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Browse files- README.md +4 -4
- app.py +118 -0
- images/akiec.jpg +0 -0
- images/bcc.jpg +0 -0
- images/bkl.jpg +0 -0
- images/df.jpg +0 -0
- images/mel.jpg +0 -0
- images/nv.jpg +0 -0
- images/vasc.jpg +0 -0
- models/DeiT/config.json +39 -0
- models/DeiT/preprocessor_config.json +39 -0
- models/DeiT/pytorch_model.bin +3 -0
- models/DenseNet121/best_model.pth +3 -0
- models/DenseNet121/config.json +25 -0
- models/DenseNet121/logs/logs_2021-12-12-14-52-26.txt +1199 -0
- models/DenseNet121/logs/test_logs_acc_2021-12-12-14-52-26.txt +40 -0
- models/DenseNet121/logs/train_logs_acc_2021-12-12-14-52-26.txt +40 -0
- models/DenseNet121/logs/train_logs_loss_2021-12-12-14-52-26.txt +40 -0
- models/MobileNetV2/best_model.pth +3 -0
- models/MobileNetV2/config.json +25 -0
- models/MobileNetV2/logs/logs_2021-12-12-15-41-03.txt +984 -0
- models/MobileNetV2/logs/test_logs_acc_2021-12-12-15-41-03.txt +40 -0
- models/MobileNetV2/logs/train_logs_acc_2021-12-12-15-41-03.txt +40 -0
- models/MobileNetV2/logs/train_logs_loss_2021-12-12-15-41-03.txt +40 -0
- models/ShuffleNetV2/best_model.pth +3 -0
- models/ShuffleNetV2/config.json +25 -0
- models/ShuffleNetV2/logs/logs_2021-12-12-15-31-56.txt +945 -0
- models/ShuffleNetV2/logs/test_logs_acc_2021-12-12-15-31-56.txt +40 -0
- models/ShuffleNetV2/logs/train_logs_acc_2021-12-12-15-31-56.txt +40 -0
- models/ShuffleNetV2/logs/train_logs_loss_2021-12-12-15-31-56.txt +40 -0
- models/VGG16/best_model.pth +3 -0
- models/VGG16/config.json +25 -0
- models/VGG16/logs/logs_2021-12-12-15-09-07.txt +744 -0
- models/VGG16/logs/test_logs_acc_2021-12-12-15-09-07.txt +40 -0
- models/VGG16/logs/train_logs_acc_2021-12-12-15-09-07.txt +40 -0
- models/VGG16/logs/train_logs_loss_2021-12-12-15-09-07.txt +40 -0
- ressources/models.csv +6 -0
- ressources/thumbnail.png +0 -0
README.md
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---
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title: Skin Cancer
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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app_file: app.py
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pinned:
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---
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# Configuration
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---
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title: Skin Cancer
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emoji: ⚕️
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colorFrom: red
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colorTo: red
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sdk: gradio
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app_file: app.py
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pinned: true
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---
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# Configuration
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app.py
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import gradio as gr
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import numpy as np
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from PIL import Image
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from transformers import DeiTFeatureExtractor, DeiTForImageClassification
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from hugsvision.inference.VisionClassifierInference import VisionClassifierInference
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from hugsvision.inference.TorchVisionClassifierInference import TorchVisionClassifierInference
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models_name = [
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"VGG16",
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"DeiT",
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"DenseNet121",
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"MobileNetV2",
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"ShuffleNetV2",
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]
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radio = gr.inputs.Radio(models_name, default="DenseNet121", type="value")
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def predict_image(image, model_name):
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image = Image.fromarray(np.uint8(image)).convert('RGB')
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model_path = "./models/" + model_name
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if model_name == "DeiT":
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model = VisionClassifierInference(
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feature_extractor = DeiTFeatureExtractor.from_pretrained(model_path),
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model = DeiTForImageClassification.from_pretrained(model_path),
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)
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else:
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model = TorchVisionClassifierInference(
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model_path = model_path
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)
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pred = model.predict_image(img=image, return_str=False)
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for key in pred.keys():
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pred[key] = pred[key]/100
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return pred
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id2label = ["akiec", "bcc", "bkl", "df", "mel", "nv", "vasc"]
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samples = [["images/" + p + ".jpg"] for p in id2label]
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print(samples)
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image = gr.inputs.Image(shape=(224, 224), label="Upload Your Image Here")
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label = gr.outputs.Label(num_top_classes=len(id2label))
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interface = gr.Interface(
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fn=predict_image,
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inputs=[image,radio],
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outputs=label,
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capture_session=True,
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allow_flagging=False,
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thumbnail="ressources/thumbnail.png",
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article="""
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<html style="color: white;">
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<style type="text/css">
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.tg {border-collapse:collapse;border-spacing:0;}
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.tg td{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
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overflow:hidden;padding:10px 5px;word-break:normal;}
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.tg th{border-color:black;border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;
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font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}
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.tg .tg-v0zy{background-color:#efefef;color:#000000;font-weight:bold;text-align:center;vertical-align:top}
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.tg .tg-4jb6{background-color:#ffffff;color:#333333;text-align:center;vertical-align:top}
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</style>
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<table class="tg">
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<thead>
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<tr>
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<th class="tg-v0zy">Model</th>
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<th class="tg-v0zy">Accuracy</th>
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<th class="tg-v0zy">Size</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td class="tg-4jb6">VGG16</td>
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<td class="tg-4jb6">38.27%</td>
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<td class="tg-4jb6">512.0 MB</td>
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</tr>
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<tr>
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<td class="tg-4jb6">DeiT</td>
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<td class="tg-4jb6">71.60%</td>
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<td class="tg-4jb6">327.0 MB</td>
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</tr>
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<tr>
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<td class="tg-4jb6">DenseNet121</td>
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<td class="tg-4jb6">77.78%</td>
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<td class="tg-4jb6">27.1 MB</td>
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</tr>
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<tr>
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<td class="tg-4jb6">MobileNetV2</td>
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<td class="tg-4jb6">75.31%</td>
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<td class="tg-4jb6">8.77 MB</td>
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</tr>
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<tr>
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<td class="tg-4jb6">ShuffleNetV2</td>
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<td class="tg-4jb6">76.54%</td>
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<td class="tg-4jb6">4.99 MB</td>
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</tr>
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</tbody>
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</table>
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</html>
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""",
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theme="darkhuggingface",
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title="HAM10000: Training and using a TorchVision Image Classifier in 5 min to identify skin cancer",
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description="A fast and easy tutorial to train a TorchVision Image Classifier that can help dermatologist in their identification procedures Melanoma cases with HugsVision and HAM10000 dataset.",
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allow_screenshot=True,
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show_tips=False,
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encrypt=False,
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examples=samples,
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)
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interface.launch()
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images/akiec.jpg
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images/bcc.jpg
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images/bkl.jpg
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images/df.jpg
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images/mel.jpg
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images/nv.jpg
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images/vasc.jpg
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models/DeiT/config.json
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{
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"_name_or_path": "facebook/deit-base-distilled-patch16-224",
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"architectures": [
