Officecaltechdomainadaptation
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OfficeCaltechDomainAdaptation
What is this ?
This dataset is part of the Computer Vision problematic consisting in making machines learn to detect the presence of an object in an image. Here, we want to learn a classification model that takes as input an image and return the category of the object it contains.
The Office Caltech dataset contains four different domains: amazon, caltech10, dslr and webcam. These domains contain respectively 958, 1123, 295 and 157 images. Each image contains only one object among a list of 10 objects: backpack, bike, calculator, headphones, keyboard, laptop, monitor, mouse, mug and projector.
With this benchmark dataset in Domain Adaptation, we repeatedly take one of the four domains as Source domain S and one of the three remaining as target T. The aim is then to learn to classify images with the data from S to correctly classify the images in T.
What is available in this repository ?
In addition to the images, we also give features that were extracted from the images to describe them. We give different sets of features that describe all the images in the corresponding folder.
We propose some code in python3 to show how to evaluate the benchmark. What is usually evaluated with this benchmark are Domain Adaptation algorithms. We provide code for a few of them.
Dependencies
Python3 and some python3 libraries:
- numpy
- scipy
- sklearn
Example of execution
Program launched by executing the main.py script with python:
python3 main.py
For each adaptation problem among the 12 possible, each adaptation algorithm chosen at the beginning of the file is applied. Then are reported the mean accuracy and standard deviation. Results (using the default surf [1] features):
Feature used: surf
Number of iterations: 10
Adaptation algorithms used: NA SA TCA OT CORAL
A->C ..........
23.1 1.9 NA 0.12s
33.9 2.0 SA 1.03s
33.3 1.8 TCA 17.49s
29.8 1.5 OT 1.85s
28.9 2.5 CORAL 8.95s
A->D ..........
23.9 2.5 NA 0.02s
32.1 3.6 SA 0.38s
28.7 4.7 TCA 0.79s
40.5 2.5 OT 0.36s
30.5 3.4 CORAL 8.43s
A->W ..........
26.5 1.6 NA 0.04s
31.2 2.4 SA 0.44s
34.1 2.7 TCA 1.67s
34.7 2.0 OT 0.46s
27.2 2.2 CORAL 8.99s
C->A ..........
22.2 2.8 NA 0.10s
35.2 2.9 SA 0.80s
35.9 2.8 TCA 12.38s
37.4 2.4 OT 1.39s
32.1 2.4 CORAL 8.88s
C->D ..........
22.5 4.0 NA 0.02s
35.2 3.4 SA 0.38s
34.7 3.4 TCA 0.80s
44.5 3.7 OT 0.37s
32.7 2.2 CORAL 9.06s
C->W ..........
20.2 4.1 NA 0.03s
30.0 3.2 SA 0.45s
30.7 4.5 TCA 1.68s
35.7 5.3 OT 0.51s
25.9 3.4 CORAL 9.28s
D->A ..........
26.6 1.8 NA 0.07s
32.0 1.1 SA 0.66s
33.4 1.4 TCA 9.19s
29.2 1.2 OT 0.79s
29.5 0.7 CORAL 8.49s
D->C ..........
25.3 1.2 NA 0.08s
30.6 0.7 SA 0.74s
31.3 1.4 TCA 13.73s
29.7 0.9 OT 0.91s
28.7 0.9 CORAL 8.71s
D->W ..........
52.2 1.8 NA 0.03s
79.4 2.0 SA 0.33s
74.4 2.7 TCA 0.93s
68.3 2.9 OT 0.32s
77.9 1.5 CORAL 8.59s
W->A ..........
23.4 1.0 NA 0.10s
30.2 1.2 SA 0.81s
29.5 1.3 TCA 12.26s
37.3 1.1 OT 1.39s
28.6 0.8 CORAL 8.70s
W->C ..........
18.9 1.0 NA 0.12s
28.7 1.5 SA 0.89s
29.9 1.0 TCA 17.34s
34.6 0.8 OT 1.60s
25.5 0.9 CORAL 8.92s
W->D ..........
52.0 2.6 NA 0.02s
83.4 1.9 SA 0.38s
78.5 1.8 TCA 0.80s
70.4 1.7 OT 0.34s
79.7 2.0 CORAL 8.53s
Mean results and total time
28.1 2.2 NA 0.76s
40.2 2.2 SA 7.28s
39.5 2.4 TCA 89.07s
41.0 2.2 OT 10.30s
37.3 1.9 CORAL 105.54s
By modifying the feature used in the script with CaffeNet [2] features:
Feature used: CaffeNet4096
Number of iterations: 10
Adaptation algorithms used: NA SA TCA OT CORAL
A->C ..........
70.8 2.7 NA 0.36s
78.6 2.0 SA 6.55s
79.6 1.7 TCA 18.43s
82.3 0.9 OT 4.16s
76.3 0.8 CORAL 601.37s
A->D ..........
77.9 3.3 NA 0.08s
80.7 1.9 SA 3.30s
86.2 1.9 TCA 0.90s
93.4 0.7 OT 1.07s
76.3 2.4 CORAL 592.45s
A->W ..........
67.1 2.4 NA 0.13s
80.3 2.5 SA 3.80s
84.4 2.5 TCA 1.91s
92.4 1.0 OT 1.52s
74.9 2.3 CORAL 628.96s
C->A ..........
81.0 1.9 NA 0.34s
84.8 1.4 SA 6.29s
87.3 2.0 TCA 13.89s
88.5 1.3 OT 3.63s
81.8 2.1 CORAL 634.86s
C->D ..........
