{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-google--vit-large-patch16-384","slug":"google--vit-large-patch16-384","name":"vit-large-patch16-384","type":"model","url":"https://huggingface.co/google/vit-large-patch16-384","page_url":"https://unfragile.ai/google--vit-large-patch16-384","categories":["image-generation"],"tags":["transformers","pytorch","tf","jax","vit","image-classification","vision","dataset:imagenet","dataset:imagenet-21k","arxiv:2010.11929","arxiv:2006.03677","license:apache-2.0","endpoints_compatible","region:us","deploy:azure"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-google--vit-large-patch16-384__cap_0","uri":"capability://image.visual.imagenet.21k.pre.trained.image.classification.with.vision.transformer.architecture","name":"imagenet-21k pre-trained image classification with vision transformer architecture","description":"Performs image classification using a Vision Transformer (ViT) model with large architecture (L/16 configuration) pre-trained on ImageNet-21k dataset containing 14M images across 14k classes. The model divides input images into 16×16 patches, embeds them through linear projection, and processes them through 24 transformer encoder layers with multi-head self-attention (16 heads, 1024 hidden dimensions) to produce class predictions. Achieves 90.88% top-1 accuracy on ImageNet-1k validation set through transfer learning from the larger pre-training corpus.","intents":["Fine-tune a pre-trained vision model on custom image classification tasks with minimal labeled data","Deploy a high-accuracy image classifier that handles diverse object categories from ImageNet-21k knowledge","Extract visual features from images for downstream tasks like image retrieval or clustering","Benchmark vision model performance against state-of-the-art transformer-based baselines"],"best_for":["Computer vision teams building production image classification systems with high accuracy requirements","Researchers prototyping vision transformer applications and comparing against CNN baselines","ML engineers fine-tuning models on domain-specific image datasets (medical imaging, satellite imagery, product catalogs)"],"limitations":["Requires 384×384 input resolution (patch16 design), increasing computational cost vs smaller models like ViT-base","Inference latency ~200-400ms on CPU, requires GPU for real-time applications (>30 FPS)","No built-in support for multi-label classification or bounding box regression — classification-only task","Memory footprint ~1.2GB for model weights, requires 8GB+ GPU VRAM for batch inference","Pre-training on ImageNet-21k may introduce dataset bias toward object-centric, well-lit images"],"requires":["Python 3.7+","PyTorch 1.9+ OR TensorFlow 2.4+ OR JAX/Flax (framework-agnostic via HuggingFace transformers)","transformers library 4.5.0+","Pillow or OpenCV for image preprocessing","GPU with 8GB+ VRAM recommended (NVIDIA CUDA 11.0+ or AMD ROCm 4.0+)","Internet connection for initial model download (~1.2GB)"],"input_types":["image/jpeg","image/png","image/webp","numpy arrays (H×W×3 uint8 or float32)","PIL Image objects","torch.Tensor (B×3×384×384)"],"output_types":["logits (B×1000 float32 for ImageNet-1k classes)","class probabilities (B×1000 softmax normalized)","top-k predictions with confidence scores","hidden states from intermediate layers (B×577×1024 for feature extraction)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google--vit-large-patch16-384__cap_1","uri":"capability://tool.use.integration.multi.framework.model.serialization.and.inference.abstraction","name":"multi-framework model serialization and inference abstraction","description":"Provides unified model loading and inference interface across PyTorch, TensorFlow, and JAX backends through HuggingFace transformers library abstraction layer. Model weights are stored in safetensors format (binary serialization with built-in integrity checks) and automatically converted to framework-specific formats on first load. Supports dynamic batching, mixed-precision inference (fp16, int8 quantization), and device placement (CPU/GPU/TPU) through a single Python API without framework-specific code changes.","intents":["Load and run the same model across different ML frameworks without rewriting inference code","Deploy models in resource-constrained environments using quantization and mixed-precision inference","Integrate the model into existing PyTorch, TensorFlow, or JAX production pipelines seamlessly","Benchmark inference