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"DeiTForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "akiec",
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"1": "bcc",
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"2": "bkl",
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"3": "df",
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"4": "mel",
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"5": "nv",
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"6": "vasc"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"akiec": "0",
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"bcc": "1",
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"bkl": "2",
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"df": "3",
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"mel": "4",
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"nv": "5",
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"vasc": "6"
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},
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| 31 |
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"layer_norm_eps": 1e-12,
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| 32 |
+
"model_type": "deit",
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+
"num_attention_heads": 12,
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+
"num_channels": 3,
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| 35 |
+
"num_hidden_layers": 12,
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| 36 |
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"patch_size": 16,
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"torch_dtype": "float32",
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"transformers_version": "4.10.0"
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}
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models/DeiT/preprocessor_config.json
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{
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"_name_or_path": "facebook/deit-base-distilled-patch16-224",
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"architectures": [
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"DeiTForImageClassification"
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],
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+
"attention_probs_dropout_prob": 0.0,
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| 7 |
+
"hidden_act": "gelu",
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| 8 |
+
"hidden_dropout_prob": 0.0,
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| 9 |
+
"hidden_size": 768,
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| 10 |
+
"id2label": {
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| 11 |
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"0": "akiec",
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"1": "bcc",
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"2": "bkl",
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| 14 |
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"3": "df",
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| 15 |
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"4": "mel",
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| 16 |
+
"5": "nv",
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| 17 |
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"6": "vasc"
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},
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| 19 |
+
"image_size": 224,
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| 20 |
+
"initializer_range": 0.02,
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| 21 |
+
"intermediate_size": 3072,
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| 22 |
+
"label2id": {
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| 23 |
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"akiec": "0",
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| 24 |
+
"bcc": "1",
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| 25 |
+
"bkl": "2",
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| 26 |
+
"df": "3",
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| 27 |
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"mel": "4",
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| 28 |
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"nv": "5",
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| 29 |
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"vasc": "6"
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| 30 |
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},
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| 31 |
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"layer_norm_eps": 1e-12,
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| 32 |
+
"model_type": "deit",
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| 33 |
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"num_attention_heads": 12,
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| 34 |
+
"num_channels": 3,
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| 35 |
+
"num_hidden_layers": 12,
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| 36 |
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"patch_size": 16,
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| 37 |
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"torch_dtype": "float32",
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"transformers_version": "4.10.0"
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}
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models/DeiT/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:8cf13380eaf41654e6eaa615ba865c88dfa02704edeab6b611b65fbbe4241485
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size 343301999
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models/DenseNet121/best_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:cb3e6f2603beb6ada576cdacaac0bf0a0acb02f6830acc8518592cf9332f9c6e
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size 28470227
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models/DenseNet121/config.json
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{
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"num_classes": 7,
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"hidden_size": 1024,
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"id2label": {
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| 5 |
+
"0": "akiec",
|
| 6 |
+
"1": "bcc",
|
| 7 |
+
"2": "bkl",
|
| 8 |
+
"3": "df",
|
| 9 |
+
"4": "mel",
|
| 10 |
+
"5": "nv",
|
| 11 |
+
"6": "vasc"
|
| 12 |
+
},
|
| 13 |
+
"label2id": {
|
| 14 |
+
"akiec": "0",
|
| 15 |
+
"bcc": "1",
|
| 16 |
+
"bkl": "2",
|
| 17 |
+
"df": "3",
|
| 18 |
+
"mel": "4",
|
| 19 |
+
"nv": "5",
|
| 20 |
+
"vasc": "6"
|
| 21 |
+
},
|
| 22 |
+
"architectures": [
|
| 23 |
+
"densenet121"
|
| 24 |
+
]
|
| 25 |
+
}
|
models/DenseNet121/logs/logs_2021-12-12-14-52-26.txt
ADDED
|
@@ -0,0 +1,1199 @@
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|
| 1 |
+
==================================================
|
| 2 |
+
Model architecture:
|
| 3 |
+
==================================================
|
| 4 |
+
DenseNet(
|
| 5 |
+
(features): Sequential(
|
| 6 |
+
(conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
|
| 7 |
+
(norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 8 |
+
(relu0): ReLU(inplace=True)
|
| 9 |
+
(pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
| 10 |
+
(denseblock1): _DenseBlock(
|
| 11 |
+
(denselayer1): _DenseLayer(
|
| 12 |
+
(norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 13 |
+
(relu1): ReLU(inplace=True)
|
| 14 |
+
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 15 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 16 |
+
(relu2): ReLU(inplace=True)
|
| 17 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 18 |
+
)
|
| 19 |
+
(denselayer2): _DenseLayer(
|
| 20 |
+
(norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 21 |
+
(relu1): ReLU(inplace=True)
|
| 22 |
+
(conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 23 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 24 |
+
(relu2): ReLU(inplace=True)
|
| 25 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 26 |
+
)
|
| 27 |
+
(denselayer3): _DenseLayer(
|
| 28 |
+
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 29 |
+
(relu1): ReLU(inplace=True)
|
| 30 |
+
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 31 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 32 |
+
(relu2): ReLU(inplace=True)
|
| 33 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 34 |
+
)
|
| 35 |
+
(denselayer4): _DenseLayer(
|
| 36 |
+
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 37 |
+
(relu1): ReLU(inplace=True)
|
| 38 |
+
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 39 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 40 |
+
(relu2): ReLU(inplace=True)
|
| 41 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 42 |
+
)
|
| 43 |
+
(denselayer5): _DenseLayer(
|
| 44 |
+
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 45 |
+
(relu1): ReLU(inplace=True)
|
| 46 |
+
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 47 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 48 |
+
(relu2): ReLU(inplace=True)
|
| 49 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 50 |
+
)
|
| 51 |
+
(denselayer6): _DenseLayer(
|
| 52 |
+
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 53 |
+
(relu1): ReLU(inplace=True)
|
| 54 |
+
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 55 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 56 |
+
(relu2): ReLU(inplace=True)
|
| 57 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 58 |
+
)
|
| 59 |
+
)
|
| 60 |
+
(transition1): _Transition(
|
| 61 |
+
(norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 62 |
+
(relu): ReLU(inplace=True)
|
| 63 |
+
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 64 |
+
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
|
| 65 |
+
)
|
| 66 |
+
(denseblock2): _DenseBlock(
|
| 67 |
+
(denselayer1): _DenseLayer(
|
| 68 |
+
(norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 69 |
+
(relu1): ReLU(inplace=True)
|
| 70 |
+
(conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 71 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 72 |
+
(relu2): ReLU(inplace=True)
|
| 73 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 74 |
+
)
|
| 75 |
+
(denselayer2): _DenseLayer(
|
| 76 |
+
(norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 77 |
+
(relu1): ReLU(inplace=True)
|
| 78 |
+
(conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 79 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 80 |
+
(relu2): ReLU(inplace=True)
|
| 81 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 82 |
+
)
|
| 83 |
+
(denselayer3): _DenseLayer(
|
| 84 |
+
(norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 85 |
+
(relu1): ReLU(inplace=True)
|
| 86 |
+
(conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 87 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 88 |
+
(relu2): ReLU(inplace=True)
|
| 89 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 90 |
+
)
|
| 91 |
+
(denselayer4): _DenseLayer(
|
| 92 |
+
(norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 93 |
+
(relu1): ReLU(inplace=True)
|
| 94 |
+
(conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 95 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 96 |
+
(relu2): ReLU(inplace=True)
|
| 97 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 98 |
+
)
|
| 99 |
+
(denselayer5): _DenseLayer(
|
| 100 |
+
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 101 |
+
(relu1): ReLU(inplace=True)
|
| 102 |
+
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 103 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 104 |
+
(relu2): ReLU(inplace=True)
|
| 105 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 106 |
+
)
|
| 107 |
+
(denselayer6): _DenseLayer(
|
| 108 |
+
