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Parent(s):
Initial commit of finished project
Browse files- .gitattributes +1 -0
- Untitled24vitsmall (1) - Copy.ipynb +2154 -0
- events.out.tfevents.1772175113.b6936449dab3.1207.0 +0 -0
- model_training_results.csv +51 -0
- model_training_results2.xlsx +0 -0
- test/New Text Document.txt +22 -0
- test/download (1).png +0 -0
- test/download (3).png +0 -0
- test/download.png +0 -0
- test/perclassmetrics.csv +8 -0
- val/New Text Document.txt +17 -0
- val/download (1).png +0 -0
- val/download (2).png +0 -0
- val/download.png +0 -0
- val/perclass.csv +8 -0
- vitsmall2.pth +3 -0
.gitattributes
ADDED
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*.pth filter=lfs diff=lfs merge=lfs -text
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Untitled24vitsmall (1) - Copy.ipynb
ADDED
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@@ -0,0 +1,2154 @@
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| 1 |
+
{
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| 2 |
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"cells": [
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| 3 |
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{
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| 4 |
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"cell_type": "code",
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| 5 |
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"execution_count": 5,
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| 6 |
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"metadata": {
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| 7 |
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"id": "DDjtMfKwauQl"
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"outputs": [
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{
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| 11 |
+
"name": "stdout",
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| 12 |
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"output_type": "stream",
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| 13 |
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"text": [
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+
"Requirement already satisfied: timm in /venv/main/lib/python3.12/site-packages (1.0.25)\n",
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| 15 |
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"Requirement already satisfied: gdown in /venv/main/lib/python3.12/site-packages (5.2.1)\n",
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| 16 |
+
"Collecting tensorboard\n",
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| 17 |
+
" Downloading tensorboard-2.20.0-py3-none-any.whl.metadata (1.8 kB)\n",
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| 18 |
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"Requirement already satisfied: torch in /venv/main/lib/python3.12/site-packages (from timm) (2.10.0+cu130)\n",
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| 19 |
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"Requirement already satisfied: torchvision in /venv/main/lib/python3.12/site-packages (from timm) (0.25.0+cu130)\n",
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| 20 |
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"Requirement already satisfied: pyyaml in /venv/main/lib/python3.12/site-packages (from timm) (6.0.3)\n",
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"Requirement already satisfied: huggingface_hub in /venv/main/lib/python3.12/site-packages (from timm) (1.2.3)\n",
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| 22 |
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"Requirement already satisfied: safetensors in /venv/main/lib/python3.12/site-packages (from timm) (0.7.0)\n",
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| 23 |
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"Requirement already satisfied: beautifulsoup4 in /venv/main/lib/python3.12/site-packages (from gdown) (4.14.3)\n",
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| 24 |
+
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"Requirement already satisfied: requests[socks] in /venv/main/lib/python3.12/site-packages (from gdown) (2.32.5)\n",
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"Requirement already satisfied: tqdm in /venv/main/lib/python3.12/site-packages (from gdown) (4.67.1)\n",
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| 27 |
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"Collecting absl-py>=0.4 (from tensorboard)\n",
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| 28 |
+
" Downloading absl_py-2.4.0-py3-none-any.whl.metadata (3.3 kB)\n",
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| 29 |
+
"Collecting grpcio>=1.48.2 (from tensorboard)\n",
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| 30 |
+
" Downloading grpcio-1.78.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (3.8 kB)\n",
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| 31 |
+
"Collecting markdown>=2.6.8 (from tensorboard)\n",
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| 32 |
+
" Downloading markdown-3.10.2-py3-none-any.whl.metadata (5.1 kB)\n",
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| 33 |
+
"Requirement already satisfied: numpy>=1.12.0 in /venv/main/lib/python3.12/site-packages (from tensorboard) (2.4.1)\n",
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| 34 |
+
"Requirement already satisfied: packaging in /venv/main/lib/python3.12/site-packages (from tensorboard) (25.0)\n",
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| 35 |
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"Requirement already satisfied: pillow in /venv/main/lib/python3.12/site-packages (from tensorboard) (12.1.0)\n",
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| 36 |
+
"Collecting protobuf!=4.24.0,>=3.19.6 (from tensorboard)\n",
|
| 37 |
+
" Downloading protobuf-7.34.0-cp310-abi3-manylinux2014_x86_64.whl.metadata (595 bytes)\n",
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| 38 |
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"Requirement already satisfied: setuptools>=41.0.0 in /venv/main/lib/python3.12/site-packages (from tensorboard) (80.9.0)\n",
|
| 39 |
+
"Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard)\n",
|
| 40 |
+
" Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl.metadata (1.1 kB)\n",
|
| 41 |
+
"Collecting werkzeug>=1.0.1 (from tensorboard)\n",
|
| 42 |
+
" Downloading werkzeug-3.1.6-py3-none-any.whl.metadata (4.0 kB)\n",
|
| 43 |
+
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| 44 |
+
"Requirement already satisfied: markupsafe>=2.1.1 in /venv/main/lib/python3.12/site-packages (from werkzeug>=1.0.1->tensorboard) (3.0.3)\n",
|
| 45 |
+
"Requirement already satisfied: soupsieve>=1.6.1 in /venv/main/lib/python3.12/site-packages (from beautifulsoup4->gdown) (2.8.3)\n",
|
| 46 |
+
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|
| 47 |
+
"Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in /venv/main/lib/python3.12/site-packages (from huggingface_hub->timm) (1.2.0)\n",
|
| 48 |
+
"Requirement already satisfied: httpx<1,>=0.23.0 in /venv/main/lib/python3.12/site-packages (from huggingface_hub->timm) (0.28.1)\n",
|
| 49 |
+
"Requirement already satisfied: shellingham in /venv/main/lib/python3.12/site-packages (from huggingface_hub->timm) (1.5.4)\n",
|
| 50 |
+
"Requirement already satisfied: typer-slim in /venv/main/lib/python3.12/site-packages (from huggingface_hub->timm) (0.21.0)\n",
|