performance across frameworks on the same hardware"],"best_for":["ML teams with heterogeneous infrastructure (some PyTorch, some TensorFlow services)","Edge deployment engineers optimizing for latency and memory on mobile/IoT devices","Researchers comparing framework performance without reimplementing models"],"limitations":["Framework conversion adds ~5-10 second overhead on first load (model compilation and optimization)","JAX backend requires explicit jit compilation for production inference; no automatic graph optimization","Mixed-precision inference (fp16) may reduce accuracy by 0.5-1.5% on ImageNet-1k depending on quantization method","No built-in support for model sharding across multiple GPUs — requires external distributed inference framework"],"requires":["transformers 4.5.0+","One of: torch 1.9+, tensorflow 2.4+, or jax 0.2.0+","safetensors 0.3.0+ for safe model loading"],"input_types":["PIL Image objects","numpy arrays","torch.Tensor","tensorflow.Tensor","jax.numpy arrays"],"output_types":["torch.Tensor (PyTorch backend)","tensorflow.Tensor (TensorFlow backend)","jax.numpy array (JAX backend)","transformers.ImageClassifierOutput (unified output object)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google--vit-large-patch16-384__cap_2","uri":"capability://image.visual.transfer.learning.with.fine.tuning.on.custom.image.datasets","name":"transfer learning with fine-tuning on custom image datasets","description":"Enables efficient fine-tuning of the pre-trained ViT-large model on custom image classification tasks by freezing early transformer layers and training only the final classification head and optional adapter layers. Implements gradient checkpointing to reduce memory usage during backpropagation, supports mixed-precision training (automatic loss scaling), and provides learning rate scheduling strategies (warmup, cosine annealing) optimized for vision transformer training. Typical fine-tuning requires 100-1000 labeled examples per class and converges in 10-50 epochs depending on dataset size and task complexity.","intents":["Adapt the model to classify custom object categories (e.g., product types, disease variants) with limited labeled data","Reduce training time and computational cost by leveraging ImageNet-21k pre-training instead of training from scratch","Implement domain-specific image classification (medical imaging, satellite imagery, industrial defect detection) with minimal data annotation","Achieve high accuracy on niche classification tasks without building a custom dataset of millions of images"],"best_for":["Product teams building image classification features with domain-specific categories (100-1000 classes)","Healthcare/biotech researchers fine-tuning for medical image analysis with limited annotated datasets","Enterprise ML teams deploying custom classifiers for internal use cases (quality control, content moderation)"],"limitations":["Fine-tuning on small datasets (<1000 images) risks overfitting; requires aggressive regularization (dropout, weight decay, early stopping)","Requires 16GB+ GPU VRAM for full fine-tuning with batch size 32; gradient checkpointing reduces to 8GB but adds ~20% training time","Pre-training bias toward ImageNet-21k object categories may not transfer well to abstract, non-visual tasks (e.g., classifying text documents by appearance alone)","No built-in support for class imbalance handling — requires manual loss weighting or data augmentation strategies"],"requires":["Python 3.7+","PyTorch 1.9+ with CUDA 11.0+ (TensorFlow/JAX fine-tuning less documented)","transformers 4.5.0+","torch.optim and torch.nn for training loop implementation","GPU with 8GB+ VRAM (16GB recommended for batch size >16)","Custom dataset with image files and class labels (CSV, JSON, or directory structure)"],"input_types":["image/jpeg, image/png files","numpy arrays (H×W×3)","PIL Image objects","PyTorch DataLoader with custom Dataset class"],"output_types":["fine-tuned model weights (safetensors format)","training logs (loss, accuracy, validation metrics)","class predictions on test set","confusion matrix and per-class metrics"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google--vit-large-patch16-384__cap_3","uri":"capability://image.visual.feature.extraction.and.embedding.generation.for.downstream.tasks","name":"feature extraction and embedding generation for downstream tasks","description":"Extracts intermediate representations (hidden states) from transformer layers to generate