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 109 |
+
(relu1): ReLU(inplace=True)
|
| 110 |
+
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 111 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 112 |
+
(relu2): ReLU(inplace=True)
|
| 113 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 114 |
+
)
|
| 115 |
+
(denselayer7): _DenseLayer(
|
| 116 |
+
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 117 |
+
(relu1): ReLU(inplace=True)
|
| 118 |
+
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 119 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 120 |
+
(relu2): ReLU(inplace=True)
|
| 121 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 122 |
+
)
|
| 123 |
+
(denselayer8): _DenseLayer(
|
| 124 |
+
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 125 |
+
(relu1): ReLU(inplace=True)
|
| 126 |
+
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 127 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 128 |
+
(relu2): ReLU(inplace=True)
|
| 129 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 130 |
+
)
|
| 131 |
+
(denselayer9): _DenseLayer(
|
| 132 |
+
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 133 |
+
(relu1): ReLU(inplace=True)
|
| 134 |
+
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 135 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 136 |
+
(relu2): ReLU(inplace=True)
|
| 137 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 138 |
+
)
|
| 139 |
+
(denselayer10): _DenseLayer(
|
| 140 |
+
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 141 |
+
(relu1): ReLU(inplace=True)
|
| 142 |
+
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 143 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 144 |
+
(relu2): ReLU(inplace=True)
|
| 145 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 146 |
+
)
|
| 147 |
+
(denselayer11): _DenseLayer(
|
| 148 |
+
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 149 |
+
(relu1): ReLU(inplace=True)
|
| 150 |
+
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 151 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 152 |
+
(relu2): ReLU(inplace=True)
|
| 153 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 154 |
+
)
|
| 155 |
+
(denselayer12): _DenseLayer(
|
| 156 |
+
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 157 |
+
(relu1): ReLU(inplace=True)
|
| 158 |
+
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 159 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 160 |
+
(relu2): ReLU(inplace=True)
|
| 161 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 162 |
+
)
|
| 163 |
+
)
|
| 164 |
+
(transition2): _Transition(
|
| 165 |
+
(norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 166 |
+
(relu): ReLU(inplace=True)
|
| 167 |
+
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 168 |
+
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
|
| 169 |
+
)
|
| 170 |
+
(denseblock3): _DenseBlock(
|
| 171 |
+
(denselayer1): _DenseLayer(
|
| 172 |
+
(norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 173 |
+
(relu1): ReLU(inplace=True)
|
| 174 |
+
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 175 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 176 |
+
(relu2): ReLU(inplace=True)
|
| 177 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 178 |
+
)
|
| 179 |
+
(denselayer2): _DenseLayer(
|
| 180 |
+
(norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 181 |
+
(relu1): ReLU(inplace=True)
|
| 182 |
+
(conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 183 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 184 |
+
(relu2): ReLU(inplace=True)
|
| 185 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 186 |
+
)
|
| 187 |
+
(denselayer3): _DenseLayer(
|
| 188 |
+
(norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 189 |
+
(relu1): ReLU(inplace=True)
|
| 190 |
+
(conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 191 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 192 |
+
(relu2): ReLU(inplace=True)
|
| 193 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 194 |
+
)
|
| 195 |
+
(denselayer4): _DenseLayer(
|
| 196 |
+
(norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 197 |
+
(relu1): ReLU(inplace=True)
|
| 198 |
+
(conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 199 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 200 |
+
(relu2): ReLU(inplace=True)
|
| 201 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 202 |
+
)
|
| 203 |
+
(denselayer5): _DenseLayer(
|
| 204 |
+
(norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 205 |
+
(relu1): ReLU(inplace=True)
|
| 206 |
+
(conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 207 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 208 |
+
(relu2): ReLU(inplace=True)
|
| 209 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 210 |
+
)
|
| 211 |
+
(denselayer6): _DenseLayer(
|
| 212 |
+
(norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 213 |
+
(relu1): ReLU(inplace=True)
|
| 214 |
+
(conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 215 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 216 |
+
(relu2): ReLU(inplace=True)
|
| 217 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 218 |
+
)
|
| 219 |
+
(denselayer7): _DenseLayer(
|
| 220 |
+
(norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 221 |
+
(relu1): ReLU(inplace=True)
|
| 222 |
+
(conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 223 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 224 |
+
(relu2): ReLU(inplace=True)
|
| 225 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 226 |
+
)
|
| 227 |
+
(denselayer8): _DenseLayer(
|
| 228 |
+
(norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 229 |
+
(relu1): ReLU(inplace=True)
|
| 230 |
+
(conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 231 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 232 |
+
(relu2): ReLU(inplace=True)
|
| 233 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 234 |
+
)
|
| 235 |
+
(denselayer9): _DenseLayer(
|
| 236 |
+
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 237 |
+
(relu1): ReLU(inplace=True)
|
| 238 |
+
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 239 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 240 |
+
(relu2): ReLU(inplace=True)
|
| 241 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 242 |
+
)
|
| 243 |
+
(denselayer10): _DenseLayer(
|
| 244 |
+
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 245 |
+
(relu1): ReLU(inplace=True)
|
| 246 |
+
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 247 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 248 |
+
(relu2): ReLU(inplace=True)
|
| 249 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 250 |
+
)
|
| 251 |
+
(denselayer11): _DenseLayer(
|
| 252 |
+
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 253 |
+
(relu1): ReLU(inplace=True)
|
| 254 |
+
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 255 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 256 |
+
(relu2): ReLU(inplace=True)
|
| 257 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 258 |
+
)
|
| 259 |
+
(denselayer12): _DenseLayer(
|
| 260 |
+
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 261 |
+
(relu1): ReLU(inplace=True)
|
| 262 |
+
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 263 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 264 |
+
(relu2): ReLU(inplace=True)
|
| 265 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 266 |
+
)
|
| 267 |
+
(denselayer13): _DenseLayer(
|
| 268 |
+
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 269 |
+
(relu1): ReLU(inplace=True)
|
| 270 |
+
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 271 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 272 |
+
(relu2): ReLU(inplace=True)
|
| 273 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 274 |
+
)
|
| 275 |
+
(denselayer14): _DenseLayer(
|
| 276 |
+
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 277 |
+
(relu1): ReLU(inplace=True)
|
| 278 |
+
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 279 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 280 |
+
(relu2): ReLU(inplace=True)
|
| 281 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 282 |
+
)
|
| 283 |
+
(denselayer15): _DenseLayer(
|
| 284 |
+
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 285 |
+
(relu1): ReLU(inplace=True)
|
| 286 |
+
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 287 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 288 |
+
(relu2): ReLU(inplace=True)
|
| 289 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 290 |
+
)
|
| 291 |
+
(denselayer16): _DenseLayer(
|
| 292 |
+
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 293 |
+
(relu1): ReLU(inplace=True)
|
| 294 |
+
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 295 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 296 |
+
(relu2): ReLU(inplace=True)
|
| 297 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 298 |
+
)
|
| 299 |
+
(denselayer17): _DenseLayer(
|
| 300 |
+
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 301 |
+
(relu1): ReLU(inplace=True)
|
| 302 |
+
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 303 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 304 |
+
(relu2): ReLU(inplace=True)
|
| 305 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 306 |
+
)
|
| 307 |
+
(denselayer18): _DenseLayer(
|
| 308 |
+
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 309 |
+
(relu1): ReLU(inplace=True)
|
| 310 |
+
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 311 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 312 |
+
(relu2): ReLU(inplace=True)
|
| 313 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 314 |
+
)
|
| 315 |
+
(denselayer19): _DenseLayer(
|
| 316 |
+
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 317 |
+
(relu1): ReLU(inplace=True)
|
| 318 |
+
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 319 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 320 |
+
(relu2): ReLU(inplace=True)
|
| 321 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 322 |
+
)
|
| 323 |
+
(denselayer20): _DenseLayer(
|
| 324 |
+
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 325 |
+
(relu1): ReLU(inplace=True)
|
| 326 |
+
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 327 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 328 |
+
(relu2): ReLU(inplace=True)
|
| 329 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 330 |
+
)
|
| 331 |
+
(denselayer21): _DenseLayer(
|
| 332 |
+
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 333 |
+
(relu1): ReLU(inplace=True)
|
| 334 |
+
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 335 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 336 |
+
(relu2): ReLU(inplace=True)
|
| 337 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 338 |
+
)
|
| 339 |
+
(denselayer22): _DenseLayer(
|
| 340 |
+
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 341 |
+
(relu1): ReLU(inplace=True)
|
| 342 |
+
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 343 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 344 |
+
(relu2): ReLU(inplace=True)
|
| 345 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 346 |
+
)
|
| 347 |
+
(denselayer23): _DenseLayer(
|
| 348 |
+
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 349 |
+
(relu1): ReLU(inplace=True)
|
| 350 |
+
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 351 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 352 |
+
(relu2): ReLU(inplace=True)
|
| 353 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 354 |
+
)
|
| 355 |
+
(denselayer24): _DenseLayer(
|
| 356 |
+
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 357 |
+
(relu1): ReLU(inplace=True)
|
| 358 |
+
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 359 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 360 |
+
(relu2): ReLU(inplace=True)
|
| 361 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 362 |
+
)
|
| 363 |
+
)
|
| 364 |
+
(transition3): _Transition(
|
| 365 |
+
(norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 366 |