| 51 |
+
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|
| 52 |
+
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| 53 |
+
"Requirement already satisfied: httpcore==1.* in /venv/main/lib/python3.12/site-packages (from httpx<1,>=0.23.0->huggingface_hub->timm) (1.0.9)\n",
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| 54 |
+
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| 55 |
+
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|
| 56 |
+
"Requirement already satisfied: charset_normalizer<4,>=2 in /venv/main/lib/python3.12/site-packages (from requests[socks]->gdown) (3.4.4)\n",
|
| 57 |
+
"Requirement already satisfied: urllib3<3,>=1.21.1 in /venv/main/lib/python3.12/site-packages (from requests[socks]->gdown) (2.6.3)\n",
|
| 58 |
+
"Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /venv/main/lib/python3.12/site-packages (from requests[socks]->gdown) (1.7.1)\n",
|
| 59 |
+
"Requirement already satisfied: sympy>=1.13.3 in /venv/main/lib/python3.12/site-packages (from torch->timm) (1.14.0)\n",
|
| 60 |
+
"Requirement already satisfied: networkx>=2.5.1 in /venv/main/lib/python3.12/site-packages (from torch->timm) (3.6.1)\n",
|
| 61 |
+
"Requirement already satisfied: jinja2 in /venv/main/lib/python3.12/site-packages (from torch->timm) (3.1.6)\n",
|
| 62 |
+
"Requirement already satisfied: cuda-bindings==13.0.3 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.3)\n",
|
| 63 |
+
"Requirement already satisfied: nvidia-cuda-nvrtc==13.0.88 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.88)\n",
|
| 64 |
+
"Requirement already satisfied: nvidia-cuda-runtime==13.0.96 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.96)\n",
|
| 65 |
+
"Requirement already satisfied: nvidia-cuda-cupti==13.0.85 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.85)\n",
|
| 66 |
+
"Requirement already satisfied: nvidia-cudnn-cu13==9.15.1.9 in /venv/main/lib/python3.12/site-packages (from torch->timm) (9.15.1.9)\n",
|
| 67 |
+
"Requirement already satisfied: nvidia-cublas==13.1.0.3 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.1.0.3)\n",
|
| 68 |
+
"Requirement already satisfied: nvidia-cufft==12.0.0.61 in /venv/main/lib/python3.12/site-packages (from torch->timm) (12.0.0.61)\n",
|
| 69 |
+
"Requirement already satisfied: nvidia-curand==10.4.0.35 in /venv/main/lib/python3.12/site-packages (from torch->timm) (10.4.0.35)\n",
|
| 70 |
+
"Requirement already satisfied: nvidia-cusolver==12.0.4.66 in /venv/main/lib/python3.12/site-packages (from torch->timm) (12.0.4.66)\n",
|
| 71 |
+
"Requirement already satisfied: nvidia-cusparse==12.6.3.3 in /venv/main/lib/python3.12/site-packages (from torch->timm) (12.6.3.3)\n",
|
| 72 |
+
"Requirement already satisfied: nvidia-cusparselt-cu13==0.8.0 in /venv/main/lib/python3.12/site-packages (from torch->timm) (0.8.0)\n",
|
| 73 |
+
"Requirement already satisfied: nvidia-nccl-cu13==2.28.9 in /venv/main/lib/python3.12/site-packages (from torch->timm) (2.28.9)\n",
|
| 74 |
+
"Requirement already satisfied: nvidia-nvshmem-cu13==3.4.5 in /venv/main/lib/python3.12/site-packages (from torch->timm) (3.4.5)\n",
|
| 75 |
+
"Requirement already satisfied: nvidia-nvtx==13.0.85 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.85)\n",
|
| 76 |
+
"Requirement already satisfied: nvidia-nvjitlink==13.0.88 in /venv/main/lib/python3.12/site-packages (from torch->timm) (13.0.88)\n",
|
| 77 |
+
"Requirement already satisfied: nvidia-cufile==1.15.1.6 in /venv/main/lib/python3.12/site-packages (from torch->timm) (1.15.1.6)\n",
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| 78 |
+
"Requirement already satisfied: triton==3.6.0 in /venv/main/lib/python3.12/site-packages (from torch->timm) (3.6.0)\n",
|
| 79 |
+
"Requirement already satisfied: cuda-pathfinder~=1.1 in /venv/main/lib/python3.12/site-packages (from cuda-bindings==13.0.3->torch->timm) (1.3.3)\n",
|
| 80 |
+
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /venv/main/lib/python3.12/site-packages (from sympy>=1.13.3->torch->timm) (1.3.0)\n",
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| 81 |
+
"Requirement already satisfied: click>=8.0.0 in /venv/main/lib/python3.12/site-packages (from typer-slim->huggingface_hub->timm) (8.3.1)\n",
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+
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"\u001b[?25hDownloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB)\n",
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| 87 |
+
"Downloading grpcio-1.78.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (6.7 MB)\n",
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+
"\u001b[?25hDownloading markdown-3.10.2-py3-none-any.whl (108 kB)\n",
|
| 90 |
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"Downloading protobuf-7.34.0-cp310-abi3-manylinux2014_x86_64.whl (324 kB)\n",
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| 91 |
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"Downloading werkzeug-3.1.6-py3-none-any.whl (225 kB)\n",
|
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"Installing collected packages: werkzeug, tensorboard-data-server, protobuf, markdown, grpcio, absl-py, tensorboard\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━��━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7/7\u001b[0m [tensorboard]\u001b[0m [tensorboard]\n",
|
| 94 |
+
"\u001b[1A\u001b[2KSuccessfully installed absl-py-2.4.0 grpcio-1.78.0 markdown-3.10.2 protobuf-7.34.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 werkzeug-3.1.6\n",
|
| 95 |
+
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
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| 96 |
+
"\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
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| 97 |
+
]
|
| 98 |
+
}
|
| 99 |
+
],
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| 100 |
+
"source": [
|
| 101 |
+
"%pip install timm gdown tensorboard"
|
| 102 |
+
]
|
| 103 |
+
},
|
| 104 |
+
{
|
| 105 |
+
"cell_type": "code",
|
| 106 |
+
"execution_count": 1,
|
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+
"metadata": {},
|
| 108 |
+
"outputs": [
|
| 109 |
+
{
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| 110 |
+
"name": "stdout",
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+
"output_type": "stream",
|
| 112 |
+
"text": [
|
| 113 |
+
"Package Version\n",
|
| 114 |
+
"----------------------- ------------\n",
|
| 115 |
+
"anyio 4.12.0\n",
|
| 116 |
+
"asttokens 3.0.1\n",
|
| 117 |
+
"certifi 2025.11.12\n",
|
| 118 |
+
"charset-normalizer 3.4.4\n",
|
| 119 |
+
"click 8.3.1\n",
|
| 120 |
+
"comm 0.2.3\n",
|
| 121 |
+
"cuda-bindings 13.0.3\n",
|
| 122 |
+
"cuda-pathfinder 1.3.3\n",
|
| 123 |
+
"debugpy 1.8.19\n",
|
| 124 |
+
"decorator 5.2.1\n",
|
| 125 |
+
"executing 2.2.1\n",
|
| 126 |
+
"filelock 3.20.1\n",
|
| 127 |
+
"fsspec 2025.12.0\n",
|
| 128 |
+
"h11 0.16.0\n",
|
| 129 |
+
"hf-xet 1.2.0\n",
|
| 130 |
+
"httpcore 1.0.9\n",
|
| 131 |
+
"httpx 0.28.1\n",
|
| 132 |
+
"huggingface_hub 1.2.3\n",
|
| 133 |
+
"idna 3.11\n",
|
| 134 |
+
"ipykernel 7.1.0\n",
|
| 135 |
+
"ipython 9.8.0\n",
|
| 136 |
+
"ipython_pygments_lexers 1.1.1\n",
|
| 137 |
+
"ipywidgets 8.1.8\n",
|
| 138 |
+
"jedi 0.19.2\n",
|
| 139 |
+
"Jinja2 3.1.6\n",
|
| 140 |
+
"jupyter_client 8.7.0\n",
|
| 141 |
+
"jupyter_core 5.9.1\n",
|
| 142 |
+
"jupyterlab_widgets 3.0.16\n",
|
| 143 |
+
"MarkupSafe 3.0.3\n",
|
| 144 |
+
"matplotlib-inline 0.2.1\n",
|
| 145 |
+
"mpmath 1.3.0\n",
|
| 146 |
+
"nest-asyncio 1.6.0\n",
|
| 147 |
+
"networkx 3.6.1\n",
|
| 148 |
+
"numpy 2.4.1\n",
|
| 149 |
+
"nvidia-cublas 13.1.0.3\n",
|
| 150 |
+