fixed-size image embeddings (1024-dimensional vectors from the final layer's [CLS] token) for use in downstream tasks like image retrieval, clustering, or similarity search. Supports extracting features from any intermediate layer (not just the final layer), enabling multi-scale feature hierarchies. Embeddings are normalized L2 vectors suitable for cosine similarity computation and can be indexed in vector databases (Faiss, Milvus, Pinecone) for efficient nearest-neighbor search at scale.","intents":["Build image search systems that find visually similar products, documents, or media without explicit labels","Generate embeddings for clustering images into semantic groups (e.g., grouping product variants by visual similarity)","Create image-to-image recommendation systems by computing similarity between embedding vectors","Reduce dimensionality of image data for downstream ML tasks (classification, anomaly detection) using pre-trained features"],"best_for":["E-commerce platforms building visual search and product recommendation features","Content moderation teams clustering similar images for efficient review workflows","Researchers building image retrieval benchmarks and evaluating embedding quality"],"limitations":["Embeddings are task-agnostic (trained on ImageNet-21k); may not capture domain-specific visual properties without fine-tuning","1024-dimensional embeddings require ~4KB storage per image; scaling to billions of images requires distributed vector database infrastructure","Cosine similarity in high-dimensional space suffers from curse of dimensionality; retrieval quality degrades with very large databases (>100M images) without approximate nearest-neighbor methods","No built-in support for temporal or sequential image analysis — treats each image independently"],"requires":["Python 3.7+","transformers 4.5.0+","PyTorch 1.9+ or TensorFlow 2.4+","Optional: Faiss, Milvus, or Pinecone for vector indexing at scale","GPU recommended for batch embedding generation (CPU inference ~100-200ms per image)"],"input_types":["image/jpeg, image/png files","PIL Image objects","numpy arrays (H×W×3)","batch of images (B×3×384×384 tensors)"],"output_types":["embeddings (B×1024 float32 tensors, L2-normalized)","hidden states from intermediate layers (B×577×1024 for all tokens)","similarity matrices (B×B cosine similarity scores)","nearest neighbor indices and distances"],"categories":["image-visual","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google--vit-large-patch16-384__cap_4","uri":"capability://image.visual.batch.inference.with.dynamic.padding.and.variable.size.image.handling","name":"batch inference with dynamic padding and variable-size image handling","description":"Processes multiple images of varying sizes in a single batch by automatically resizing and padding them to the fixed 384×384 input resolution required by the ViT-large model. Implements efficient batching through PyTorch DataLoader or TensorFlow Dataset APIs with configurable batch sizes (typically 8-64 depending on GPU memory). Supports asynchronous data loading and preprocessing on CPU while GPU performs inference, achieving near-optimal GPU utilization. Returns predictions for all images in batch simultaneously, reducing per-image inference latency through amortization.","intents":["Process large image collections (thousands to millions) efficiently for classification or feature extraction","Deploy the model in production services handling variable-size image uploads without manual preprocessing","Maximize GPU throughput by batching inference requests and overlapping data loading with computation","Build data pipelines that automatically handle diverse image formats and resolutions"],"best_for":["Backend services processing image uploads at scale (e-commerce, social media, cloud storage)","Batch processing pipelines analyzing large image datasets (satellite imagery, medical imaging archives)","ML inference servers (TorchServe, TensorFlow Serving) handling concurrent requests"],"limitations":["Fixed 384×384 resolution may distort aspect ratios of very wide or tall images; padding adds black borders affecting model predictions on edge cases","Batch size is limited by GPU memory; typical maximum batch size 64 on 16GB GPU, 32 on 8GB GPU","Dynamic batching adds latency variance (p99 latency depends on batch size); not suitable for strict real-time SLAs (<50ms)","No built-in support for streaming inference or online batching across multiple requests"],"requires":["Python 