+
(relu): ReLU(inplace=True)
|
| 367 |
+
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 368 |
+
(pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
|
| 369 |
+
)
|
| 370 |
+
(denseblock4): _DenseBlock(
|
| 371 |
+
(denselayer1): _DenseLayer(
|
| 372 |
+
(norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 373 |
+
(relu1): ReLU(inplace=True)
|
| 374 |
+
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 375 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 376 |
+
(relu2): ReLU(inplace=True)
|
| 377 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 378 |
+
)
|
| 379 |
+
(denselayer2): _DenseLayer(
|
| 380 |
+
(norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 381 |
+
(relu1): ReLU(inplace=True)
|
| 382 |
+
(conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 383 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 384 |
+
(relu2): ReLU(inplace=True)
|
| 385 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 386 |
+
)
|
| 387 |
+
(denselayer3): _DenseLayer(
|
| 388 |
+
(norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 389 |
+
(relu1): ReLU(inplace=True)
|
| 390 |
+
(conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 391 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 392 |
+
(relu2): ReLU(inplace=True)
|
| 393 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 394 |
+
)
|
| 395 |
+
(denselayer4): _DenseLayer(
|
| 396 |
+
(norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 397 |
+
(relu1): ReLU(inplace=True)
|
| 398 |
+
(conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 399 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 400 |
+
(relu2): ReLU(inplace=True)
|
| 401 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 402 |
+
)
|
| 403 |
+
(denselayer5): _DenseLayer(
|
| 404 |
+
(norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 405 |
+
(relu1): ReLU(inplace=True)
|
| 406 |
+
(conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 407 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 408 |
+
(relu2): ReLU(inplace=True)
|
| 409 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 410 |
+
)
|
| 411 |
+
(denselayer6): _DenseLayer(
|
| 412 |
+
(norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 413 |
+
(relu1): ReLU(inplace=True)
|
| 414 |
+
(conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 415 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 416 |
+
(relu2): ReLU(inplace=True)
|
| 417 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 418 |
+
)
|
| 419 |
+
(denselayer7): _DenseLayer(
|
| 420 |
+
(norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 421 |
+
(relu1): ReLU(inplace=True)
|
| 422 |
+
(conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 423 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 424 |
+
(relu2): ReLU(inplace=True)
|
| 425 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 426 |
+
)
|
| 427 |
+
(denselayer8): _DenseLayer(
|
| 428 |
+
(norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 429 |
+
(relu1): ReLU(inplace=True)
|
| 430 |
+
(conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 431 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 432 |
+
(relu2): ReLU(inplace=True)
|
| 433 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 434 |
+
)
|
| 435 |
+
(denselayer9): _DenseLayer(
|
| 436 |
+
(norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 437 |
+
(relu1): ReLU(inplace=True)
|
| 438 |
+
(conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 439 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 440 |
+
(relu2): ReLU(inplace=True)
|
| 441 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 442 |
+
)
|
| 443 |
+
(denselayer10): _DenseLayer(
|
| 444 |
+
(norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 445 |
+
(relu1): ReLU(inplace=True)
|
| 446 |
+
(conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 447 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 448 |
+
(relu2): ReLU(inplace=True)
|
| 449 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 450 |
+
)
|
| 451 |
+
(denselayer11): _DenseLayer(
|
| 452 |
+
(norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 453 |
+
(relu1): ReLU(inplace=True)
|
| 454 |
+
(conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 455 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 456 |
+
(relu2): ReLU(inplace=True)
|
| 457 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 458 |
+
)
|
| 459 |
+
(denselayer12): _DenseLayer(
|
| 460 |
+
(norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 461 |
+
(relu1): ReLU(inplace=True)
|
| 462 |
+
(conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 463 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 464 |
+
(relu2): ReLU(inplace=True)
|
| 465 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 466 |
+
)
|
| 467 |
+
(denselayer13): _DenseLayer(
|
| 468 |
+
(norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 469 |
+
(relu1): ReLU(inplace=True)
|
| 470 |
+
(conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 471 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 472 |
+
(relu2): ReLU(inplace=True)
|
| 473 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 474 |
+
)
|
| 475 |
+
(denselayer14): _DenseLayer(
|
| 476 |
+
(norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 477 |
+
(relu1): ReLU(inplace=True)
|
| 478 |
+
(conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 479 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 480 |
+
(relu2): ReLU(inplace=True)
|
| 481 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 482 |
+
)
|
| 483 |
+
(denselayer15): _DenseLayer(
|
| 484 |
+
(norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 485 |
+
(relu1): ReLU(inplace=True)
|
| 486 |
+
(conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 487 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 488 |
+
(relu2): ReLU(inplace=True)
|
| 489 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 490 |
+
)
|
| 491 |
+
(denselayer16): _DenseLayer(
|
| 492 |
+
(norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 493 |
+
(relu1): ReLU(inplace=True)
|
| 494 |
+
(conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 495 |
+
(norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 496 |
+
(relu2): ReLU(inplace=True)
|
| 497 |
+
(conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
| 498 |
+
)
|
| 499 |
+
)
|
| 500 |
+
(norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 501 |
+
)
|
| 502 |
+
(classifier): Linear(in_features=1024, out_features=7, bias=True)
|
| 503 |
+
)
|
| 504 |
+
==================================================
|
| 505 |
+
|
| 506 |
+
[Epoch 0], [Batch 0 / 40], [Loss 2.126059055328369]
|
| 507 |
+
precision recall f1-score support
|
| 508 |
+
|
| 509 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 510 |
+
bcc 1.0000 0.1000 0.1818 10
|
| 511 |
+
bkl 0.0000 0.0000 0.0000 10
|
| 512 |
+
df 0.1765 0.2500 0.2069 12
|
| 513 |
+
mel 0.0000 0.0000 0.0000 9
|
| 514 |
+
nv 0.2708 0.8125 0.4062 16
|
| 515 |
+
vasc 0.4286 0.6667 0.5217 9
|
| 516 |
+
|
| 517 |
+
accuracy 0.2840 81
|
| 518 |
+
macro avg 0.2680 0.2613 0.1881 81
|
| 519 |
+
weighted avg 0.2507 0.2840 0.1913 81
|
| 520 |
+
|
| 521 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 522 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 28.40%
|
| 523 |
+
[Epoch 1], [Batch 0 / 40], [Loss 0.7534104585647583]
|
| 524 |
+
precision recall f1-score support
|
| 525 |
+
|
| 526 |
+
akiec 0.7500 0.2000 0.3158 15
|
| 527 |
+
bcc 0.2143 0.6000 0.3158 10
|
| 528 |
+
bkl 0.1667 0.3000 0.2143 10
|
| 529 |
+
df 0.0000 0.0000 0.0000 12
|
| 530 |
+
mel 0.0000 0.0000 0.0000 9
|
| 531 |
+
nv 0.5238 0.6875 0.5946 16
|
| 532 |
+
vasc 0.7143 0.5556 0.6250 9
|
| 533 |
+
|
| 534 |
+
accuracy 0.3457 81
|
| 535 |
+
macro avg 0.3384 0.3347 0.2951 81
|
| 536 |
+
weighted avg 0.3688 0.3457 0.3108 81
|
| 537 |
+
|
| 538 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 539 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 34.57%
|
| 540 |
+
[Epoch 2], [Batch 0 / 40], [Loss 0.654026448726654]
|
| 541 |
+
precision recall f1-score support
|
| 542 |
+
|
| 543 |
+
akiec 0.5417 0.8667 0.6667 15
|
| 544 |
+
bcc 0.8000 0.4000 0.5333 10
|
| 545 |
+
bkl 0.3333 0.2000 0.2500 10
|
| 546 |
+
df 0.7500 0.2500 0.3750 12
|
| 547 |
+
mel 1.0000 0.5556 0.7143 9
|
| 548 |
+
nv 0.7857 0.6875 0.7333 16
|
| 549 |
+
vasc 0.3913 1.0000 0.5625 9
|
| 550 |
+
|
| 551 |
+
accuracy 0.5802 81
|
| 552 |
+
macro avg 0.6574 0.5657 0.5479 81
|
| 553 |
+
weighted avg 0.6611 0.5802 0.5624 81
|
| 554 |
+
|
| 555 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 556 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 58.02%
|
| 557 |
+
[Epoch 3], [Batch 0 / 40], [Loss 0.7007324695587158]
|
| 558 |
+
precision recall f1-score support
|
| 559 |
+
|
| 560 |
+
akiec 0.7778 0.4667 0.5833 15
|
| 561 |
+
bcc 0.5000 0.4000 0.4444 10
|
| 562 |
+
bkl 0.1667 0.2000 0.1818 10
|
| 563 |
+
df 1.0000 0.2500 0.4000 12
|
| 564 |
+
mel 0.5714 0.4444 0.5000 9
|
| 565 |
+
nv 0.6923 0.5625 0.6207 16
|
| 566 |
+
vasc 0.3103 1.0000 0.4737 9
|
| 567 |
+
|
| 568 |
+
accuracy 0.4691 81
|
| 569 |
+
macro avg 0.5741 0.4748 0.4577 81
|
| 570 |
+
weighted avg 0.6092 0.4691 0.4754 81
|
| 571 |
+
|
| 572 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 573 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 58.02%
|
| 574 |
+
[Epoch 4], [Batch 0 / 40], [Loss 0.32403188943862915]
|
| 575 |
+
precision recall f1-score support
|
| 576 |
+
|
| 577 |
+
akiec 0.5714 0.2667 0.3636 15
|
| 578 |
+
bcc 1.0000 0.4000 0.5714 10
|
| 579 |
+
bkl 0.2593 0.7000 0.3784 10
|
| 580 |
+
df 0.7500 0.2500 0.3750 12
|
| 581 |
+
mel 0.3529 0.6667 0.4615 9
|
| 582 |
+
nv 0.8333 0.6250 0.7143 16
|
| 583 |
+
vasc 0.8000 0.8889 0.8421 9
|
| 584 |
+
|
| 585 |
+
accuracy 0.5185 81
|
| 586 |
+
macro avg 0.6524 0.5425 0.5295 81
|
| 587 |
+
weighted avg 0.6651 0.5185 0.5261 81
|
| 588 |
+
|
| 589 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 590 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 58.02%
|
| 591 |
+
[Epoch 5], [Batch 0 / 40], [Loss 0.4335012137889862]
|
| 592 |
+
precision recall f1-score support
|
| 593 |
+
|
| 594 |
+
akiec 0.4444 0.5333 0.4848 15
|
| 595 |
+
bcc 0.1875 0.3000 0.2308 10
|
| 596 |
+
bkl 0.0000 0.0000 0.0000 10
|
| 597 |
+
df 1.0000 0.2500 0.4000 12
|
| 598 |
+
mel 0.6000 0.6667 0.6316 9
|
| 599 |
+
nv 0.6316 0.7500 0.6857 16
|
| 600 |
+
vasc 0.6923 1.0000 0.8182 9
|
| 601 |
+
|
| 602 |
+
accuracy 0.5062 81
|
| 603 |
+
macro avg 0.5080 0.5000 0.4644 81
|
| 604 |
+
weighted avg 0.5219 0.5062 0.4741 81
|
| 605 |
+
|
| 606 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 607 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 58.02%
|
| 608 |
+
[Epoch 6], [Batch 0 / 40], [Loss 0.15954938530921936]
|
| 609 |
+
precision recall f1-score support
|
| 610 |
+
|
| 611 |
+
akiec 0.3333 0.0667 0.1111 15
|
| 612 |
+
bcc 0.6667 0.6000 0.6316 10
|
| 613 |
+
bkl 0.3333 0.8000 0.4706 10
|
| 614 |
+
df 0.8333 0.4167 0.5556 12
|
| 615 |
+
mel 0.4167 0.5556 0.4762 9
|
| 616 |
+
nv 0.7059 0.7500 0.7273 16
|
| 617 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 618 |
+
|
| 619 |
+
accuracy 0.5679 81
|
| 620 |
+
macro avg 0.5985 0.5984 0.5600 81
|
| 621 |
+
weighted avg 0.5944 0.5679 0.5408 81
|
| 622 |
+
|
| 623 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 624 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 58.02%
|
| 625 |
+
[Epoch 7], [Batch 0 / 40], [Loss 0.09287992864847183]
|
| 626 |
+
precision recall f1-score support
|
| 627 |
+
|
| 628 |
+
akiec 0.4815 0.8667 0.6190 15
|
| 629 |
+
bcc 1.0000 0.6000 0.7500 10
|
| 630 |
+
bkl 0.5000 0.1000 0.1667 10
|
| 631 |
+
df 0.7273 0.6667 0.6957 12
|
| 632 |
+
mel 0.7500 0.6667 0.7059 9
|
| 633 |
+
nv 0.7500 0.9375 0.8333 16
|
| 634 |
+
vasc 1.0000 0.7778 0.8750 9
|
| 635 |
+
|
| 636 |
+
accuracy 0.6914 81
|
| 637 |
+
macro avg 0.7441 0.6593 0.6637 81
|
| 638 |
+
weighted avg 0.7247 0.6914 0.6711 81
|
| 639 |
+
|
| 640 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 641 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 642 |