75.5 5.1 NA 0.09s
81.1 2.2 SA 3.39s
83.6 2.9 TCA 0.89s
93.0 1.5 OT 1.10s
78.7 1.4 CORAL 620.05s
C->W ..........
72.4 7.0 NA 0.13s
76.8 3.0 SA 3.82s
80.5 2.5 TCA 1.93s
90.7 1.0 OT 1.51s
71.3 3.2 CORAL 639.16s
D->A ..........
70.1 1.8 NA 0.22s
83.3 1.2 SA 6.14s
87.3 1.0 TCA 9.96s
85.9 1.9 OT 2.64s
82.3 1.4 CORAL 592.33s
D->C ..........
66.4 1.3 NA 0.25s
75.2 0.9 SA 6.55s
78.2 0.9 TCA 14.57s
79.0 2.5 OT 2.91s
75.8 0.8 CORAL 599.77s
D->W ..........
91.7 2.1 NA 0.11s
96.6 1.1 SA 4.86s
97.5 1.1 TCA 1.41s
96.4 0.6 OT 1.25s
96.4 0.9 CORAL 803.69s
W->A ..........
69.9 2.0 NA 0.32s
82.8 0.9 SA 5.85s
86.7 1.1 TCA 12.77s
86.8 1.8 OT 3.47s
77.5 0.7 CORAL 589.03s
W->C ..........
61.1 2.2 NA 0.35s
73.4 0.7 SA 6.41s
76.6 1.3 TCA 18.59s
77.9 1.8 OT 4.10s
70.3 0.9 CORAL 602.38s
W->D ..........
95.9 1.3 NA 0.09s
99.7 0.4 SA 3.76s
99.0 1.0 TCA 0.94s
97.1 0.7 OT 1.15s
99.6 0.3 CORAL 661.38s
Mean results and total time
75.0 2.8 NA 2.47s
82.8 1.5 SA 60.71s
85.6 1.7 TCA 96.20s
88.6 1.3 OT 28.50s
80.1 1.4 CORAL 7565.44s
and with GoogleNet [3] features:
Feature used: GoogleNet1024
Number of iterations: 10
Adaptation algorithms used: NA SA TCA OT CORAL
A->C ..........
84.4 1.2 NA 0.14s
85.7 1.0 SA 1.77s
87.2 1.1 TCA 20.34s
90.0 0.7 OT 2.08s
85.6 1.0 CORAL 18.54s
A->D ..........
88.4 2.3 NA 0.03s
87.5 2.9 SA 0.56s
90.2 3.3 TCA 0.83s
93.4 0.9 OT 0.51s
86.2 3.3 CORAL 16.99s
A->W ..........
82.2 2.3 NA 0.04s
83.2 1.6 SA 0.64s
85.9 1.6 TCA 1.75s
95.7 1.1 OT 0.65s
82.5 1.2 CORAL 15.97s
C->A ..........
90.0 1.2 NA 0.11s
90.9 0.8 SA 1.34s
92.3 1.4 TCA 13.54s
93.8 0.4 OT 1.66s
90.0 0.5 CORAL 18.67s
C->D ..........
87.3 2.2 NA 0.03s
88.5 2.1 SA 0.55s
90.1 2.9 TCA 0.81s
93.4 1.3 OT 0.50s
86.1 2.8 CORAL 16.55s
C->W ..........
84.4 2.4 NA 0.05s
86.6 1.5 SA 0.68s
90.9 2.4 TCA 1.74s
96.9 0.4 OT 0.69s
83.6 3.3 CORAL 17.15s
D->A ..........
83.0 1.7 NA 0.08s
88.0 1.5 SA 1.25s
90.3 1.5 TCA 10.17s
91.2 0.9 OT 1.13s
87.2 1.6 CORAL 18.08s
D->C ..........
77.2 1.8 NA 0.09s
83.6 1.1 SA 1.30s
85.1 1.4 TCA 15.19s
89.5 0.5 OT 1.32s
84.7 1.0 CORAL 16.53s
D->W ..........
97.5 1.1 NA 0.03s
98.0 0.8 SA 0.48s
97.3 0.9 TCA 0.94s
97.8 0.7 OT 0.46s
98.2 0.7 CORAL 15.97s
W->A ..........
86.4 1.2 NA 0.11s
90.0 0.6 SA 1.36s
92.2 0.6 TCA 13.29s
92.6 0.5 OT 1.87s
87.8 0.9 CORAL 17.85s
W->C ..........
80.0 1.0 NA 0.13s
84.4 0.7 SA 1.49s
87.9 0.5 TCA 19.54s
90.1 0.7 OT 1.96s
84.7 0.6 CORAL 16.93s
W->D ..........
99.4 0.3 NA 0.03s
99.4 0.4 SA 0.54s
99.8 0.4 TCA 0.82s
97.3 1.0 OT 0.49s
99.0 0.6 CORAL 16.22s
Mean results and total time
86.7 1.5 NA 0.87s
88.8 1.3 SA 11.97s
90.8 1.5 TCA 98.98s
93.5 0.8 OT 13.32s
88.0 1.5 CORAL 205.45s
[1] Gong, B., Grauman, K., & Sha, F. (2014). Learning kernels for unsupervised domain adaptation with applications to visual object recognition. International Journal of Computer Vision, 109(1-2), 3-27.
[2] Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678). ACM.
[3] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