"nvidia-cuda-cupti 13.0.85\n",
|
| 151 |
+
"nvidia-cuda-nvrtc 13.0.88\n",
|
| 152 |
+
"nvidia-cuda-runtime 13.0.96\n",
|
| 153 |
+
"nvidia-cudnn-cu13 9.15.1.9\n",
|
| 154 |
+
"nvidia-cufft 12.0.0.61\n",
|
| 155 |
+
"nvidia-cufile 1.15.1.6\n",
|
| 156 |
+
"nvidia-curand 10.4.0.35\n",
|
| 157 |
+
"nvidia-cusolver 12.0.4.66\n",
|
| 158 |
+
"nvidia-cusparse 12.6.3.3\n",
|
| 159 |
+
"nvidia-cusparselt-cu13 0.8.0\n",
|
| 160 |
+
"nvidia-nccl-cu13 2.28.9\n",
|
| 161 |
+
"nvidia-nvjitlink 13.0.88\n",
|
| 162 |
+
"nvidia-nvshmem-cu13 3.4.5\n",
|
| 163 |
+
"nvidia-nvtx 13.0.85\n",
|
| 164 |
+
"packaging 25.0\n",
|
| 165 |
+
"parso 0.8.5\n",
|
| 166 |
+
"pexpect 4.9.0\n",
|
| 167 |
+
"pillow 12.1.0\n",
|
| 168 |
+
"pip 25.3\n",
|
| 169 |
+
"platformdirs 4.5.1\n",
|
| 170 |
+
"prompt_toolkit 3.0.52\n",
|
| 171 |
+
"psutil 7.2.1\n",
|
| 172 |
+
"ptyprocess 0.7.0\n",
|
| 173 |
+
"pure_eval 0.2.3\n",
|
| 174 |
+
"Pygments 2.19.2\n",
|
| 175 |
+
"python-dateutil 2.9.0.post0\n",
|
| 176 |
+
"PyYAML 6.0.3\n",
|
| 177 |
+
"pyzmq 27.1.0\n",
|
| 178 |
+
"requests 2.32.5\n",
|
| 179 |
+
"sentencepiece 0.2.1\n",
|
| 180 |
+
"setuptools 80.9.0\n",
|
| 181 |
+
"shellingham 1.5.4\n",
|
| 182 |
+
"six 1.17.0\n",
|
| 183 |
+
"stack-data 0.6.3\n",
|
| 184 |
+
"sympy 1.14.0\n",
|
| 185 |
+
"torch 2.10.0+cu130\n",
|
| 186 |
+
"torchaudio 2.10.0+cu130\n",
|
| 187 |
+
"torchcodec 0.10.0\n",
|
| 188 |
+
"torchdata 0.10.0\n",
|
| 189 |
+
"torchtext 0.6.0\n",
|
| 190 |
+
"torchvision 0.25.0+cu130\n",
|
| 191 |
+
"tornado 6.5.4\n",
|
| 192 |
+
"tqdm 4.67.1\n",
|
| 193 |
+
"traitlets 5.14.3\n",
|
| 194 |
+
"triton 3.6.0\n",
|
| 195 |
+
"typer-slim 0.21.0\n",
|
| 196 |
+
"typing_extensions 4.15.0\n",
|
| 197 |
+
"urllib3 2.6.3\n",
|
| 198 |
+
"wcwidth 0.2.14\n",
|
| 199 |
+
"wheel 0.45.1\n",
|
| 200 |
+
"widgetsnbextension 4.0.15\n",
|
| 201 |
+
"Note: you may need to restart the kernel to use updated packages.\n"
|
| 202 |
+
]
|
| 203 |
+
}
|
| 204 |
+
],
|
| 205 |
+
"source": [
|
| 206 |
+
"%pip list"
|
| 207 |
+
]
|
| 208 |
+
},
|
| 209 |
+
{
|
| 210 |
+
"cell_type": "code",
|
| 211 |
+
"execution_count": 6,
|
| 212 |
+
"metadata": {
|
| 213 |
+
"id": "xJYUsKdBCPVS"
|
| 214 |
+
},
|
| 215 |
+
"outputs": [],
|
| 216 |
+
"source": [
|
| 217 |
+
"import os\n",
|
| 218 |
+
"import shutil\n",
|
| 219 |
+
"import torch\n",
|
| 220 |
+
"import torch.nn as nn\n",
|
| 221 |
+
"import torch.optim as optim\n",
|
| 222 |
+
"from torchvision import datasets, transforms\n",
|
| 223 |
+
"from timm import create_model\n",
|
| 224 |
+
"from torch.optim.lr_scheduler import CosineAnnealingLR\n",
|
| 225 |
+
"from torch.utils.data import DataLoader\n",
|
| 226 |
+
"from torch.utils.tensorboard import SummaryWriter\n",
|
| 227 |
+
"from tqdm import tqdm # For progress bar\n",
|
| 228 |
+
"from torchvision.transforms import RandAugment\n",
|
| 229 |
+
"from timm.data import Mixup\n",
|
| 230 |
+
"from timm.loss import SoftTargetCrossEntropy\n",
|
| 231 |
+
"from timm.layers import DropPath # Updated import path\n",
|
| 232 |
+
"from timm.scheduler.cosine_lr import CosineLRScheduler\n",
|
| 233 |
+
"\n"
|
| 234 |
+
]
|
| 235 |
+
},
|
| 236 |
+
{
|
| 237 |
+
"cell_type": "code",
|
| 238 |
+
"execution_count": null,
|
| 239 |
+
"metadata": {},
|
| 240 |
+
"outputs": [],
|
| 241 |
+
"source": [
|
| 242 |
+
"import gdown\n",
|
| 243 |
+
"\n",
|
| 244 |
+
"url = 'https://drive.google.com/'\n",
|
| 245 |
+
"output = 'dat2.zip'\n",
|
| 246 |
+
"gdown.download(url, output, quiet=False, fuzzy=True)"
|
| 247 |
+
]
|
| 248 |
+
},
|
| 249 |
+
{
|
| 250 |
+
"cell_type": "code",
|
| 251 |
+
"execution_count": 10,
|
| 252 |
+
"metadata": {
|
| 253 |
+
"colab": {
|
| 254 |
+
"base_uri": "https://localhost:8080/"
|
| 255 |
+
},
|
| 256 |
+
"id": "cbW5kekcM0nQ",
|
| 257 |
+
"outputId": "0c0d198c-d492-4b04-8870-eea889d8dfea"
|
| 258 |
+
},
|
| 259 |
+
"outputs": [
|
| 260 |
+
{
|
| 261 |
+
"name": "stdout",
|
| 262 |
+
"output_type": "stream",
|
| 263 |
+
"text": [
|
| 264 |
+
"Files extracted to: /workspace/red\n"
|
| 265 |
+
]
|
| 266 |
+
}
|
| 267 |
+
],
|
| 268 |
+
"source": [
|
| 269 |
+
"import zipfile\n",
|
| 270 |
+
"import os\n",
|
| 271 |
+
"\n",
|
| 272 |
+
"zip_file_name = '/workspace/dat2.zip'\n",
|
| 273 |
+
"extract_dir = '/workspace/red' # Target directory for extraction\n",
|
| 274 |
+
"\n",
|
| 275 |
+
"# Create the target directory if it doesn't exist\n",
|
| 276 |
+
"if not os.path.exists(extract_dir):\n",
|
| 277 |
+
" os.makedirs(extract_dir)\n",
|
| 278 |
+
"\n",
|
| 279 |
+
"with zipfile.ZipFile(zip_file_name, 'r') as zip_ref:\n",
|
| 280 |
+
" # Extract all contents to the specified directory\n",
|
| 281 |
+
" zip_ref.extractall(extract_dir)\n",
|
| 282 |
+
"\n",
|
| 283 |
+
"print(f\"Files extracted to: {extract_dir}\")"
|
| 284 |
+
]
|
| 285 |
+
},
|
| 286 |
+
{
|
| 287 |
+
"cell_type": "code",
|
| 288 |
+
"execution_count": 11,
|
| 289 |
+
"metadata": {
|
| 290 |
+
"id": "5D1NQo1LCStD"
|
| 291 |
+
},
|
| 292 |
+
"outputs": [],
|
| 293 |
+
"source": [
|
| 294 |
+
"# Paths and Constants\n",
|
| 295 |
+
"data_dir = \"/workspace/red/splits1\"\n",
|
| 296 |
+
"num_classes = 7\n",
|
| 297 |
+
"batch_size = 128 # Adjust based on GPU memory\n",
|
| 298 |
+
"num_epochs = 50 # Increased number of epochs for better convergence\n",
|
| 299 |
+
"learning_rate = 5e-4 # Lowered learning rate for fine-tuning\n",
|
| 300 |
+
"weight_decay = 0.01 # Adjusted weight decay\n",
|
| 301 |
+
"image_size = 224\n",
|
| 302 |
+
"log_interval = 100 # Log metrics every 100 batches\n"
|
| 303 |
+
]
|
| 304 |
+
},
|
| 305 |
+
{
|
| 306 |
+
"cell_type": "code",
|
| 307 |
+
"execution_count": 12,
|
| 308 |
+
"metadata": {
|
| 309 |
+
"id": "JgcWhUooCfC7"
|
| 310 |
+
},
|
| 311 |
+
"outputs": [],
|
| 312 |
+
"source": [
|
| 313 |
+
"transform_train = transforms.Compose([\n",
|
| 314 |
+
" transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),\n",
|
| 315 |
+
" RandAugment(), # Enhanced augmentation\n",
|
| 316 |
+
" transforms.ToTensor(),\n",
|
| 317 |
+
" transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n",
|
| 318 |
+
" transforms.RandomErasing(p=0.1),\n",
|
| 319 |
+
"])\n"
|
| 320 |
+
]
|
| 321 |
+
},
|
| 322 |
+
{
|
| 323 |
+
"cell_type": "code",
|
| 324 |
+
"execution_count": 13,
|
| 325 |
+
"metadata": {
|
| 326 |
+
"id": "5YWSDRJrCgwF"
|
| 327 |
+
},
|
| 328 |
+
"outputs": [],
|
| 329 |
+
"source": [
|
| 330 |
+
"transform_test = transforms.Compose([\n",
|
| 331 |
+
" transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC),\n",
|
| 332 |
+
" transforms.ToTensor(),\n",
|
| 333 |
+
" transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n",
|
| 334 |
+
"])\n"
|
| 335 |
+
]
|
| 336 |
+
},
|
| 337 |
+
{
|
| 338 |
+
"cell_type": "code",
|
| 339 |
+
"execution_count": 14,
|
| 340 |
+
"metadata": {
|
| 341 |
+
"id": "sqcG-hMtChPa"
|
| 342 |
+
},
|
| 343 |
+
"outputs": [],
|
| 344 |
+
"source": [
|
| 345 |
+
"train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=transform_train)\n",
|
| 346 |
+
"train_loader = DataLoader(\n",
|
| 347 |
+
" train_dataset,\n",
|
| 348 |
+
" batch_size=batch_size,\n",
|
| 349 |
+
" shuffle=True,\n",
|
| 350 |
+
" num_workers=16, # Reduced from 8 to 2\n",
|
| 351 |
+
" pin_memory=True,\n",
|
| 352 |
+
" prefetch_factor=4,\n",
|
| 353 |
+
" persistent_workers=True\n",
|
| 354 |
+
")\n"
|
| 355 |
+
]
|
| 356 |
+
},
|
| 357 |
+
{
|
| 358 |
+
"cell_type": "code",
|
| 359 |
+
"execution_count": 15,
|
| 360 |
+
"metadata": {
|
| 361 |
+
"id": "h2QXKLTqCmT8"
|
| 362 |
+
},
|
| 363 |
+
"outputs": [],
|
| 364 |
+
"source": [
|
| 365 |
+
"val_images_dir = os.path.join(\"/workspace/red/splits1/val\", '')\n",
|
| 366 |
+