+
[Epoch 8], [Batch 0 / 40], [Loss 0.16124042868614197]
|
| 643 |
+
precision recall f1-score support
|
| 644 |
+
|
| 645 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 646 |
+
bcc 0.5000 0.4000 0.4444 10
|
| 647 |
+
bkl 0.2727 0.3000 0.2857 10
|
| 648 |
+
df 0.5714 0.6667 0.6154 12
|
| 649 |
+
mel 0.2667 0.4444 0.3333 9
|
| 650 |
+
nv 0.6667 0.8750 0.7568 16
|
| 651 |
+
vasc 0.7500 1.0000 0.8571 9
|
| 652 |
+
|
| 653 |
+
accuracy 0.5185 81
|
| 654 |
+
macro avg 0.4325 0.5266 0.4704 81
|
| 655 |
+
weighted avg 0.4247 0.5185 0.4631 81
|
| 656 |
+
|
| 657 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 658 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 659 |
+
[Epoch 9], [Batch 0 / 40], [Loss 0.11325355619192123]
|
| 660 |
+
precision recall f1-score support
|
| 661 |
+
|
| 662 |
+
akiec 0.7500 0.2000 0.3158 15
|
| 663 |
+
bcc 0.2941 0.5000 0.3704 10
|
| 664 |
+
bkl 0.1622 0.6000 0.2553 10
|
| 665 |
+
df 1.0000 0.1667 0.2857 12
|
| 666 |
+
mel 1.0000 0.2222 0.3636 9
|
| 667 |
+
nv 0.8333 0.6250 0.7143 16
|
| 668 |
+
vasc 0.8571 0.6667 0.7500 9
|
| 669 |
+
|
| 670 |
+
accuracy 0.4198 81
|
| 671 |
+
macro avg 0.6995 0.4258 0.4364 81
|
| 672 |
+
weighted avg 0.7143 0.4198 0.4429 81
|
| 673 |
+
|
| 674 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 675 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 676 |
+
[Epoch 10], [Batch 0 / 40], [Loss 0.17134839296340942]
|
| 677 |
+
precision recall f1-score support
|
| 678 |
+
|
| 679 |
+
akiec 1.0000 0.1333 0.2353 15
|
| 680 |
+
bcc 0.4762 1.0000 0.6452 10
|
| 681 |
+
bkl 0.5714 0.4000 0.4706 10
|
| 682 |
+
df 0.7143 0.8333 0.7692 12
|
| 683 |
+
mel 0.6000 0.3333 0.4286 9
|
| 684 |
+
nv 0.6364 0.8750 0.7368 16
|
| 685 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 686 |
+
|
| 687 |
+
accuracy 0.6420 81
|
| 688 |
+
macro avg 0.6998 0.6536 0.6047 81
|
| 689 |
+
weighted avg 0.7127 0.6420 0.5937 81
|
| 690 |
+
|
| 691 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 692 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 693 |
+
[Epoch 11], [Batch 0 / 40], [Loss 0.058563705533742905]
|
| 694 |
+
precision recall f1-score support
|
| 695 |
+
|
| 696 |
+
akiec 0.4737 0.6000 0.5294 15
|
| 697 |
+
bcc 0.5000 0.6000 0.5455 10
|
| 698 |
+
bkl 0.2632 0.5000 0.3448 10
|
| 699 |
+
df 1.0000 0.5000 0.6667 12
|
| 700 |
+
mel 0.6667 0.2222 0.3333 9
|
| 701 |
+
nv 0.8333 0.6250 0.7143 16
|
| 702 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 703 |
+
|
| 704 |
+
accuracy 0.5802 81
|
| 705 |
+
macro avg 0.6624 0.5782 0.5830 81
|
| 706 |
+
weighted avg 0.6688 0.5802 0.5901 81
|
| 707 |
+
|
| 708 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 709 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 710 |
+
[Epoch 12], [Batch 0 / 40], [Loss 0.034065358340740204]
|
| 711 |
+
precision recall f1-score support
|
| 712 |
+
|
| 713 |
+
akiec 0.7000 0.4667 0.5600 15
|
| 714 |
+
bcc 0.4348 1.0000 0.6061 10
|
| 715 |
+
bkl 0.4286 0.6000 0.5000 10
|
| 716 |
+
df 0.8333 0.4167 0.5556 12
|
| 717 |
+
mel 0.8000 0.4444 0.5714 9
|
| 718 |
+
nv 0.8462 0.6875 0.7586 16
|
| 719 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 720 |
+
|
| 721 |
+
accuracy 0.6420 81
|
| 722 |
+
macro avg 0.7061 0.6593 0.6427 81
|
| 723 |
+
weighted avg 0.7157 0.6420 0.6412 81
|
| 724 |
+
|
| 725 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 726 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 727 |
+
[Epoch 13], [Batch 0 / 40], [Loss 0.04914743825793266]
|
| 728 |
+
precision recall f1-score support
|
| 729 |
+
|
| 730 |
+
akiec 0.6667 0.2667 0.3810 15
|
| 731 |
+
bcc 0.6667 0.6000 0.6316 10
|
| 732 |
+
bkl 0.3077 0.4000 0.3478 10
|
| 733 |
+
df 0.8889 0.6667 0.7619 12
|
| 734 |
+
mel 0.3000 0.6667 0.4138 9
|
| 735 |
+
nv 1.0000 0.8125 0.8966 16
|
| 736 |
+
vasc 0.8182 1.0000 0.9000 9
|
| 737 |
+
|
| 738 |
+
accuracy 0.6173 81
|
| 739 |
+
macro avg 0.6640 0.6304 0.6189 81
|
| 740 |
+
weighted avg 0.6972 0.6173 0.6274 81
|
| 741 |
+
|
| 742 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 743 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 744 |
+
[Epoch 14], [Batch 0 / 40], [Loss 0.023914232850074768]
|
| 745 |
+
precision recall f1-score support
|
| 746 |
+
|
| 747 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 748 |
+
bcc 0.3000 0.3000 0.3000 10
|
| 749 |
+
bkl 0.1795 0.7000 0.2857 10
|
| 750 |
+
df 0.0000 0.0000 0.0000 12
|
| 751 |
+
mel 0.5000 0.5556 0.5263 9
|
| 752 |
+
nv 0.8000 0.5000 0.6154 16
|
| 753 |
+
vasc 0.7500 1.0000 0.8571 9
|
| 754 |
+
|
| 755 |
+
accuracy 0.3951 81
|
| 756 |
+
macro avg 0.3614 0.4365 0.3692 81
|
| 757 |
+
weighted avg 0.3561 0.3951 0.3476 81
|
| 758 |
+
|
| 759 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 760 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 761 |
+
[Epoch 15], [Batch 0 / 40], [Loss 0.03596542775630951]
|
| 762 |
+
precision recall f1-score support
|
| 763 |
+
|
| 764 |
+
akiec 0.5385 0.4667 0.5000 15
|
| 765 |
+
bcc 0.6667 0.8000 0.7273 10
|
| 766 |
+
bkl 0.7500 0.3000 0.4286 10
|
| 767 |
+
df 0.5000 0.7500 0.6000 12
|
| 768 |
+
mel 0.6000 0.3333 0.4286 9
|
| 769 |
+
nv 0.7059 0.7500 0.7273 16
|
| 770 |
+
vasc 0.7500 1.0000 0.8571 9
|
| 771 |
+
|
| 772 |
+
accuracy 0.6296 81
|
| 773 |
+
macro avg 0.6444 0.6286 0.6098 81
|
| 774 |
+
weighted avg 0.6381 0.6296 0.6107 81
|
| 775 |
+
|
| 776 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 777 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 778 |
+
[Epoch 16], [Batch 0 / 40], [Loss 0.049507200717926025]
|
| 779 |
+
precision recall f1-score support
|
| 780 |
+
|
| 781 |
+
akiec 0.6667 0.5333 0.5926 15
|
| 782 |
+
bcc 0.5333 0.8000 0.6400 10
|
| 783 |
+
bkl 0.4545 0.5000 0.4762 10
|
| 784 |
+
df 0.6923 0.7500 0.7200 12
|
| 785 |
+
mel 0.6667 0.4444 0.5333 9
|
| 786 |
+
nv 0.9286 0.8125 0.8667 16
|
| 787 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 788 |
+
|
| 789 |
+
accuracy 0.6914 81
|
| 790 |
+
macro avg 0.6917 0.6915 0.6823 81
|
| 791 |
+
weighted avg 0.7055 0.6914 0.6899 81
|
| 792 |
+
|
| 793 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 794 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 795 |
+
[Epoch 17], [Batch 0 / 40], [Loss 0.009000462479889393]
|
| 796 |
+
precision recall f1-score support
|
| 797 |
+
|
| 798 |
+
akiec 0.7778 0.4667 0.5833 15
|
| 799 |
+
bcc 0.6364 0.7000 0.6667 10
|
| 800 |
+
bkl 0.4000 0.4000 0.4000 10
|
| 801 |
+
df 0.8000 0.6667 0.7273 12
|
| 802 |
+
mel 0.3000 0.6667 0.4138 9
|
| 803 |
+
nv 0.9167 0.6875 0.7857 16
|
| 804 |
+
vasc 0.8889 0.8889 0.8889 9
|
| 805 |
+
|
| 806 |
+
accuracy 0.6296 81
|
| 807 |
+
macro avg 0.6742 0.6395 0.6380 81
|
| 808 |
+
weighted avg 0.7037 0.6296 0.6474 81
|
| 809 |
+
|
| 810 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 811 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 69.14%
|
| 812 |
+
[Epoch 18], [Batch 0 / 40], [Loss 0.014917709864675999]
|
| 813 |
+
precision recall f1-score support
|
| 814 |
+
|
| 815 |
+
akiec 0.8889 0.5333 0.6667 15
|
| 816 |
+
bcc 0.6429 0.9000 0.7500 10
|
| 817 |
+
bkl 0.5455 0.6000 0.5714 10
|
| 818 |
+
df 0.9000 0.7500 0.8182 12
|
| 819 |
+
mel 0.5000 0.6667 0.5714 9
|
| 820 |
+
nv 0.8667 0.8125 0.8387 16
|
| 821 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 822 |
+
|
| 823 |
+
accuracy 0.7407 81
|
| 824 |
+
macro avg 0.7491 0.7518 0.7377 81
|
| 825 |
+
weighted avg 0.7714 0.7407 0.7422 81
|
| 826 |
+
|
| 827 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 828 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 74.07%
|
| 829 |
+
[Epoch 19], [Batch 0 / 40], [Loss 0.012562758289277554]
|
| 830 |
+
precision recall f1-score support
|
| 831 |
+
|
| 832 |
+
akiec 0.8462 0.7333 0.7857 15
|
| 833 |
+
bcc 0.6667 0.8000 0.7273 10
|
| 834 |
+
bkl 0.6000 0.6000 0.6000 10
|
| 835 |
+
df 0.8000 0.6667 0.7273 12
|
| 836 |
+
mel 0.3571 0.5556 0.4348 9
|
| 837 |
+
nv 0.9167 0.6875 0.7857 16
|
| 838 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 839 |
+
|
| 840 |
+
accuracy 0.7160 81
|
| 841 |
+
macro avg 0.7267 0.7204 0.7154 81
|
| 842 |
+
weighted avg 0.7523 0.7160 0.7259 81
|
| 843 |
+
|
| 844 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 845 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 74.07%
|
| 846 |
+
[Epoch 20], [Batch 0 / 40], [Loss 0.014114274643361568]
|
| 847 |
+
precision recall f1-score support
|
| 848 |
+
|
| 849 |
+
akiec 0.7778 0.4667 0.5833 15
|
| 850 |
+
bcc 0.8571 0.6000 0.7059 10
|
| 851 |
+
bkl 0.5000 0.5000 0.5000 10
|
| 852 |
+
df 0.6429 0.7500 0.6923 12
|
| 853 |
+
mel 0.4286 0.6667 0.5217 9
|
| 854 |
+
nv 0.7647 0.8125 0.7879 16
|
| 855 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 856 |
+
|
| 857 |
+
accuracy 0.6790 81
|
| 858 |
+
macro avg 0.6959 0.6851 0.6769 81
|
| 859 |
+
weighted avg 0.7055 0.6790 0.6783 81
|
| 860 |
+
|
| 861 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 862 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 74.07%
|
| 863 |
+
[Epoch 21], [Batch 0 / 40], [Loss 0.012415341101586819]
|
| 864 |
+
precision recall f1-score support
|
| 865 |
+
|
| 866 |
+
akiec 0.8889 0.5333 0.6667 15
|
| 867 |
+
bcc 0.8000 0.8000 0.8000 10
|
| 868 |
+
bkl 0.5000 0.5000 0.5000 10
|
| 869 |
+
df 0.8333 0.8333 0.8333 12
|
| 870 |
+
mel 0.3571 0.5556 0.4348 9
|
| 871 |
+
nv 0.8750 0.8750 0.8750 16
|
| 872 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 873 |
+
|
| 874 |
+
accuracy 0.7284 81
|
| 875 |
+
macro avg 0.7363 0.7282 0.7225 81
|
| 876 |
+
weighted avg 0.7611 0.7284 0.7338 81
|
| 877 |
+
|
| 878 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 879 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 74.07%
|
| 880 |
+
[Epoch 22], [Batch 0 / 40], [Loss 0.005975313950330019]
|
| 881 |
+
precision recall f1-score support
|
| 882 |
+
|
| 883 |
+
akiec 0.9167 0.7333 0.8148 15
|
| 884 |
+
bcc 0.8000 0.8000 0.8000 10
|
| 885 |
+
bkl 0.5000 0.7000 0.5833 10
|
| 886 |
+
df 0.9167 0.9167 0.9167 12
|
| 887 |
+
mel 0.4545 0.5556 0.5000 9
|
| 888 |
+
nv 1.0000 0.7500 0.8571 16
|
| 889 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 890 |
+
|
| 891 |
+
accuracy 0.7778 81
|
| 892 |
+
macro avg 0.7840 0.7794 0.7742 81
|
| 893 |
+
weighted avg 0.8141 0.7778 0.7876 81
|
| 894 |
+
|
| 895 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m
|
| 896 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 897 |
+
[Epoch 23], [Batch 0 / 40], [Loss 0.004292001016438007]
|
| 898 |
+
precision recall f1-score support
|
| 899 |
+
|
| 900 |
+
akiec 0.8182 0.6000 0.6923 15
|
| 901 |
+
bcc 0.7273 0.8000 0.7619 10
|
| 902 |
+
bkl 0.5714 0.8000 0.6667 10
|
| 903 |
+
df 0.7500 0.7500 0.7500 12
|
| 904 |
+
mel 0.6667 0.6667 0.6667 9
|
| 905 |
+
nv 0.8571 0.7500 0.8000 16
|
| 906 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 907 |
+
|
| 908 |
+
accuracy 0.7531 81
|
| 909 |
+
macro avg 0.7558 0.7667 0.7550 81
|
| 910 |
+
weighted avg 0.7663 0.7531 0.7530 81
|
| 911 |
+
|
| 912 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 913 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 914 |
+
[Epoch 24], [Batch 0 / 40], [Loss 0.008238147012889385]
|
| 915 |
+
precision recall f1-score support
|
| 916 |
+
|
| 917 |
+
akiec 0.6667 0.5333 0.5926 15
|
| 918 |
+
bcc 0.5625 0.9000 0.6923 10
|
| 919 |
+
bkl 0.6667 0.6000 0.6316 10
|
| 920 |
+
df 0.8182 0.7500 0.7826 12
|
| 921 |
+
mel 0.8571 0.6667 0.7500 9
|
| 922 |
+
nv 0.8125 0.8125 0.8125 16
|
| 923 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 924 |
+
|
| 925 |
+
accuracy 0.7407 81
|
| 926 |
+
macro avg 0.7548 0.7518 0.7441 81
|
| 927 |
+
weighted avg 0.7521 0.7407 0.7382 81
|
| 928 |
+
|
| 929 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 930 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 931 |
+
[Epoch 25], [Batch 0 / 40], [Loss 0.003627797355875373]
|
| 932 |
+
precision recall f1-score support
|
| 933 |
+
|
| 934 |
+
akiec 0.7333 0.7333 0.7333 15
|
| 935 |
+
bcc 0.6923 0.9000 0.7826 10
|
| 936 |
+
bkl 0.5556 0.5000 0.5263 10
|
| 937 |
+
df 0.7500 0.7500 0.7500 12
|
| 938 |
+
