metadata
library_name: pytorch
ResNet is a family of deep convolutional neural networks that introduced residual (skip) connections to enable stable training of very deep architectures with strong representational capacity.
Original paper: Deep Residual Learning for Image Recognition, He et al., 2015
ResNet-50
ResNet-50 is a commonly used 50-layer variant that offers a strong balance between accuracy and computational cost and is widely adopted as a baseline and as a backbone feature extractor for tasks such as object detection, segmentation, and re-identification.
Model Configuration:
- Reference implementation: ResNet50_v1.5
- Original Weight: ResNet50_Weights.IMAGENET1K_V2
- Resolution: 3x224x224
- Support Cooper version:
- Cooper SDK: [2.5.3]
- Cooper Foundry: [2.2]
| Model | Device | compression | Model Link |
|---|---|---|---|
| Resnet50 | N1-655 | Amba_optimized | Model_Link |
| Resnet50 | N1-655 | Activation_fp16 | Model_Link |
| Resnet50 | CV7 | Amba_optimized | Model_Link |
| Resnet50 | CV7 | Activation_fp16 | Model_Link |
| Resnet50 | CV72 | Amba_optimized | Model_Link |
| Resnet50 | CV72 | Activation_fp16 | Model_Link |
| Resnet50 | CV75 | Amba_optimized | Model_Link |
| Resnet50 | CV75 | Activation_fp16 | Model_Link |