"val_dataset = datasets.ImageFolder(val_images_dir, transform=transform_test)\n",
|
| 367 |
+
"val_loader = DataLoader(\n",
|
| 368 |
+
" val_dataset,\n",
|
| 369 |
+
" batch_size=batch_size,\n",
|
| 370 |
+
" shuffle=False,\n",
|
| 371 |
+
" num_workers=16, # Reduced from 8 to 2\n",
|
| 372 |
+
" pin_memory=True,\n",
|
| 373 |
+
" prefetch_factor=4,\n",
|
| 374 |
+
" persistent_workers=True\n",
|
| 375 |
+
")\n"
|
| 376 |
+
]
|
| 377 |
+
},
|
| 378 |
+
{
|
| 379 |
+
"cell_type": "code",
|
| 380 |
+
"execution_count": 16,
|
| 381 |
+
"metadata": {
|
| 382 |
+
"colab": {
|
| 383 |
+
"base_uri": "https://localhost:8080/"
|
| 384 |
+
},
|
| 385 |
+
"id": "EQzvAErvCpJM",
|
| 386 |
+
"outputId": "704d863e-3bdf-487f-e62e-8c5f724ed99f"
|
| 387 |
+
},
|
| 388 |
+
"outputs": [
|
| 389 |
+
{
|
| 390 |
+
"data": {
|
| 391 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 392 |
+
"model_id": "50ffe1bd5e6443719a4752151afd6b14",
|
| 393 |
+
"version_major": 2,
|
| 394 |
+
"version_minor": 0
|
| 395 |
+
},
|
| 396 |
+
"text/plain": [
|
| 397 |
+
"model.safetensors: 0%| | 0.00/88.2M [00:00<?, ?B/s]"
|
| 398 |
+
]
|
| 399 |
+
},
|
| 400 |
+
"metadata": {},
|
| 401 |
+
"output_type": "display_data"
|
| 402 |
+
}
|
| 403 |
+
],
|
| 404 |
+
"source": [
|
| 405 |
+
"model = create_model('vit_small_patch16_224.augreg_in1k', pretrained=True, num_classes=num_classes)\n"
|
| 406 |
+
]
|
| 407 |
+
},
|
| 408 |
+
{
|
| 409 |
+
"cell_type": "code",
|
| 410 |
+
"execution_count": 17,
|
| 411 |
+
"metadata": {
|
| 412 |
+
"id": "KtTwgd7nC4VA"
|
| 413 |
+
},
|
| 414 |
+
"outputs": [],
|
| 415 |
+
"source": [
|
| 416 |
+
"def apply_stochastic_depth(model, drop_prob):\n",
|
| 417 |
+
" for module in model.modules():\n",
|
| 418 |
+
" if isinstance(module, DropPath):\n",
|
| 419 |
+
" module.drop_prob = drop_prob"
|
| 420 |
+
]
|
| 421 |
+
},
|
| 422 |
+
{
|
| 423 |
+
"cell_type": "code",
|
| 424 |
+
"execution_count": 18,
|
| 425 |
+
"metadata": {
|
| 426 |
+
"id": "2lHD5QknC6FK"
|
| 427 |
+
},
|
| 428 |
+
"outputs": [],
|
| 429 |
+
"source": [
|
| 430 |
+
"apply_stochastic_depth(model, drop_prob=0.1)\n"
|
| 431 |
+
]
|
| 432 |
+
},
|
| 433 |
+
{
|
| 434 |
+
"cell_type": "code",
|
| 435 |
+
"execution_count": 19,
|
| 436 |
+
"metadata": {
|
| 437 |
+
"id": "17jrn7IsC7WT"
|
| 438 |
+
},
|
| 439 |
+
"outputs": [],
|
| 440 |
+
"source": [
|
| 441 |
+
"for param in model.parameters():\n",
|
| 442 |
+
" param.requires_grad = True"
|
| 443 |
+
]
|
| 444 |
+
},
|
| 445 |
+
{
|
| 446 |
+
"cell_type": "code",
|
| 447 |
+
"execution_count": 20,
|
| 448 |
+
"metadata": {
|
| 449 |
+
"colab": {
|
| 450 |
+
"base_uri": "https://localhost:8080/"
|
| 451 |
+
},
|
| 452 |
+
"id": "cLT8zi0uDH2_",
|
| 453 |
+
"outputId": "c81d6fba-6b58-462a-aa5f-163b5afd8a1d"
|
| 454 |
+
},
|
| 455 |
+
"outputs": [
|
| 456 |
+
{
|
| 457 |
+
"name": "stdout",
|
| 458 |
+
"output_type": "stream",
|
| 459 |
+
"text": [
|
| 460 |
+
"Using device: cuda\n"
|
| 461 |
+
]
|
| 462 |
+
}
|
| 463 |
+
],
|
| 464 |
+
"source": [
|
| 465 |
+
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
| 466 |
+
"print(f\"Using device: {device}\")"
|
| 467 |
+
]
|
| 468 |
+
},
|
| 469 |
+
{
|
| 470 |
+
"cell_type": "code",
|
| 471 |
+
"execution_count": 21,
|
| 472 |
+
"metadata": {
|
| 473 |
+
"id": "-gBcVTPXDKSK"
|
| 474 |
+
},
|
| 475 |
+
"outputs": [
|
| 476 |
+
{
|
| 477 |
+
"name": "stdout",
|
| 478 |
+
"output_type": "stream",
|
| 479 |
+
"text": [
|
| 480 |
+
"Using 2 GPUs\n"
|
| 481 |
+
]
|
| 482 |
+
}
|
| 483 |
+
],
|
| 484 |
+
"source": [
|
| 485 |
+
"if torch.cuda.device_count() > 1:\n",
|
| 486 |
+
" print(f\"Using {torch.cuda.device_count()} GPUs\")\n",
|
| 487 |
+
" model = nn.DataParallel(model) # This will use all available GPUs"
|
| 488 |
+
]
|
| 489 |
+
},
|
| 490 |
+
{
|
| 491 |
+
"cell_type": "code",
|
| 492 |
+
"execution_count": 22,
|
| 493 |
+
"metadata": {
|
| 494 |
+
"id": "W-stw1OBDLqp"
|
| 495 |
+
},
|
| 496 |
+
"outputs": [],
|
| 497 |
+
"source": [
|
| 498 |
+
"model = model.to(device)\n"
|
| 499 |
+
]
|
| 500 |
+
},
|
| 501 |
+
{
|
| 502 |
+
"cell_type": "code",
|
| 503 |
+
"execution_count": 23,
|
| 504 |
+
"metadata": {},
|
| 505 |
+
"outputs": [],
|
| 506 |
+
"source": [
|
| 507 |
+
"class_weights = torch.tensor([1.560, 3.737, 2.242, 0.541, 0.527, 0.970, 1.149], dtype=torch.float)\n",
|
| 508 |
+
"class_weights = class_weights.to(device)"
|
| 509 |
+
]
|
| 510 |
+
},
|
| 511 |
+
{
|
| 512 |
+
"cell_type": "code",
|
| 513 |
+
"execution_count": 24,
|
| 514 |
+
"metadata": {
|
| 515 |
+
"id": "tI3m3KquDPXI"
|
| 516 |
+
},
|
| 517 |
+
"outputs": [],
|
| 518 |
+
"source": [
|
| 519 |
+
"criterion = nn.CrossEntropyLoss(weight=class_weights) # For Mixup and CutMix\n"
|
| 520 |
+
]
|
| 521 |
+
},
|
| 522 |
+
{
|
| 523 |
+
"cell_type": "code",
|
| 524 |
+
"execution_count": 25,
|
| 525 |
+
"metadata": {
|
| 526 |
+
"id": "afX6EEL8DQvq"
|
| 527 |
+
},
|
| 528 |
+
"outputs": [],
|
| 529 |
+
"source": [
|
| 530 |
+
"optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)"
|
| 531 |
+
]
|
| 532 |
+
},
|
| 533 |
+
{
|
| 534 |
+
"cell_type": "code",
|
| 535 |
+
"execution_count": 26,
|
| 536 |
+
"metadata": {
|
| 537 |
+
"id": "Yf0JflY9DR_t"
|
| 538 |
+
},
|
| 539 |
+
"outputs": [],
|
| 540 |
+
"source": [
|
| 541 |
+
"scheduler = CosineLRScheduler(\n",
|
| 542 |
+
" optimizer,\n",
|
| 543 |
+
" t_initial=num_epochs,\n",
|
| 544 |
+
" lr_min=1e-5,\n",
|
| 545 |
+
" warmup_t=5,\n",
|
| 546 |
+
" warmup_lr_init=1e-6,\n",
|
| 547 |
+
" warmup_prefix=True \n",
|
| 548 |
+
")"
|
| 549 |
+
]
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"cell_type": "code",
|
| 553 |
+
"execution_count": 27,
|
| 554 |
+
"metadata": {
|
| 555 |
+
"id": "jmO4QHy3DT0j"
|
| 556 |
+
},
|
| 557 |
+
"outputs": [],
|
| 558 |
+
"source": [
|
| 559 |
+
"scaler = torch.amp.GradScaler(device='cuda') # Updated instantiation\n"
|
| 560 |
+
]
|
| 561 |
+
},
|
| 562 |
+
{
|
| 563 |
+
"cell_type": "code",
|
| 564 |
+
"execution_count": 28,
|
| 565 |
+
"metadata": {
|
| 566 |
+
"id": "pW1uIqzeDVZv"
|
| 567 |
+
},
|
| 568 |
+
"outputs": [],
|
| 569 |
+
"source": [
|
| 570 |
+
"writer = SummaryWriter() # For TensorBoard logging\n"
|
| 571 |
+
]
|
| 572 |
+
},
|
| 573 |
+
{
|
| 574 |
+
"cell_type": "code",
|
| 575 |
+
"execution_count": 29,
|
| 576 |
+
"metadata": {
|
| 577 |
+
"id": "eFQv3QNYDWyq"
|
| 578 |
+
},
|
| 579 |
+
"outputs": [],
|
| 580 |
+
"source": [
|
| 581 |
+
"def train_one_epoch(epoch):\n",
|
| 582 |
+
" model.train()\n",
|
| 583 |
+
" running_loss, correct, total = 0.0, 0, 0\n",
|
| 584 |
+
"\n",
|
| 585 |
+
" # Progress bar for training loop\n",
|
| 586 |
+
" train_loader_tqdm = tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Training]\", leave=False)\n",
|
| 587 |
+
" for batch_idx, (images, labels) in enumerate(train_loader_tqdm):\n",
|
| 588 |
+
" images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True)\n",
|
| 589 |
+
"\n",
|
| 590 |
+
" optimizer.zero_grad()\n",
|
| 591 |
+
"\n",
|
| 592 |
+
" with torch.cuda.amp.autocast():\n",
|
| 593 |
+
" outputs = model(images)\n",
|
| 594 |
+
" loss = criterion(outputs, labels)\n",
|
| 595 |
+
"\n",
|
| 596 |
+
" scaler.scale(loss).backward()\n",
|
| 597 |
+
" scaler.step(optimizer)\n",
|
| 598 |
+
" scaler.update()\n",
|
| 599 |
+
"\n",
|
| 600 |
+
" running_loss += loss.item() * images.size(0)\n",
|
| 601 |
+
" total += labels.size(0)\n",
|
| 602 |
+
"\n",
|
| 603 |
+
" # Since labels are soft, calculate accuracy based on predicted class vs hard labels\n",
|
| 604 |
+
" _, predicted = outputs.max(1)\n",
|
| 605 |
+
" correct += predicted.eq(labels).sum().item()\n",
|
| 606 |
+
"\n",
|
| 607 |
+