mel 0.6250 0.5556 0.5882 9
|
| 939 |
+
nv 0.8571 0.7500 0.8000 16
|
| 940 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 941 |
+
|
| 942 |
+
accuracy 0.7407 81
|
| 943 |
+
macro avg 0.7305 0.7413 0.7326 81
|
| 944 |
+
weighted avg 0.7397 0.7407 0.7372 81
|
| 945 |
+
|
| 946 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 947 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 948 |
+
[Epoch 26], [Batch 0 / 40], [Loss 0.0014865432167425752]
|
| 949 |
+
precision recall f1-score support
|
| 950 |
+
|
| 951 |
+
akiec 0.6667 0.6667 0.6667 15
|
| 952 |
+
bcc 0.6429 0.9000 0.7500 10
|
| 953 |
+
bkl 0.5556 0.5000 0.5263 10
|
| 954 |
+
df 0.8000 0.6667 0.7273 12
|
| 955 |
+
mel 0.5000 0.5556 0.5263 9
|
| 956 |
+
nv 0.9231 0.7500 0.8276 16
|
| 957 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 958 |
+
|
| 959 |
+
accuracy 0.7160 81
|
| 960 |
+
macro avg 0.7126 0.7198 0.7102 81
|
| 961 |
+
weighted avg 0.7278 0.7160 0.7160 81
|
| 962 |
+
|
| 963 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 964 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 965 |
+
[Epoch 27], [Batch 0 / 40], [Loss 0.004643445368856192]
|
| 966 |
+
precision recall f1-score support
|
| 967 |
+
|
| 968 |
+
akiec 0.7143 0.3333 0.4545 15
|
| 969 |
+
bcc 0.6923 0.9000 0.7826 10
|
| 970 |
+
bkl 0.6667 0.6000 0.6316 10
|
| 971 |
+
df 0.6667 0.8333 0.7407 12
|
| 972 |
+
mel 0.5833 0.7778 0.6667 9
|
| 973 |
+
nv 0.8667 0.8125 0.8387 16
|
| 974 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 975 |
+
|
| 976 |
+
accuracy 0.7284 81
|
| 977 |
+
macro avg 0.7271 0.7510 0.7232 81
|
| 978 |
+
weighted avg 0.7348 0.7284 0.7135 81
|
| 979 |
+
|
| 980 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 981 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 982 |
+
[Epoch 28], [Batch 0 / 40], [Loss 0.002596722450107336]
|
| 983 |
+
precision recall f1-score support
|
| 984 |
+
|
| 985 |
+
akiec 0.7778 0.4667 0.5833 15
|
| 986 |
+
bcc 0.5333 0.8000 0.6400 10
|
| 987 |
+
bkl 0.5833 0.7000 0.6364 10
|
| 988 |
+
df 0.7273 0.6667 0.6957 12
|
| 989 |
+
mel 0.6250 0.5556 0.5882 9
|
| 990 |
+
nv 0.8750 0.8750 0.8750 16
|
| 991 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 992 |
+
|
| 993 |
+
accuracy 0.7160 81
|
| 994 |
+
macro avg 0.7174 0.7234 0.7094 81
|
| 995 |
+
weighted avg 0.7319 0.7160 0.7121 81
|
| 996 |
+
|
| 997 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 998 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 999 |
+
[Epoch 29], [Batch 0 / 40], [Loss 0.007674324791878462]
|
| 1000 |
+
precision recall f1-score support
|
| 1001 |
+
|
| 1002 |
+
akiec 0.6250 0.3333 0.4348 15
|
| 1003 |
+
bcc 0.8000 0.8000 0.8000 10
|
| 1004 |
+
bkl 0.3333 0.5000 0.4000 10
|
| 1005 |
+
df 0.6154 0.6667 0.6400 12
|
| 1006 |
+
mel 0.4000 0.6667 0.5000 9
|
| 1007 |
+
nv 0.9091 0.6250 0.7407 16
|
| 1008 |
+
vasc 0.8889 0.8889 0.8889 9
|
| 1009 |
+
|
| 1010 |
+
accuracy 0.6173 81
|
| 1011 |
+
macro avg 0.6531 0.6401 0.6292 81
|
| 1012 |
+
weighted avg 0.6696 0.6173 0.6241 81
|
| 1013 |
+
|
| 1014 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1015 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1016 |
+
[Epoch 30], [Batch 0 / 40], [Loss 0.0040101222693920135]
|
| 1017 |
+
precision recall f1-score support
|
| 1018 |
+
|
| 1019 |
+
akiec 0.7143 0.3333 0.4545 15
|
| 1020 |
+
bcc 0.8000 0.8000 0.8000 10
|
| 1021 |
+
bkl 0.5385 0.7000 0.6087 10
|
| 1022 |
+
df 0.7692 0.8333 0.8000 12
|
| 1023 |
+
mel 0.3636 0.4444 0.4000 9
|
| 1024 |
+
nv 0.8750 0.8750 0.8750 16
|
| 1025 |
+
vasc 0.8182 1.0000 0.9000 9
|
| 1026 |
+
|
| 1027 |
+
accuracy 0.7037 81
|
| 1028 |
+
macro avg 0.6970 0.7123 0.6912 81
|
| 1029 |
+
weighted avg 0.7156 0.7037 0.6939 81
|
| 1030 |
+
|
| 1031 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1032 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1033 |
+
[Epoch 31], [Batch 0 / 40], [Loss 0.018934518098831177]
|
| 1034 |
+
precision recall f1-score support
|
| 1035 |
+
|
| 1036 |
+
akiec 0.6667 0.5333 0.5926 15
|
| 1037 |
+
bcc 0.5294 0.9000 0.6667 10
|
| 1038 |
+
bkl 0.5714 0.4000 0.4706 10
|
| 1039 |
+
df 0.8182 0.7500 0.7826 12
|
| 1040 |
+
mel 0.5000 0.5556 0.5263 9
|
| 1041 |
+
nv 0.8667 0.8125 0.8387 16
|
| 1042 |
+
vasc 1.0000 1.0000 1.0000 9
|
| 1043 |
+
|
| 1044 |
+
accuracy 0.7037 81
|
| 1045 |
+
macro avg 0.7075 0.7073 0.6968 81
|
| 1046 |
+
weighted avg 0.7184 0.7037 0.7013 81
|
| 1047 |
+
|
| 1048 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1049 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1050 |
+
[Epoch 32], [Batch 0 / 40], [Loss 0.009038643911480904]
|
| 1051 |
+
precision recall f1-score support
|
| 1052 |
+
|
| 1053 |
+
akiec 0.6429 0.6000 0.6207 15
|
| 1054 |
+
bcc 0.5714 0.8000 0.6667 10
|
| 1055 |
+
bkl 0.5000 0.4000 0.4444 10
|
| 1056 |
+
df 0.8889 0.6667 0.7619 12
|
| 1057 |
+
mel 0.3846 0.5556 0.4545 9
|
| 1058 |
+
nv 0.9286 0.8125 0.8667 16
|
| 1059 |
+
vasc 1.0000 1.0000 1.0000 9
|
| 1060 |
+
|
| 1061 |
+
accuracy 0.6914 81
|
| 1062 |
+
macro avg 0.7023 0.6907 0.6878 81
|
| 1063 |
+
weighted avg 0.7203 0.6914 0.6978 81
|
| 1064 |
+
|
| 1065 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1066 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1067 |
+
[Epoch 33], [Batch 0 / 40], [Loss 0.0041113984771072865]
|
| 1068 |
+
precision recall f1-score support
|
| 1069 |
+
|
| 1070 |
+
akiec 0.7273 0.5333 0.6154 15
|
| 1071 |
+
bcc 0.5333 0.8000 0.6400 10
|
| 1072 |
+
bkl 0.6000 0.6000 0.6000 10
|
| 1073 |
+
df 0.7273 0.6667 0.6957 12
|
| 1074 |
+
mel 0.4545 0.5556 0.5000 9
|
| 1075 |
+
nv 0.9286 0.8125 0.8667 16
|
| 1076 |
+
vasc 1.0000 1.0000 1.0000 9
|
| 1077 |
+
|
| 1078 |
+
accuracy 0.7037 81
|
| 1079 |
+
macro avg 0.7101 0.7097 0.7025 81
|
| 1080 |
+
weighted avg 0.7274 0.7037 0.7080 81
|
| 1081 |
+
|
| 1082 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1083 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1084 |
+
[Epoch 34], [Batch 0 / 40], [Loss 0.0029135795775800943]
|
| 1085 |
+
precision recall f1-score support
|
| 1086 |
+
|
| 1087 |
+
akiec 0.6429 0.6000 0.6207 15
|
| 1088 |
+
bcc 0.7273 0.8000 0.7619 10
|
| 1089 |
+
bkl 0.4000 0.4000 0.4000 10
|
| 1090 |
+
df 0.8889 0.6667 0.7619 12
|
| 1091 |
+
mel 0.4167 0.5556 0.4762 9
|
| 1092 |
+
nv 0.8125 0.8125 0.8125 16
|
| 1093 |
+
vasc 1.0000 1.0000 1.0000 9
|
| 1094 |
+
|
| 1095 |
+
accuracy 0.6914 81
|
| 1096 |
+
macro avg 0.6983 0.6907 0.6905 81
|
| 1097 |
+
weighted avg 0.7078 0.6914 0.6958 81
|
| 1098 |
+
|
| 1099 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1100 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1101 |
+
[Epoch 35], [Batch 0 / 40], [Loss 0.0028080192860215902]
|
| 1102 |
+
precision recall f1-score support
|
| 1103 |
+
|
| 1104 |
+
akiec 0.7273 0.5333 0.6154 15
|
| 1105 |
+
bcc 0.6154 0.8000 0.6957 10
|
| 1106 |
+
bkl 0.5000 0.6000 0.5455 10
|
| 1107 |
+
df 0.8000 0.6667 0.7273 12
|
| 1108 |
+
mel 0.5000 0.5556 0.5263 9
|
| 1109 |
+
nv 0.8000 0.7500 0.7742 16
|
| 1110 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1111 |
+
|
| 1112 |
+
accuracy 0.6914 81
|
| 1113 |
+
macro avg 0.6918 0.7008 0.6902 81
|
| 1114 |
+
weighted avg 0.7045 0.6914 0.6916 81
|
| 1115 |
+
|
| 1116 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1117 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1118 |
+
[Epoch 36], [Batch 0 / 40], [Loss 0.005959199741482735]
|
| 1119 |
+
precision recall f1-score support
|
| 1120 |
+
|
| 1121 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 1122 |
+
bcc 0.7273 0.8000 0.7619 10
|
| 1123 |
+
bkl 0.4545 0.5000 0.4762 10
|
| 1124 |
+
df 0.8000 0.6667 0.7273 12
|
| 1125 |
+
mel 0.4167 0.5556 0.4762 9
|
| 1126 |
+
nv 0.8462 0.6875 0.7586 16
|
| 1127 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1128 |
+
|
| 1129 |
+
accuracy 0.6914 81
|
| 1130 |
+
macro avg 0.6941 0.6966 0.6910 81
|
| 1131 |
+
weighted avg 0.7101 0.6914 0.6963 81
|
| 1132 |
+
|
| 1133 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1134 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1135 |
+
[Epoch 37], [Batch 0 / 40], [Loss 0.0016287051839753985]
|
| 1136 |
+
precision recall f1-score support
|
| 1137 |
+
|
| 1138 |
+
akiec 0.7000 0.4667 0.5600 15
|
| 1139 |
+
bcc 0.6154 0.8000 0.6957 10
|
| 1140 |
+
bkl 0.5455 0.6000 0.5714 10
|
| 1141 |
+
df 0.8182 0.7500 0.7826 12
|
| 1142 |
+
mel 0.4545 0.5556 0.5000 9
|
| 1143 |
+
nv 0.8667 0.8125 0.8387 16
|
| 1144 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1145 |
+
|
| 1146 |
+
accuracy 0.7037 81
|
| 1147 |
+
macro avg 0.7000 0.7121 0.6994 81
|
| 1148 |
+
weighted avg 0.7159 0.7037 0.7026 81
|
| 1149 |
+
|
| 1150 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1151 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1152 |
+
[Epoch 38], [Batch 0 / 40], [Loss 0.0018719729268923402]
|
| 1153 |
+
precision recall f1-score support
|
| 1154 |
+
|
| 1155 |
+
akiec 0.6667 0.5333 0.5926 15
|
| 1156 |
+
bcc 0.6154 0.8000 0.6957 10
|
| 1157 |
+
bkl 0.4444 0.4000 0.4211 10
|
| 1158 |
+
df 0.7273 0.6667 0.6957 12
|
| 1159 |
+
mel 0.4167 0.5556 0.4762 9
|
| 1160 |
+
nv 0.9286 0.8125 0.8667 16
|
| 1161 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1162 |
+
|
| 1163 |
+
accuracy 0.6790 81
|
| 1164 |
+
macro avg 0.6713 0.6812 0.6707 81
|
| 1165 |
+
weighted avg 0.6918 0.6790 0.6800 81
|
| 1166 |
+
|
| 1167 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1168 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1169 |
+
[Epoch 39], [Batch 0 / 40], [Loss 0.0011942997807636857]
|
| 1170 |
+
precision recall f1-score support
|
| 1171 |
+
|
| 1172 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 1173 |
+
bcc 0.6154 0.8000 0.6957 10
|
| 1174 |
+
bkl 0.5455 0.6000 0.5714 10
|
| 1175 |
+
df 0.8000 0.6667 0.7273 12
|
| 1176 |
+
mel 0.4545 0.5556 0.5000 9
|
| 1177 |
+
nv 0.9167 0.6875 0.7857 16
|
| 1178 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1179 |
+
|
| 1180 |
+
accuracy 0.7037 81
|
| 1181 |
+
macro avg 0.7066 0.7109 0.7024 81
|
| 1182 |
+
weighted avg 0.7257 0.7037 0.7079 81
|
| 1183 |
+
|
| 1184 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/last_model.pth[0m
|
| 1185 |
+
[93m[densenet121][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-14-52-26/best_model.pth[0m - Accuracy 77.78%
|
| 1186 |
+
precision recall f1-score support
|
| 1187 |
+
|
| 1188 |
+
akiec 0.8000 0.5333 0.6400 15
|
| 1189 |
+
bcc 0.6154 0.8000 0.6957 10
|
| 1190 |
+
bkl 0.5000 0.7000 0.5833 10
|
| 1191 |
+
df 0.9167 0.9167 0.9167 12
|
| 1192 |
+
mel 0.4545 0.5556 0.5000 9
|
| 1193 |
+
nv 1.0000 0.6875 0.8148 16
|
| 1194 |
+
vasc 0.9000 1.0000 0.9474 9
|
| 1195 |
+
|
| 1196 |
+
accuracy 0.7284 81
|
| 1197 |
+
macro avg 0.7409 0.7419 0.7283 81
|
| 1198 |
+
weighted avg 0.7697 0.7284 0.7340 81
|
| 1199 |
+
|
models/DenseNet121/logs/test_logs_acc_2021-12-12-14-52-26.txt
ADDED
|
@@ -0,0 +1,40 @@
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| 1 |
+
0,0.2839506172839506
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1,0.345679012345679
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2,0.5802469135802469
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3,0.4691358024691358
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4,0.5185185185185185
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5,0.5061728395061729
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6,0.5679012345679012
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7,0.691358024691358
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8,0.5185185185185185
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9,0.41975308641975306
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10,0.6419753086419753
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11,0.5802469135802469
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12,0.6419753086419753
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13,0.6172839506172839
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14,0.3950617283950617
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15,0.6296296296296297
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16,0.691358024691358
