TinyCLIP is a compact vision–language model that compresses CLIP through knowledge distillation, enabling efficient image–text representation learning with significantly lower compute and memory requirements.

Original paper: TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance, Wu et al., 2023

TinyCLIP-ViT8M16

This model uses the TinyCLIP variant, optimized for efficient image–text embedding generation while preserving strong zero-shot classification and retrieval performance. It is well suited for applications such as image retrieval, zero-shot classification, multimodal search, and edge vision-language deployments.

Model Configuration:

• Reference implementation: Official TinyCLIP source code
• Original Weight: TinyCLIP-ViT8M16
• Resolution: 3x224x224
• Support Cooper version:
  • Cooper SDK: [2.5.4]
  • Cooper Foundry: [2.3]

Model	Device	Model Link
TinyCLIP-ViT8M16 Image Encoder	N1-655	Model_Link
TinyCLIP-ViT8M16 Text Encoder	N1-655	Model_Link
TinyCLIP-ViT8M16 Image encoder	CV7	Model_Link
TinyCLIP-ViT8M16 Text Encoder	CV7	Model_Link
TinyCLIP-ViT8M16 Image encoder	CV72	Model_Link
TinyCLIP-ViT8M16 Text Encoder	CV72	Model_Link
TinyCLIP-ViT8M16 Image encoder	CV75	Model_Link
TinyCLIP-ViT8M16 Text Encoder	CV75	Model_Link