" # Update progress bar (accuracy in percentage)\n",
|
| 608 |
+
" if (batch_idx + 1) % log_interval == 0 or (batch_idx + 1) == len(train_loader):\n",
|
| 609 |
+
" current_loss = loss.item()\n",
|
| 610 |
+
" current_acc = 100. * correct / total\n",
|
| 611 |
+
" train_loader_tqdm.set_postfix(loss=f\"{current_loss:.4f}\", accuracy=f\"{current_acc:.2f}%\")\n",
|
| 612 |
+
"\n",
|
| 613 |
+
" epoch_loss = running_loss / total\n",
|
| 614 |
+
" epoch_acc = 100. * correct / total # Multiply by 100 to get percentage\n",
|
| 615 |
+
" writer.add_scalar('Loss/train', epoch_loss, epoch)\n",
|
| 616 |
+
" writer.add_scalar('Accuracy/train', epoch_acc, epoch)\n",
|
| 617 |
+
" print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f}, Acc: {epoch_acc:.2f}%\") # Acc in %\n"
|
| 618 |
+
]
|
| 619 |
+
},
|
| 620 |
+
{
|
| 621 |
+
"cell_type": "code",
|
| 622 |
+
"execution_count": 30,
|
| 623 |
+
"metadata": {
|
| 624 |
+
"id": "Bi9RQ6CDDZuE"
|
| 625 |
+
},
|
| 626 |
+
"outputs": [],
|
| 627 |
+
"source": [
|
| 628 |
+
"def validate(epoch):\n",
|
| 629 |
+
" model.eval()\n",
|
| 630 |
+
" val_loss, correct, total = 0.0, 0, 0\n",
|
| 631 |
+
"\n",
|
| 632 |
+
" # Progress bar for validation loop\n",
|
| 633 |
+
" val_loader_tqdm = tqdm(val_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Validation]\", leave=False)\n",
|
| 634 |
+
"\n",
|
| 635 |
+
" criterion_val = nn.CrossEntropyLoss() # Standard loss for validation\n",
|
| 636 |
+
"\n",
|
| 637 |
+
" with torch.no_grad():\n",
|
| 638 |
+
" for batch_idx, (images, labels) in enumerate(val_loader_tqdm):\n",
|
| 639 |
+
" images, labels = images.to(device, non_blocking=True), labels.to(device, non_blocking=True)\n",
|
| 640 |
+
"\n",
|
| 641 |
+
" with torch.cuda.amp.autocast():\n",
|
| 642 |
+
" outputs = model(images)\n",
|
| 643 |
+
" loss = criterion_val(outputs, labels)\n",
|
| 644 |
+
"\n",
|
| 645 |
+
" val_loss += loss.item() * images.size(0)\n",
|
| 646 |
+
" total += labels.size(0)\n",
|
| 647 |
+
" _, predicted = outputs.max(1)\n",
|
| 648 |
+
" correct += predicted.eq(labels).sum().item()\n",
|
| 649 |
+
"\n",
|
| 650 |
+
" # Update progress bar (accuracy in percentage)\n",
|
| 651 |
+
" if (batch_idx + 1) % log_interval == 0 or (batch_idx + 1) == len(val_loader):\n",
|
| 652 |
+
" current_loss = loss.item()\n",
|
| 653 |
+
" current_acc = 100. * correct / total\n",
|
| 654 |
+
" val_loader_tqdm.set_postfix(loss=f\"{current_loss:.4f}\", accuracy=f\"{current_acc:.2f}%\")\n",
|
| 655 |
+
"\n",
|
| 656 |
+
" epoch_loss = val_loss / total\n",
|
| 657 |
+
" epoch_acc = 100. * correct / total # Multiply by 100 to get percentage\n",
|
| 658 |
+
" writer.add_scalar('Loss/val', epoch_loss, epoch)\n",
|
| 659 |
+
" writer.add_scalar('Accuracy/val', epoch_acc, epoch)\n",
|
| 660 |
+
" print(f\"Validation Loss: {epoch_loss:.4f}, Acc: {epoch_acc:.2f}%\") # Acc in %\n",
|
| 661 |
+
"\n",
|
| 662 |
+
" return epoch_acc"
|
| 663 |
+
]
|
| 664 |
+
},
|
| 665 |
+
{
|
| 666 |
+
"cell_type": "code",
|
| 667 |
+
"execution_count": null,
|
| 668 |
+
"metadata": {
|
| 669 |
+
"colab": {
|
| 670 |
+
"base_uri": "https://localhost:8080/",
|
| 671 |
+
"height": 474
|
| 672 |
+
},
|
| 673 |
+
"id": "YJ53kRT3DcuJ",
|
| 674 |
+
"outputId": "ef6e52b1-8f2a-43c3-a5f4-49009ce167d8"
|
| 675 |
+
},
|
| 676 |
+
"outputs": [
|
| 677 |
+
{
|
| 678 |
+
"name": "stderr",
|
| 679 |
+
"output_type": "stream",
|
| 680 |
+
"text": [
|
| 681 |
+
"Epoch 1/50 [Training]: 0%| | 0/420 [00:00<?, ?it/s]/tmp/ipykernel_1207/2336286154.py:12: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
|
| 682 |
+
" with torch.cuda.amp.autocast():\n",
|
| 683 |
+
" "
|
| 684 |
+
]
|
| 685 |
+
},
|
| 686 |
+
{
|
| 687 |
+
"name": "stdout",
|
| 688 |
+
"output_type": "stream",
|
| 689 |
+
"text": [
|
| 690 |
+
"Epoch [1/50], Loss: 1.8604, Acc: 25.83%\n"
|
| 691 |
+
]
|
| 692 |
+
},
|
| 693 |
+
{
|
| 694 |
+
"name": "stderr",
|
| 695 |
+
"output_type": "stream",
|
| 696 |
+
"text": [
|
| 697 |
+
"Epoch 1/50 [Validation]: 0%| | 0/53 [00:00<?, ?it/s]/tmp/ipykernel_1207/1125314546.py:14: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
|
| 698 |
+
" with torch.cuda.amp.autocast():\n",
|
| 699 |
+
" "
|
| 700 |
+
]
|
| 701 |
+
},
|
| 702 |
+
{
|
| 703 |
+
"name": "stdout",
|
| 704 |
+
"output_type": "stream",
|
| 705 |
+
"text": [
|
| 706 |
+
"Validation Loss: 1.6683, Acc: 35.74%\n",
|
| 707 |
+
"New best model saved with accuracy: 35.74%\n"
|
| 708 |
+
]
|
| 709 |
+
},
|
| 710 |
+
{
|
| 711 |
+
"name": "stderr",
|
| 712 |
+
"output_type": "stream",
|
| 713 |
+
"text": [
|
| 714 |
+
" "
|
| 715 |
+
]
|
| 716 |
+
},
|
| 717 |
+
{
|
| 718 |
+
"name": "stdout",
|
| 719 |
+
"output_type": "stream",
|
| 720 |
+
"text": [
|
| 721 |
+
"Epoch [2/50], Loss: 1.1986, Acc: 58.37%\n"
|
| 722 |
+
]
|
| 723 |
+
},
|
| 724 |
+
{
|
| 725 |
+
"name": "stderr",
|
| 726 |
+
"output_type": "stream",
|
| 727 |
+
"text": [
|
| 728 |
+
" "
|
| 729 |
+
]
|
| 730 |
+
},
|
| 731 |
+
{
|
| 732 |
+
"name": "stdout",
|
| 733 |
+
"output_type": "stream",
|
| 734 |
+
"text": [
|
| 735 |
+
"Validation Loss: 0.9391, Acc: 64.96%\n",
|
| 736 |
+
"New best model saved with accuracy: 64.96%\n"
|
| 737 |
+
]
|
| 738 |
+
},
|
| 739 |
+
{
|
| 740 |
+
"name": "stderr",
|
| 741 |
+
"output_type": "stream",
|
| 742 |
+
"text": [
|
| 743 |
+
" "
|
| 744 |
+
]
|
| 745 |
+
},
|
| 746 |
+
{
|
| 747 |
+
"name": "stdout",
|
| 748 |
+
"output_type": "stream",
|
| 749 |
+
"text": [
|
| 750 |
+
"Epoch [3/50], Loss: 1.0441, Acc: 63.76%\n"
|
| 751 |
+
]
|
| 752 |
+
},
|
| 753 |
+
{
|
| 754 |
+
"name": "stderr",
|
| 755 |
+
"output_type": "stream",
|
| 756 |
+
"text": [
|
| 757 |
+
" "
|
| 758 |
+
]
|
| 759 |
+
},
|
| 760 |
+
{
|
| 761 |
+
"name": "stdout",
|
| 762 |
+
"output_type": "stream",
|
| 763 |
+
"text": [
|
| 764 |
+
"Validation Loss: 0.8123, Acc: 69.91%\n",
|
| 765 |
+
"New best model saved with accuracy: 69.91%\n"
|
| 766 |
+
]
|
| 767 |
+
},
|
| 768 |
+
{
|
| 769 |
+
"name": "stderr",
|
| 770 |
+
"output_type": "stream",
|
| 771 |
+
"text": [
|
| 772 |
+
" "
|
| 773 |
+
]
|
| 774 |
+
},
|
| 775 |
+
{
|
| 776 |
+
"name": "stdout",
|
| 777 |
+
"output_type": "stream",
|
| 778 |
+
"text": [
|
| 779 |
+
"Epoch [4/50], Loss: 0.9982, Acc: 65.22%\n"
|
| 780 |
+
]
|
| 781 |
+
},
|
| 782 |
+
{
|
| 783 |
+
"name": "stderr",
|
| 784 |
+
"output_type": "stream",
|
| 785 |
+
"text": [
|
| 786 |
+
" "
|
| 787 |
+
]
|
| 788 |
+
},
|
| 789 |
+
{
|
| 790 |
+
"name": "stdout",
|
| 791 |
+
"output_type": "stream",
|
| 792 |
+
"text": [
|
| 793 |
+
"Validation Loss: 0.8204, Acc: 68.54%\n"
|
| 794 |
+
]
|
| 795 |
+
},
|
| 796 |
+
{
|
| 797 |
+
"name": "stderr",
|
| 798 |
+
"output_type": "stream",
|
| 799 |
+
"text": [
|
| 800 |
+
" "
|
| 801 |
+
]
|
| 802 |
+
},
|
| 803 |
+
{
|
| 804 |
+
"name": "stdout",
|
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"Epoch [11/50], Loss: 0.9211, Acc: 67.15%\n"
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"Validation Loss: 0.8167, Acc: 70.44%\n",
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"Epoch [12/50], Loss: 0.8618, Acc: 69.12%\n"
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"Validation Loss: 0.7712, Acc: 70.79%\n",
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"Epoch [13/50], Loss: 0.8535, Acc: 69.34%\n"
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"Validation Loss: 0.8438, Acc: 68.26%\n"
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"Epoch [14/50], Loss: 0.8238, Acc: 70.29%\n"
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"text": [
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"Validation Loss: 0.7468, Acc: 72.20%\n",
