Overview

Rorshark ViT Base is a Vision Transformer-based image classification model designed for developers needing a balance between inference speed and classification accuracy. Built on the ViT-Base architecture, it leverages attention mechanisms to capture global dependencies across images, making it a viable alternative to traditional CNNs for high-dimensional visual data. It is particularly suited for integration into automated tagging pipelines, visual search indexing, or as a feature extractor for downstream computer vision tasks. With an Apache-2.0 license, it offers full flexibility for commercial deployment and modification. Devs will find it compatible with standard PyTorch/Hugging Face workflows, allowing for straightforward fine-tuning on domain-specific datasets without extensive architectural overhead.

Highlights

Efficient Vision Transformer architecture for image classification
Permissive Apache-2.0 license for commercial production
Seamless integration with standard ML frameworks
Strong global context capture compared to CNNs
Optimized for fine-tuning on specialized visual datasets

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("amunchet/rorshark-vit-base")
tokenizer = AutoTokenizer.from_pretrained("amunchet/rorshark-vit-base")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download amunchet/rorshark-vit-base

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download amunchet/rorshark-vit-base config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('amunchet/rorshark-vit-base')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/amunchet/rorshark-vit-base

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/amunchet/rorshark-vit-base

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('amunchet/rorshark-vit-base')
tokenizer = AutoTokenizer.from_pretrained('amunchet/rorshark-vit-base')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:

image-classification

vision

generated_from_trainer

datasets:

imagefolder

metrics:

accuracy

model-index:

name: rorshark-vit-base

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: 0.9922928709055877
---