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430_FastReID

README.md

Note

FastReID is a research platform that implements state-of-the-art re-identification algorithms.

1. Citation Repository

https://github.com/JDAI-CV/fast-reid

https://github.com/NirAharon/BoT-SORT

2. ONNX Export

https://github.com/PINTO0309/SMILEtrack

3. Code snippet for calculating Cosine similarity (COS similarity) from feature vectors

Cosine similarity is calculated by dividing the inner product of two vectors by the product of their norms. However, since the vectors here are already normalized, simply computing the inner product results in the cosine similarity.

import torch
import torch.nn.functional as F

# Obtain feature vectors from images
with torch.no_grad():
    f1 = model(image1)  # Feature vector of image1
    f2 = model(image2)  # Feature vector of image2
    # Normalize and convert each vector to the unit norm (length is 1)
    A_feat = F.normalize(f1, dim=1).cpu()
    B_feat = F.normalize(f2, dim=1).cpu()
simlarity = A_feat.matmul(B_feat.transpose(1, 0)) # inner product of feature vectors
print("\033[1;31m The similarity is {}\033[".format(simlarity[0, 0]))

4. Similarity validation

Comparison
Patterns		 image.1 image.2 Comparison
Patterns		 image.1 image.2
30 vs 31⬇️ 00030 00031 1 vs 2⏫ 1 2
30 vs 1⬇️ 00030 1 1 vs 3⏫ 1 3
31 vs 2⬇️ 00031 2 1 vs 4⏫ 1 4 
python validation.py
Model 30
vs
31
⬇️		 30
vs
1
⬇️		 31
vs
2
⬇️		 1
vs
2
⏫		 1
vs
3
⏫		 1
vs
4
⏫
mot17_sbs_S50_NMx3x256x128_post 0.148 0.046 0.219 0.359 0.611 0.543
mot17_sbs_S50_NMx3x288x128_post 0.154 0.036 0.223 0.375 0.643 0.562
mot17_sbs_S50_NMx3x320x128_post 0.093 0.002 0.180 0.386 0.635 0.631
mot17_sbs_S50_NMx3x352x128_post 0.057 0.000 0.153 0.366 0.642 0.649
mot17_sbs_S50_NMx3x384x128_post 0.044 0.000 0.139 0.359 0.629 0.686
mot20_sbs_S50_NMx3x256x128_post 0.406 0.318 0.309 0.538 0.727 0.778
mot20_sbs_S50_NMx3x288x128_post 0.393 0.288 0.324 0.544 0.724 0.770
mot20_sbs_S50_NMx3x320x128_post 0.372 0.253 0.293 0.543 0.701 0.775
mot20_sbs_S50_NMx3x352x128_post 0.351 0.243 0.301 0.578 0.695 0.756
mot20_sbs_S50_NMx3x384x128_post 0.325 0.226 0.289 0.559 0.698 0.757
OSNet
osnet_x1_0_msmt17_combineall_256x128_amsgrad_NMx3x256x128 0.341 0.285 0.265 0.476 0.686 0.504
resnet50_msmt17_combineall_256x128_amsgrad_NMx3x256x128 0.418 0.373 0.329 0.593 0.810 0.752

5. BoT-SORT Implementation by onnxruntime + TensorRT only

https://github.com/PINTO0309/BoT-SORT-ONNX-TensorRT

293717688-368bbfda-b204-4246-8663-259f999dab1c.mp4