This is a project intended to create a new CNN Network for Image Classification. Fas14MNet can also be used for Multi-Label Image Classification and in this project we try to create a CNN that will work equally well with for Traditional AI architectures as well as a Federated Architecture and Fas14MNet helps us achieve that. Fas14MNet is inspired from the basic architecture of a ResNet50 model but is much lighter while having ~15M parameters compared to 23M+ parameters in a ResNet50 model.
Steps to run:
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from fas14mnet.net import Fas14MNet
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model = Fas14MNet(num_classes)
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Metadata
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Release files for Fas14MNet 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| Fas14MNet-0.1.2.linux-x86_64.tar.gz | 4.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Fas14MNet-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 7.9 kB
Release files / Fas14MNet-0.1.2.linux-x86_64.tar.gz
| Download URL | Fas14MNet-0.1.2.linux-x86_64.tar.gz |
|---|---|
| Size | 4.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.7.1 importlib_metadata/4.6.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.10
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Release files / Fas14MNet-0.1.2-py3-none-any.whl
| Download URL | Fas14MNet-0.1.2-py3-none-any.whl |
|---|---|
| Size | 3.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
666f833f5b937524ade6a5447761cbb822ca330c3dabe98b21cb575021cbaae8
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0137e85cd14a80e30928b2f224078981466c343d770863d793cf2cadbfc40e9c
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
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twine/3.7.1 importlib_metadata/4.6.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.56.0 CPython/3.8.10
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