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17,0.6296296296296297
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18,0.7407407407407407
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| 20 |
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19,0.7160493827160493
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| 21 |
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20,0.6790123456790124
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21,0.7283950617283951
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22,0.7777777777777778
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| 24 |
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23,0.7530864197530864
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| 25 |
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24,0.7407407407407407
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| 26 |
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25,0.7407407407407407
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| 27 |
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26,0.7160493827160493
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| 28 |
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27,0.7283950617283951
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28,0.7160493827160493
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29,0.6172839506172839
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30,0.7037037037037037
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31,0.7037037037037037
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32,0.691358024691358
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33,0.7037037037037037
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| 35 |
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34,0.691358024691358
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| 36 |
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35,0.691358024691358
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| 37 |
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36,0.691358024691358
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| 38 |
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37,0.7037037037037037
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| 39 |
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38,0.6790123456790124
|
| 40 |
+
39,0.7037037037037037
|
models/DenseNet121/logs/train_logs_acc_2021-12-12-14-52-26.txt
ADDED
|
@@ -0,0 +1,40 @@
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|
| 1 |
+
0,0.4889502762430939
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| 2 |
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1,0.7099447513812155
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2,0.7279005524861878
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3,0.787292817679558
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4,0.8411602209944752
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5,0.8825966850828729
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6,0.9406077348066298
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7,0.9475138121546961
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8,0.9378453038674033
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9,0.9461325966850829
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| 11 |
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10,0.962707182320442
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| 12 |
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11,0.9834254143646409
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| 13 |
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12,0.9917127071823204
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13,0.9848066298342542
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14,0.9861878453038674
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15,0.9861878453038674
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16,0.9903314917127072
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17,0.994475138121547
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| 19 |
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18,0.9917127071823204
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19,0.9917127071823204
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20,0.9917127071823204
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| 22 |
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21,0.9972375690607734
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| 23 |
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22,0.9972375690607734
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| 24 |
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23,1.0
|
| 25 |
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24,1.0
|
| 26 |
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25,1.0
|
| 27 |
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26,0.9986187845303868
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| 28 |
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27,0.9986187845303868
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| 29 |
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28,0.994475138121547
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| 30 |
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29,0.9903314917127072
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| 31 |
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30,0.994475138121547
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| 32 |
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31,1.0
|
| 33 |
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32,0.9986187845303868
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| 34 |
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33,1.0
|
| 35 |
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34,1.0
|
| 36 |
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35,1.0
|
| 37 |
+
36,1.0
|
| 38 |
+
37,1.0
|
| 39 |
+
38,1.0
|
| 40 |
+
39,1.0
|
models/DenseNet121/logs/train_logs_loss_2021-12-12-14-52-26.txt
ADDED
|
@@ -0,0 +1,40 @@
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|
| 1 |
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0,1.3115723133087158
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| 2 |
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1,0.8430644273757935
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| 3 |
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2,0.7275338172912598
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3,0.5839248895645142
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4,0.43988701701164246
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5,0.3507845401763916
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6,0.2043382227420807
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7,0.15382269024848938
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| 9 |
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8,0.19266119599342346
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| 10 |
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9,0.15648579597473145
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| 11 |
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10,0.12639513611793518
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| 12 |
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11,0.08402916043996811
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| 13 |
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12,0.04659491032361984
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13,0.06116586923599243
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14,0.07438090443611145
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| 16 |
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15,0.05328061804175377
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16,0.039055921137332916
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17,0.034553162753582
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| 19 |
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18,0.03516067564487457
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19,0.03217845782637596
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20,0.03015070967376232
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21,0.016602592542767525
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| 23 |
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22,0.013648818247020245
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| 24 |
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23,0.0056058987975120544
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| 25 |
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24,0.005661854520440102
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| 26 |
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25,0.007601853460073471
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| 27 |
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26,0.008942866697907448
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| 28 |
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27,0.01236275490373373
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| 29 |
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28,0.019639354199171066
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| 30 |
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29,0.03432326763868332
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| 31 |
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30,0.022994978353381157
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| 32 |
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31,0.012169293127954006
|
| 33 |
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32,0.008412164635956287
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| 34 |
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33,0.003678540699183941
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| 35 |
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34,0.004635999910533428
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| 36 |
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35,0.004215737339109182
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| 37 |
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36,0.002594105899333954
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| 38 |
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37,0.0016405590577051044
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| 39 |
+
38,0.004391920752823353
|
| 40 |
+
39,0.0025029259268194437
|
models/MobileNetV2/best_model.pth
ADDED
|
@@ -0,0 +1,3 @@
|
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|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b54066967da855a7e9487c661221a17689a9fddffa15982ef63ad10b56178428
|
| 3 |
+
size 9198861
|
models/MobileNetV2/config.json
ADDED
|
@@ -0,0 +1,25 @@
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|
| 1 |
+
{
|
| 2 |
+
"num_classes": 7,
|
| 3 |
+
"hidden_size": 1280,
|
| 4 |
+
"id2label": {
|
| 5 |
+
"0": "akiec",
|
| 6 |
+
"1": "bcc",
|
| 7 |
+
"2": "bkl",
|
| 8 |
+
"3": "df",
|
| 9 |
+
"4": "mel",
|
| 10 |
+
"5": "nv",
|
| 11 |
+
"6": "vasc"
|
| 12 |
+
},
|
| 13 |
+
"label2id": {
|
| 14 |
+
"akiec": "0",
|
| 15 |
+
"bcc": "1",
|
| 16 |
+
"bkl": "2",
|
| 17 |
+
"df": "3",
|
| 18 |
+
"mel": "4",
|
| 19 |
+
"nv": "5",
|
| 20 |
+
"vasc": "6"
|
| 21 |
+
},
|
| 22 |
+
"architectures": [
|
| 23 |
+
"mobilenet_v2"
|
| 24 |
+
]
|
| 25 |
+
}
|
models/MobileNetV2/logs/logs_2021-12-12-15-41-03.txt
ADDED
|
@@ -0,0 +1,984 @@
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|
| 1 |
+
==================================================
|
| 2 |
+
Model architecture:
|
| 3 |
+
==================================================
|
| 4 |
+
MobileNetV2(
|
| 5 |
+
(features): Sequential(
|
| 6 |
+
(0): ConvBNActivation(
|
| 7 |
+
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
| 8 |