3.7+","PyTorch 1.9+ with DataLoader or TensorFlow 2.4+ with tf.data.Dataset","transformers 4.5.0+","GPU with 8GB+ VRAM for batch size >16","Image preprocessing library (Pillow, OpenCV, torchvision.transforms)"],"input_types":["list of PIL Image objects","list of image file paths","numpy arrays with variable heights/widths","PyTorch DataLoader yielding batches","TensorFlow Dataset yielding batches"],"output_types":["batch predictions (B×1000 logits or probabilities)","batch embeddings (B×1024 features)","per-image confidence scores and top-k class predictions","inference timing metrics (latency per image, throughput)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google--vit-large-patch16-384__cap_5","uri":"capability://image.visual.model.quantization.and.optimization.for.edge.deployment","name":"model quantization and optimization for edge deployment","description":"Supports post-training quantization (INT8, INT4) and knowledge distillation to reduce model size from 1.2GB to 300-600MB while maintaining 1-2% accuracy loss. Enables deployment on edge devices (mobile phones, embedded systems, IoT devices) with limited memory and compute. Implements quantization-aware training (QAT) through PyTorch's quantization API and supports ONNX export for cross-platform inference on mobile runtimes (CoreML, TensorFlow Lite, ONNX Runtime). Typical inference latency on mobile GPU: 500-1000ms per image (vs 200-400ms on desktop GPU).","intents":["Deploy image classification to mobile apps and edge devices without cloud inference","Reduce model serving costs by decreasing model size and memory requirements","Enable on-device privacy-preserving image analysis without sending images to cloud servers","Optimize inference latency for real-time mobile applications (camera-based classification)"],"best_for":["Mobile app developers building on-device image classification features","IoT/embedded systems engineers deploying vision models on resource-constrained hardware","Privacy-focused applications requiring local inference without cloud connectivity"],"limitations":["INT8 quantization reduces accuracy by 1-2% on ImageNet-1k; INT4 quantization may reduce accuracy by 3-5%","Quantized models require framework-specific optimization (PyTorch, TensorFlow, ONNX); no universal quantized format","Mobile inference latency (500-1000ms) is too slow for real-time video processing (>30 FPS); suitable for single-image classification only","Knowledge distillation requires training a smaller student model; adds complexity to deployment pipeline","ONNX export may lose some model features (e.g., custom layers); requires testing on target platform"],"requires":["Python 3.7+","PyTorch 1.9+ with quantization support or TensorFlow 2.4+","transformers 4.5.0+","ONNX and onnx-simplifier for model export","Mobile framework: CoreML (iOS), TensorFlow Lite (Android), or ONNX Runtime","Target device with 512MB+ RAM and 300MB+ storage"],"input_types":["PIL Image objects","numpy arrays (H×W×3)","quantized model checkpoint (INT8/INT4)"],"output_types":["quantized model weights (300-600MB)","ONNX model file for mobile deployment","CoreML or TensorFlow Lite model bundle","quantization statistics (per-layer bit-width, scale factors)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ OR TensorFlow 2.4+ OR JAX/Flax (framework-agnostic via HuggingFace transformers)","transformers library 4.5.0+","Pillow or OpenCV for image preprocessing","GPU with 8GB+ VRAM recommended (NVIDIA CUDA 11.0+ or AMD ROCm 4.0+)","Internet connection for initial model download (~1.2GB)","transformers 4.5.0+","One of: torch 1.9+, tensorflow 2.4+, or jax 0.2.0+","safetensors 0.3.0+ for safe model loading","PyTorch 1.9+ with CUDA 11.0+ (TensorFlow/JAX fine-tuning less documented)"],"failure_modes":["Requires 384×384 input resolution (patch16 design), increasing computational cost vs smaller models like ViT-base","Inference latency ~200-400ms on CPU, requires GPU for real-time applications (>30 FPS)","No built-in support for multi-label classification or bounding box regression — classification-only task","Memory footprint ~1.2GB for model weights, requires 8GB+ GPU VRAM for batch inference","Pre-training on ImageNet-21k may introduce dataset bias toward object-centric, well-lit images","Framework conversion adds ~5-10 second overhead on first load (model compilation and optimization)","JAX backend requires explicit jit compilation for production inference; 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