---
license: mit
library_name: pytorch
tags:
  - foundation
  - amd
  - rocm
  - clip
  - zero-shot-image-classification
pipeline_tag: zero-shot-image-classification
---

![Image](https://huggingface.co/BlankHead/ModelCard_T1/resolve/main/Image.png)

# CLIP: Optimized for AMD ROCm

CLIP (Contrastive Language-Image Pre-training) performs zero-shot image classification by comparing image embeddings against text prompt embeddings. This repository packages CLIP evaluation on CIFAR-10 using **PyTorch** (CPU or GPU via OpenAI CLIP) and **vLLM** (GPU server with pooling runner, CPU HTTP client), exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs and CPUs.

This is based on the implementation of CLIP found [here](https://github.com/openai/CLIP).
This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [<repo-name> AMD scripts](<github-link>) to reproduce results or export with custom configurations. More details on model performance can be found [here](#performance-summary).

---

## Task Overview

**Task:** Zero-shot image classification

**Dataset:** CIFAR-10 test split (10,000 images, 10 classes)

**Output metrics:** Zero-shot accuracy (%)

> **Quick Start:** Before running this example, complete the main repository setup — see **[Prerequisites](<github-link>#prerequisites)**, **[Configure System Paths](<github-link>#2-configure-system-paths)**, and **[Virtual Environments](<github-link>#3-virtual-environments)** in the [main README](<github-link>).

> **Model variants:** Default is **base32** (`openai/clip-vit-base-patch32` / `ViT-B/32`). Override with `MODEL_VARIANT=base16|large14|large14-336`.

> **vLLM note:** vLLM's CLIP backend embeds **one modality per request** — text prompts and images are sent in separate API calls, then cosine similarity is computed client-side (same approach as the original evaluation scripts).

---

## AMD ROCm Optimization

This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs. Key points:

- Exported/tested with ROCm `<rocm-version>` and PyTorch ROCm build `<torch-rocm-version>`.
- Validated backends: **PyTorch** (native ROCm HIP kernels) and **vLLM** (ROCm-enabled server build).
- No code changes required versus the upstream OpenAI CLIP implementation — only environment/runtime configuration differs.
- CPU fallback path supported for environments without a ROCm-capable GPU.

| Runtime | Precision | Backend | Hardware | Notes |
|---|---|---|---|---|
| PyTorch | fp32/fp16 | HIP (ROCm) | AMD Instinct / Radeon GPU | Native OpenAI CLIP inference |
| PyTorch | fp32 | CPU | AMD CPU (EPYC/Ryzen) | CPU-only fallback |
| vLLM | fp16 | ROCm server | AMD Instinct GPU | Pooling runner, image-only requests |
| vLLM (client) | — | HTTP | AMD CPU | Text-prompt requests, CPU client |

---

## Getting Started

### Option 1: Use Provided Scripts

Pre-configured evaluation scripts are available for direct use on ROCm hardware. See [Quick Start](#task-overview) above for setup steps.

### Option 2: Run with Custom Configuration

Use the scripts in [<repo-name> on GitHub](<github-link>) to run with your own:

- Custom model variant (`base16`, `large14`, `large14-336`, etc.)
- Custom dataset (beyond CIFAR-10)
- Target AMD GPU/CPU and runtime (PyTorch vs vLLM)

This option is ideal if you need to customize the evaluation beyond the default configuration provided here.

---

## Model Details

**Model Type:** Zero-shot image classification (contrastive image-text embedding)

**Base Model:** `openai/clip-vit-base-patch32` (ViT-B/32)

**Model Stats:**
- Model variant: base32 (default) — base16 / large14 / large14-336 also supported
- Image encoder: ViT-B/32
- Text encoder: Transformer (CLIP text tower)
- Input resolution: 224x224 (base variants), 336x336 (large14-336)
- Number of parameters: `<fill-in>`
- Precision tested: fp32, fp16

---

## Performance Summary

Higher zero-shot accuracy means more test images are assigned the correct CIFAR-10 class via CLIP's image–text similarity — 100% is perfect, 10% is chance level for 10 classes. Values above ~85% on CIFAR-10 with ViT-B/32 are typical for this benchmark.

### Metrics Explained

| Metric | Description |
|--------|-------------|
| Zero-shot accuracy (%) | Fraction of CIFAR-10 test images whose highest-scoring text prompt matches the ground-truth label after softmax over 10 class prompts. Primary accuracy metric; sensitive to both image and text embedding quality. |

### Accuracy Results

**Full Dataset Evaluation (CIFAR-10 test)** — filled from `evaluation_results/`; run `make metrics` to refresh:

<!-- accuracy-table-start -->
| Device | Backend | Precision | Variant | Accuracy (%) |
|--------|---------|-----------|---------|--------------|
| CPU | PyTorch | FP16 | clip-vit-base-patch32 | 88.79 |
| CPU | PyTorch | FP32 | clip-vit-base-patch32 | 88.80 |
| GPU | PyTorch | FP16 | clip-vit-base-patch32 | 88.75 |
| GPU | PyTorch | FP32 | clip-vit-base-patch32 | 88.80 |
| GPU | vLLM | FP16 | clip-vit-base-patch32 | 88.78 |
| GPU | vLLM | FP32 | clip-vit-base-patch32 | 88.80 |
<!-- accuracy-table-end -->

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## Dig Deeper

Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples?

📂 **[View the full project on GitHub](<github-link>)**

The GitHub repository includes:
- Setup and prerequisites for ROCm environments
- Scripts for both PyTorch and vLLM runners
- Additional model variants and datasets
- Benchmarking and reproduction instructions

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