0, model-index, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
21,504
pipeline tag
image-classification

📊 Engagement & Metrics

downloads
37
stars
0
forks
0

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