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"text": [
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"Epoch [15/50], Loss: 0.7955, Acc: 71.32%\n"
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"text": [
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"Validation Loss: 0.7421, Acc: 72.94%\n",
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"New best model saved with accuracy: 72.94%\n"
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"text": [
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"Epoch [16/50], Loss: 0.7659, Acc: 72.32%\n"
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"text": [
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"Validation Loss: 0.8212, Acc: 69.08%\n"
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"text": [
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"Epoch [17/50], Loss: 0.7439, Acc: 72.93%\n"
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"Validation Loss: 0.7595, Acc: 72.08%\n"
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"text": [
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"Epoch [18/50], Loss: 0.7126, Acc: 73.81%\n"
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"text": [
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"Validation Loss: 0.7728, Acc: 72.69%\n"
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"text": [
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"Epoch [19/50], Loss: 0.6794, Acc: 75.10%\n"
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"text": [
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"Validation Loss: 0.7778, Acc: 71.33%\n"
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"text": [
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"Epoch [20/50], Loss: 0.6500, Acc: 76.01%\n"
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"text": [
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"Validation Loss: 0.7097, Acc: 74.52%\n",
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"Epoch [24/50], Loss: 0.5343, Acc: 79.65%\n"
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"Validation Loss: 0.6848, Acc: 77.01%\n",
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"Epoch [25/50], Loss: 0.5009, Acc: 80.83%\n"
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"Validation Loss: 0.6942, Acc: 75.85%\n"
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"Epoch [26/50], Loss: 0.4729, Acc: 81.98%\n"
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"Epoch [27/50], Loss: 0.4450, Acc: 82.59%\n"
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"Validation Loss: 0.7061, Acc: 76.64%\n"
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"Epoch [28/50], Loss: 0.4161, Acc: 83.64%\n"
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"Validation Loss: 0.7243, Acc: 76.22%\n"
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"text": [
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"Epoch [29/50], Loss: 0.3894, Acc: 84.72%\n"
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"text": [
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"Validation Loss: 0.7221, Acc: 77.03%\n",
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"output_type": "stream",
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"text": [
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"Epoch [30/50], Loss: 0.3514, Acc: 86.06%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7121, Acc: 78.37%\n",
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"New best model saved with accuracy: 78.37%\n"
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"output_type": "stream",
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"text": [
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"Epoch [31/50], Loss: 0.3326, Acc: 86.66%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7321, Acc: 77.68%\n"
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"output_type": "stream",
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"text": [
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"Epoch [32/50], Loss: 0.3043, Acc: 87.87%\n"
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},
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7001, Acc: 80.12%\n",
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"New best model saved with accuracy: 80.12%\n"
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"output_type": "stream",
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"text": [
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"Epoch [33/50], Loss: 0.2952, Acc: 88.21%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7658, Acc: 78.04%\n"
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"output_type": "stream",
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"text": [
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"Epoch [34/50], Loss: 0.2709, Acc: 89.20%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7202, Acc: 79.50%\n"
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"text": [
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"Epoch [35/50], Loss: 0.2688, Acc: 89.49%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.7348, Acc: 79.95%\n"
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"output_type": "stream",
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"text": [
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"Epoch [36/50], Loss: 0.2305, Acc: 90.76%\n"
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"text": [
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"Validation Loss: 0.7410, Acc: 80.30%\n",
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"New best model saved with accuracy: 80.30%\n"
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"Epoch [37/50], Loss: 0.2143, Acc: 91.46%\n"
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"text": [
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"Validation Loss: 0.7574, Acc: 80.36%\n",
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"New best model saved with accuracy: 80.36%\n"
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"text": [
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"Epoch [38/50], Loss: 0.1931, Acc: 92.13%\n"
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"text": [
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"Validation Loss: 0.7907, Acc: 79.33%\n"
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"text": [
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"Epoch [39/50], Loss: 0.1750, Acc: 92.91%\n"
|
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+
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"text": [
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+
"Validation Loss: 0.8297, Acc: 80.44%\n",
|
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"New best model saved with accuracy: 80.44%\n"
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"text": [
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"Epoch [40/50], Loss: 0.1695, Acc: 93.31%\n"
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"output_type": "stream",
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"text": [
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"Validation Loss: 0.8326, Acc: 80.50%\n",
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"New best model saved with accuracy: 80.50%\n"
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"text": [
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"Epoch [41/50], Loss: 0.1556, Acc: 93.82%\n"
|
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"text": [
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| 1843 |
