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custom-clip-vit-b-coco
MuhammedKsee/custom-clip-vit-b-coco
custom-clip-vit-b-coco is a zero-shot image classification model from MuhammedKsee. Use it for the zero-shot image classification task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as cc-by-4.0.
This model is a scratch-built, highly optimized implementation of the CLIP architecture, developed as part of an Academic Research Project.
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Public
Updated Jan 13, 2026
Repo size
1.8 GB
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At a glance
- Task
- Zero-Shot Image Classification
- Library
- pytorch
- License
- cc-by-4.0
- Access
- Public
- Created
- Jan 13, 2026
- Updated
- Jan 13, 2026
- SHA
- 18348ca7
Model card
From the Hugging Face model README
Custom CLIP (ViT-B/16) - Optimized
This model is a scratch-built, highly optimized implementation of the CLIP architecture, developed as part of an Academic Research Project.
It achieves 2.46x faster inference speed (Latency: 21ms vs 52ms) compared to the standard OpenAI CLIP model on consumer hardware (RTX 3050 Ti), while maintaining 97.7% Zero-Shot Accuracy.
🔗 Source Code & Usage
The full source code, training details, and inference scripts are available on GitHub: 👉 GitHub Repository: custom-clip-vit-b-coco
(Please verify the GitHub link matches your actual repo URL)
🚀 Performance Benchmark
| Model | Optimization | Latency | Speedup | Accuracy |
|---|---|---|---|---|
| OpenAI CLIP | FP32 | 52.22 ms | 1.0x | 99.88% |
| Custom CLIP | FP16 + Compile | 21.20 ms | 2.46x | 97.71% |
⚠️ License & Citation
This model is licensed under CC-BY 4.0. You are free to use it for academic or commercial purposes, but you must provide attribution to the author:
Author: Muhammed Köse
Project: Custom CLIP Optimization
- Task
- Zero-Shot Image Classification
- Library
- pytorch
- License
- cc-by-4.0
- Languages
- en
- Created
- Jan 13, 2026
- Updated
- Jan 13, 2026