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"Validation Loss: 0.8221, Acc: 80.55%\n",
|
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"New best model saved with accuracy: 80.55%\n"
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"text": [
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"Epoch [42/50], Loss: 0.1390, Acc: 94.33%\n"
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+
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"output_type": "stream",
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"text": [
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| 1872 |
+
"Validation Loss: 0.8562, Acc: 80.65%\n",
|
| 1873 |
+
"New best model saved with accuracy: 80.65%\n"
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+
]
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+
},
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"output_type": "stream",
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"text": [
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+
"Epoch [43/50], Loss: 0.1334, Acc: 94.76%\n"
|
| 1888 |
+
]
|
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+
},
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+
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"output_type": "stream",
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"text": [
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| 1901 |
+
"Validation Loss: 0.8463, Acc: 80.92%\n",
|
| 1902 |
+
"New best model saved with accuracy: 80.92%\n"
|
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+
]
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"Epoch [44/50], Loss: 0.1305, Acc: 94.73%\n"
|
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"text": [
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"Validation Loss: 0.8475, Acc: 80.89%\n"
|
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"text": [
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| 1944 |
+
"Epoch [45/50], Loss: 0.1145, Acc: 95.42%\n"
|
| 1945 |
+
]
|
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+
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+
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+
"name": "stdout",
|
| 1956 |
+
"output_type": "stream",
|
| 1957 |
+
"text": [
|
| 1958 |
+
"Validation Loss: 0.8956, Acc: 80.97%\n",
|
| 1959 |
+
"New best model saved with accuracy: 80.97%\n"
|
| 1960 |
+
]
|
| 1961 |
+
},
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"output_type": "stream",
|
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+
"text": [
|
| 1973 |
+
"Epoch [46/50], Loss: 0.1095, Acc: 95.56%\n"
|
| 1974 |
+
]
|
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+
},
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+
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+
"name": "stdout",
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+
"output_type": "stream",
|
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+
"text": [
|
| 1987 |
+
"Validation Loss: 0.8992, Acc: 81.41%\n",
|
| 1988 |
+
"New best model saved with accuracy: 81.41%\n"
|
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+
]
|
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+
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+
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| 2000 |
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"output_type": "stream",
|
| 2001 |
+
"text": [
|
| 2002 |
+
"Epoch [47/50], Loss: 0.1039, Acc: 95.84%\n"
|
| 2003 |
+
]
|
| 2004 |
+
},
|
| 2005 |
+
{
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+
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| 2013 |
+
"name": "stdout",
|
| 2014 |
+
"output_type": "stream",
|
| 2015 |
+
"text": [
|
| 2016 |
+
"Validation Loss: 0.8921, Acc: 81.18%\n"
|
| 2017 |
+
]
|
| 2018 |
+
},
|
| 2019 |
+
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| 2020 |
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"name": "stderr",
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|
| 2027 |
+
"name": "stdout",
|
| 2028 |
+
"output_type": "stream",
|
| 2029 |
+
"text": [
|
| 2030 |
+
"Epoch [48/50], Loss: 0.0988, Acc: 96.01%\n"
|
| 2031 |
+
]
|
| 2032 |
+
},
|
| 2033 |
+
{
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| 2034 |
+
"name": "stderr",
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+
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| 2040 |
+
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|
| 2041 |
+
"name": "stdout",
|
| 2042 |
+
"output_type": "stream",
|
| 2043 |
+
"text": [
|
| 2044 |
+
"Validation Loss: 0.8984, Acc: 81.24%\n"
|
| 2045 |
+
]
|
| 2046 |
+
},
|
| 2047 |
+
{
|
| 2048 |
+
"name": "stderr",
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| 2049 |
+
"output_type": "stream",
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"text": [
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| 2052 |
+
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| 2053 |
+
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| 2054 |
+
{
|
| 2055 |
+
"name": "stdout",
|
| 2056 |
+
"output_type": "stream",
|
| 2057 |
+
"text": [
|
| 2058 |
+
"Epoch [49/50], Loss: 0.0942, Acc: 96.17%\n"
|
| 2059 |
+
]
|
| 2060 |
+
},
|
| 2061 |
+
{
|
| 2062 |
+
"name": "stderr",
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| 2063 |
+
"output_type": "stream",
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+
"text": [
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| 2066 |
+
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|
| 2067 |
+
},
|
| 2068 |
+
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|
| 2069 |
+
"name": "stdout",
|
| 2070 |
+
"output_type": "stream",
|
| 2071 |
+
"text": [
|
| 2072 |
+
"Validation Loss: 0.9172, Acc: 81.13%\n"
|
| 2073 |
+
]
|
| 2074 |
+
},
|
| 2075 |
+
{
|
| 2076 |
+
"name": "stderr",
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| 2077 |
+
"output_type": "stream",
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+
"text": [
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+
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|
| 2080 |
+
]
|
| 2081 |
+
},
|
| 2082 |
+
{
|
| 2083 |
+
"name": "stdout",
|
| 2084 |
+
"output_type": "stream",
|
| 2085 |
+
"text": [
|
| 2086 |
+
"Epoch [50/50], Loss: 0.0896, Acc: 96.47%\n"
|
| 2087 |
+
]
|
| 2088 |
+
},
|
| 2089 |
+
{
|
| 2090 |
+
"name": "stderr",
|
| 2091 |
+
"output_type": "stream",
|
| 2092 |
+
"text": [
|
| 2093 |
+
"Epoch 50/50 [Validation]: 75%|███████▌ | 40/53 [00:03<00:00, 19.44it/s]"
|
| 2094 |
+
]
|
| 2095 |
+
}
|
| 2096 |
+
],
|
| 2097 |
+
"source": [
|
| 2098 |
+
"best_acc = 0\n",
|
| 2099 |
+
"for epoch in range(num_epochs):\n",
|
| 2100 |
+
" train_one_epoch(epoch)\n",
|
| 2101 |
+
" val_acc = validate(epoch)\n",
|
| 2102 |
+
"\n",
|
| 2103 |
+
" # Scheduler step\n",
|
| 2104 |
+
" scheduler.step(epoch+1)\n",
|
| 2105 |
+
"\n",
|
| 2106 |
+
" # Save best model\n",
|
| 2107 |
+
" if val_acc > best_acc:\n",
|
| 2108 |
+
" best_acc = val_acc\n",
|
| 2109 |
+
" os.makedirs('./models', exist_ok=True)\n",
|
| 2110 |
+
" # If using DataParallel, save the underlying model\n",
|
| 2111 |
+
" if isinstance(model, nn.DataParallel):\n",
|
| 2112 |
+
" torch.save(model.module.state_dict(), './models/best_vit_tiny_imagenet.pth')\n",
|
| 2113 |
+
" else:\n",
|
| 2114 |
+
" torch.save(model.state_dict(), './models/best_vit_tiny_imagenet.pth')\n",
|
| 2115 |
+
" print(f\"New best model saved with accuracy: {best_acc:.2f}%\")\n",
|
| 2116 |
+
"\n",
|
| 2117 |
+
"print(\"Training complete. Best validation accuracy:\", best_acc)"
|
| 2118 |
+
]
|
| 2119 |
+
},
|
| 2120 |
+
{
|
| 2121 |
+
"cell_type": "code",
|
| 2122 |
+
"execution_count": null,
|
| 2123 |
+
"metadata": {},
|
| 2124 |
+
"outputs": [],
|
| 2125 |
+
"source": []
|
| 2126 |
+
}
|
| 2127 |
+
],
|
| 2128 |
+
"metadata": {
|
| 2129 |
+
"accelerator": "GPU",
|
| 2130 |
+
"colab": {
|
| 2131 |
+
"gpuType": "T4",
|
| 2132 |
+
"provenance": []
|
| 2133 |
+
},
|
| 2134 |
+
"kernelspec": {
|
| 2135 |
+
"display_name": "Python3 (main venv)",
|
| 2136 |
+
"language": "python",
|
| 2137 |
+
"name": "main"
|
| 2138 |
+
},
|
| 2139 |
+
"language_info": {
|
| 2140 |
+
"codemirror_mode": {
|
| 2141 |
+
"name": "ipython",
|
| 2142 |
+
"version": 3
|
| 2143 |
+
},
|
| 2144 |
+
"file_extension": ".py",
|
| 2145 |
+
"mimetype": "text/x-python",
|
| 2146 |
+
"name": "python",
|
| 2147 |
+
"nbconvert_exporter": "python",
|
| 2148 |
+
"pygments_lexer": "ipython3",
|
| 2149 |
+
"version": "3.12.12"
|
| 2150 |
+
}
|
| 2151 |
+
},
|
| 2152 |
+
"nbformat": 4,
|
| 2153 |
+
"nbformat_minor": 4
|
| 2154 |
+
}
|
events.out.tfevents.1772175113.b6936449dab3.1207.0
ADDED
|
Binary file (9.88 kB). View file
|
|
|
model_training_results.csv
ADDED
|
@@ -0,0 +1,51 @@
|
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|
|
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|
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|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Epoch,Model1_Train_Loss,Model1_Val_Acc,Model2_Train_Loss,Model2_Val_Acc
|
| 2 |
+
1,,35.74,2.0360705567318855,22.568616724384142
|
| 3 |
+
2,1.1986,64.96,1.9792786671214126,28.714201857764852
|
| 4 |
+
3,1.0441,69.91,1.7821412383084092,52.3717207174597
|
| 5 |
+
4,0.9982,68.54,1.6183974135435368,61.03818857129263
|
| 6 |
+
5,1.0101,63.25,1.5248827010250547,61.7989283324995
|
| 7 |
+
6,1.0382,63.53,1.4669698419753445,67.19869004684303
|
| 8 |
+
7,0.9866,64.74,1.4290498651956256,70.68914373393275
|
| 9 |
+
8,0.9614,67.26,1.3899061232092278,68.09367889549965
|
| 10 |
+
9,0.9348,67.36,1.3519616517724033,72.85203231661302
|
| 11 |
+
10,0.917,66.42,1.327325906479758,72.13604118488284
|
| 12 |
+
11,0.9211,70.44,1.2994008415053335,72.52386902966192
|
| 13 |
+
12,0.8618,70.79,1.274697577554073,75.3430823016565
|
| 14 |
+
13,0.8535,68.26,1.2592898128135352,73.29952621459961
|
| 15 |
+
14,0.8238,72.2,1.237077219064156,74.82100560727723
|
| 16 |
+
15,0.7955,72.94,1.2224091088942934,75.82040918527754
|
| 17 |
+
16,0.7659,69.08,1.2057472752612173,75.83532527295388
|
| 18 |
+
17,0.7439,72.08,1.1897126553161292,76.04415603014051
|
| 19 |
+
18,0.7126,72.69,1.176309052836952,76.1485714832752
|
| 20 |
+
19,0.6794,71.33,1.1633472284346675,77.08830949059534
|
| 21 |
+
20,0.65,74.52,1.145757243251116,78.43079130063478
|
| 22 |
+
21,0.6196,75.07,1.1302614176387422,78.0429628549726
|
| 23 |
+
22,0.602,73.93,1.1211656371942547,78.62470563328635
|
| 24 |
+
23,0.5602,74.6,1.1059313349461442,78.53520640439235
|
| 25 |
+
24,0.5343,77.01,1.0975406804438412,77.50597055658235
|
| 26 |
+
25,0.5009,75.85,1.084783926392286,78.96778403830699
|
| 27 |
+
26,0.4729,76.18,1.081091641239002,78.60978895838335
|
| 28 |
+
27,0.445,76.64,1.066453042212856,78.5799557747306
|
| 29 |
+
28,0.4161,76.22,1.0537544052566639,78.66945435380595
|
| 30 |
+
29,0.3894,77.03,1.0435457479155235,79.05728320688507
|
| 31 |
+
30,0.3514,78.37,1.0366827436326223,79.53460898205887
|
| 32 |
+
31,0.3326,77.68,1.0267470826942955,79.54952623280819
|
| 33 |
+
32,0.3043,80.12,1.0243510783574228,79.99702059738961
|
| 34 |
+
33,0.2952,78.04,1.0170651106743152,79.59427570898379
|
| 35 |
+
34,0.2709,79.5,1.0055033556011874,80.08651905469507
|
| 36 |
+
35,0.2688,79.95,1.0005212784098667,80.38484843306439
|
| 37 |
+
36,0.2305,80.3,0.9912698331632113,80.41468205144696
|
| 38 |
+
37,0.2143,80.36,0.984084892501101,79.9671874558442
|
| 39 |
+
38,0.1931,79.33,0.9812082930615074,80.16110167810626
|
| 40 |
+
39,0.175,80.44,0.9815773544699381,80.54892974025026
|
| 41 |
+
40,0.1695,80.5,0.9741421825292578,80.45943074351564
|
| 42 |
+
41,0.1556,80.55,0.9691943751567859,80.65334544261486
|
| 43 |
+
42,0.139,80.65,0.9659411455170389,80.66826100907063
|
| 44 |
+
43,0.1334,80.92,0.9666519684084295,80.47434704172868
|
| 45 |
+
44,0.1305,80.89,0.9551008048525267,80.72792770640662
|
| 46 |
+
45,0.1145,80.97,0.9540892773838134,81.02625692203677
|
| 47 |
+
46,0.1095,81.41,0.9528565328372153,80.90692550868579
|
| 48 |
+
47,0.1039,81.18,0.9488438052709023,80.57876276685401
|
| 49 |
+
48,0.0988,81.24,0.9472993114633423,80.7577597679557
|
| 50 |
+
49,0.0942,81.13,0.9416460762754011,80.71301133194515
|
| 51 |
+
50,0.0896,,0.9394862801549537,80.68317776591226
|
model_training_results2.xlsx
ADDED
|
Binary file (33.1 kB). View file
|
|
|
test/New Text Document.txt
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Class Class_ID Precision Recall F1-Score Support
|
| 2 |
+
happy 3 0.918699 0.937759 0.928131 482
|
| 3 |
+
surprise 6 0.879310 0.796875 0.836066 128
|
| 4 |
+
angry 0 0.815789 0.849315 0.832215 73
|
| 5 |
+
sad 5 0.868750 0.727749 0.792023 191
|
| 6 |
+
neutral 4 0.724014 0.870690 0.790607 232
|
| 7 |
+
fear 2 0.647059 0.500000 0.564103 22
|
| 8 |
+
disgust 1 0.606557 0.506849 0.552239 73
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
================================================================================
|
| 13 |
+
OVERALL METRICS SUMMARY
|
| 14 |
+
================================================================================
|
| 15 |
+
Metric Score
|
| 16 |
+
Accuracy 0.836803
|
| 17 |
+
Precision (Macro) 0.780026
|
| 18 |
+
Precision (Weighted) 0.838746
|
| 19 |
+
Recall (Macro) 0.741320
|
| 20 |
+
Recall (Weighted) 0.836803
|
| 21 |
+
F1-Score (Macro) 0.756483
|
| 22 |
+
F1-Score (Weighted) 0.834761
|
test/download (1).png
ADDED
.png)
|
test/download (3).png
ADDED
.png)
|
test/download.png
ADDED
|
test/perclassmetrics.csv
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Class,Class_ID,Precision,Recall,F1-Score
|
| 2 |
+
happy,3,0.918699,0.937759,0.928131
|
| 3 |
+
surprise,6,0.87931,0.796875,0.836066
|
| 4 |
+
angry,0,0.815789,0.849315,0.832215
|
| 5 |
+
sad,5,0.86875,0.727749,0.792023
|
| 6 |
+
neutral,4,0.724014,0.87069,0.790607
|
| 7 |
+
fear,2,0.647059,0.5,0.564103
|
| 8 |
+
disgust,1,0.606557,0.506849,0.552239
|
val/New Text Document.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Class Class_ID Precision Recall F1-Score Support
|
| 2 |
+
happy 3 0.924686 0.934461 0.929548 473
|
| 3 |
+
surprise 6 0.863636 0.766129 0.811966 124
|
| 4 |
+
neutral 4 0.794326 0.814545 0.804309 275
|
| 5 |
+
angry 0 0.741176 0.840000 0.787500 75
|
| 6 |
+
sad 5 0.775758 0.771084 0.773414 166
|
| 7 |
+
fear 2 0.800000 0.727273 0.761905 33
|
| 8 |
+
disgust 1 0.625000 0.583333 0.603448 60
|
| 9 |
+
|
| 10 |
+
Metric Score
|
| 11 |
+
Accuracy 0.838308
|
| 12 |
+
Precision (Macro) 0.789226
|
| 13 |
+
Precision (Weighted) 0.838450
|
| 14 |
+
Recall (Macro) 0.776689
|
| 15 |
+
Recall (Weighted) 0.838308
|
| 16 |
+
F1-Score (Macro) 0.781727
|
| 17 |
+
F1-Score (Weighted) 0.837764
|
val/download (1).png
ADDED
.png)
|
val/download (2).png
ADDED
.png)
|
val/download.png
ADDED
|
val/perclass.csv
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Class,Class_ID,Precision,Recall,F1-Score,Support
|
| 2 |
+
happy,3,0.924686,0.934461,0.929548,473
|
| 3 |
+
surprise,6,0.863636,0.766129,0.811966,124
|
| 4 |
+
neutral,4,0.794326,0.814545,0.804309,275
|
| 5 |
+
angry,0,0.741176,0.840000,0.787500,75
|
| 6 |
+
sad,5,0.775758,0.771084,0.773414,166
|
| 7 |
+
fear,2,0.800000,0.727273,0.761905,33
|
| 8 |
+
disgust,1,0.625000,0.583333,0.603448,60
|
vitsmall2.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bc3993eb6db745097c62b3a5ee00380edc8e0b998dbdb4a039083ad12628ff42
|
| 3 |
+
size 86734865
|