+
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 9 |
+
(2): ReLU6(inplace=True)
|
| 10 |
+
)
|
| 11 |
+
(1): InvertedResidual(
|
| 12 |
+
(conv): Sequential(
|
| 13 |
+
(0): ConvBNActivation(
|
| 14 |
+
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
|
| 15 |
+
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 16 |
+
(2): ReLU6(inplace=True)
|
| 17 |
+
)
|
| 18 |
+
(1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 19 |
+
(2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 20 |
+
)
|
| 21 |
+
)
|
| 22 |
+
(2): InvertedResidual(
|
| 23 |
+
(conv): Sequential(
|
| 24 |
+
(0): ConvBNActivation(
|
| 25 |
+
(0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 26 |
+
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 27 |
+
(2): ReLU6(inplace=True)
|
| 28 |
+
)
|
| 29 |
+
(1): ConvBNActivation(
|
| 30 |
+
(0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
|
| 31 |
+
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 32 |
+
(2): ReLU6(inplace=True)
|
| 33 |
+
)
|
| 34 |
+
(2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 35 |
+
(3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 36 |
+
)
|
| 37 |
+
)
|
| 38 |
+
(3): InvertedResidual(
|
| 39 |
+
(conv): Sequential(
|
| 40 |
+
(0): ConvBNActivation(
|
| 41 |
+
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 42 |
+
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 43 |
+
(2): ReLU6(inplace=True)
|
| 44 |
+
)
|
| 45 |
+
(1): ConvBNActivation(
|
| 46 |
+
(0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
| 47 |
+
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 48 |
+
(2): ReLU6(inplace=True)
|
| 49 |
+
)
|
| 50 |
+
(2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 51 |
+
(3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 52 |
+
)
|
| 53 |
+
)
|
| 54 |
+
(4): InvertedResidual(
|
| 55 |
+
(conv): Sequential(
|
| 56 |
+
(0): ConvBNActivation(
|
| 57 |
+
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 58 |
+
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 59 |
+
(2): ReLU6(inplace=True)
|
| 60 |
+
)
|
| 61 |
+
(1): ConvBNActivation(
|
| 62 |
+
(0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
|
| 63 |
+
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 64 |
+
(2): ReLU6(inplace=True)
|
| 65 |
+
)
|
| 66 |
+
(2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 67 |
+
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 68 |
+
)
|
| 69 |
+
)
|
| 70 |
+
(5): InvertedResidual(
|
| 71 |
+
(conv): Sequential(
|
| 72 |
+
(0): ConvBNActivation(
|
| 73 |
+
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 74 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 75 |
+
(2): ReLU6(inplace=True)
|
| 76 |
+
)
|
| 77 |
+
(1): ConvBNActivation(
|
| 78 |
+
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
|
| 79 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 80 |
+
(2): ReLU6(inplace=True)
|
| 81 |
+
)
|
| 82 |
+
(2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 83 |
+
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 84 |
+
)
|
| 85 |
+
)
|
| 86 |
+
(6): InvertedResidual(
|
| 87 |
+
(conv): Sequential(
|
| 88 |
+
(0): ConvBNActivation(
|
| 89 |
+
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 90 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 91 |
+
(2): ReLU6(inplace=True)
|
| 92 |
+
)
|
| 93 |
+
(1): ConvBNActivation(
|
| 94 |
+
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
|
| 95 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 96 |
+
(2): ReLU6(inplace=True)
|
| 97 |
+
)
|
| 98 |
+
(2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 99 |
+
(3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 100 |
+
)
|
| 101 |
+
)
|
| 102 |
+
(7): InvertedResidual(
|
| 103 |
+
(conv): Sequential(
|
| 104 |
+
(0): ConvBNActivation(
|
| 105 |
+
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 106 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 107 |
+
(2): ReLU6(inplace=True)
|
| 108 |
+
)
|
| 109 |
+
(1): ConvBNActivation(
|
| 110 |
+
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
|
| 111 |
+
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 112 |
+
(2): ReLU6(inplace=True)
|
| 113 |
+
)
|
| 114 |
+
(2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 115 |
+
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 116 |
+
)
|
| 117 |
+
)
|
| 118 |
+
(8): InvertedResidual(
|
| 119 |
+
(conv): Sequential(
|
| 120 |
+
(0): ConvBNActivation(
|
| 121 |
+
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 122 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 123 |
+
(2): ReLU6(inplace=True)
|
| 124 |
+
)
|
| 125 |
+
(1): ConvBNActivation(
|
| 126 |
+
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
|
| 127 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 128 |
+
(2): ReLU6(inplace=True)
|
| 129 |
+
)
|
| 130 |
+
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 131 |
+
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 132 |
+
)
|
| 133 |
+
)
|
| 134 |
+
(9): InvertedResidual(
|
| 135 |
+
(conv): Sequential(
|
| 136 |
+
(0): ConvBNActivation(
|
| 137 |
+
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 138 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 139 |
+
(2): ReLU6(inplace=True)
|
| 140 |
+
)
|
| 141 |
+
(1): ConvBNActivation(
|
| 142 |
+
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
|
| 143 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 144 |
+
(2): ReLU6(inplace=True)
|
| 145 |
+
)
|
| 146 |
+
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 147 |
+
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 148 |
+
)
|
| 149 |
+
)
|
| 150 |
+
(10): InvertedResidual(
|
| 151 |
+
(conv): Sequential(
|
| 152 |
+
(0): ConvBNActivation(
|
| 153 |
+
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 154 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 155 |
+
(2): ReLU6(inplace=True)
|
| 156 |
+
)
|
| 157 |
+
(1): ConvBNActivation(
|
| 158 |
+
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
|
| 159 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 160 |
+
(2): ReLU6(inplace=True)
|
| 161 |
+
)
|
| 162 |
+
(2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 163 |
+
(3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 164 |
+
)
|
| 165 |
+
)
|
| 166 |
+
(11): InvertedResidual(
|
| 167 |
+
(conv): Sequential(
|
| 168 |
+
(0): ConvBNActivation(
|
| 169 |
+
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 170 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 171 |
+
(2): ReLU6(inplace=True)
|
| 172 |
+
)
|
| 173 |
+
(1): ConvBNActivation(
|
| 174 |
+
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
|
| 175 |
+
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 176 |
+
(2): ReLU6(inplace=True)
|
| 177 |
+
)
|
| 178 |
+
(2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 179 |
+
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 180 |
+
)
|
| 181 |
+
)
|
| 182 |
+
(12): InvertedResidual(
|
| 183 |
+
(conv): Sequential(
|
| 184 |
+
(0): ConvBNActivation(
|
| 185 |
+
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 186 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 187 |
+
(2): ReLU6(inplace=True)
|
| 188 |
+
)
|
| 189 |
+
(1): ConvBNActivation(
|
| 190 |
+
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
|
| 191 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 192 |
+
(2): ReLU6(inplace=True)
|
| 193 |
+
)
|
| 194 |
+
(2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 195 |
+
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 196 |
+
)
|
| 197 |
+
)
|
| 198 |
+
(13): InvertedResidual(
|
| 199 |
+
(conv): Sequential(
|
| 200 |
+
(0): ConvBNActivation(
|
| 201 |
+
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 202 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 203 |
+
(2): ReLU6(inplace=True)
|
| 204 |
+
)
|
| 205 |
+
(1): ConvBNActivation(
|
| 206 |
+
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
|
| 207 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 208 |
+
(2): ReLU6(inplace=True)
|
| 209 |
+
)
|
| 210 |
+
(2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 211 |
+
(3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 212 |
+
)
|
| 213 |
+
)
|
| 214 |
+
(14): InvertedResidual(
|
| 215 |
+
(conv): Sequential(
|
| 216 |
+
(0): ConvBNActivation(
|
| 217 |
+
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 218 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 219 |
+
(2): ReLU6(inplace=True)
|
| 220 |
+
)
|
| 221 |
+
(1): ConvBNActivation(
|
| 222 |
+
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
|
| 223 |
+
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 224 |
+
(2): ReLU6(inplace=True)
|
| 225 |
+
)
|
| 226 |
+
(2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 227 |
+
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 228 |
+
)
|
| 229 |
+
)
|
| 230 |
+
(15): InvertedResidual(
|
| 231 |
+
(conv): Sequential(
|
| 232 |
+
(0): ConvBNActivation(
|
| 233 |
+
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 234 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 235 |
+
(2): ReLU6(inplace=True)
|
| 236 |
+
)
|
| 237 |
+
(1): ConvBNActivation(
|
| 238 |
+
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
|
| 239 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 240 |
+
(2): ReLU6(inplace=True)
|
| 241 |
+
)
|
| 242 |
+
(2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 243 |
+
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 244 |
+
)
|
| 245 |
+
)
|
| 246 |
+
(16): InvertedResidual(
|
| 247 |
+
(conv): Sequential(
|
| 248 |
+
(0): ConvBNActivation(
|
| 249 |
+
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 250 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 251 |
+
(2): ReLU6(inplace=True)
|
| 252 |
+
)
|
| 253 |
+
(1): ConvBNActivation(
|
| 254 |
+
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
|
| 255 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 256 |
+
(2): ReLU6(inplace=True)
|
| 257 |
+
)
|
| 258 |
+
(2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 259 |
+
(3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 260 |
+
)
|
| 261 |
+
)
|
| 262 |
+
(17): InvertedResidual(
|
| 263 |
+
(conv): Sequential(
|
| 264 |
+
(0): ConvBNActivation(
|
| 265 |
+
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 266 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 267 |
+
(2): ReLU6(inplace=True)
|
| 268 |
+
)
|
| 269 |
+
(1): ConvBNActivation(
|
| 270 |
+
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
|
| 271 |
+
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 272 |
+
(2): ReLU6(inplace=True)
|
| 273 |
+
)
|
| 274 |
+
(2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 275 |
+
(3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 276 |
+
)
|
| 277 |
+
)
|
| 278 |
+
(18): ConvBNActivation(
|
| 279 |
+
(0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 280 |
+
(1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 281 |
+
(2): ReLU6(inplace=True)
|
| 282 |
+
)
|
| 283 |
+
)
|
| 284 |
+
(classifier): Sequential(
|
| 285 |
+
(0): Dropout(p=0.2, inplace=False)
|
| 286 |
+
(1): Linear(in_features=1280, out_features=7, bias=True)
|
| 287 |
+
)
|
| 288 |
+
)
|
| 289 |
+
==================================================
|
| 290 |
+
|
| 291 |
+
[Epoch 0], [Batch 0 / 40], [Loss 1.9437333345413208]
|
| 292 |
+
precision recall f1-score support
|
| 293 |
+
|
| 294 |
+
akiec 0.5000 0.1667 0.2500 6
|
| 295 |
+
bcc 1.0000 0.2500 0.4000 12
|
| 296 |
+
bkl 0.0000 0.0000 0.0000 13
|
| 297 |
+
df 0.2632 0.3571 0.3030 14
|
| 298 |
+
mel 0.0000 0.0000 0.0000 14
|
| 299 |
+
nv 0.3043 0.9333 0.4590 15
|
| 300 |
+
vasc 0.5455 0.8571 0.6667 7
|
| 301 |
+
|
| 302 |
+
accuracy 0.3580 81
|
| 303 |
+
macro avg 0.3733 0.3663 0.2970 81
|
| 304 |
+
weighted avg 0.3342 0.3580 0.2728 81
|
| 305 |
+
|
| 306 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 307 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 35.80%
|
| 308 |
+
[Epoch 1], [Batch 0 / 40], [Loss 0.7436763644218445]
|
| 309 |
+
precision recall f1-score support
|
| 310 |
+
|
| 311 |
+
akiec 0.1250 0.1667 0.1429 6
|
| 312 |
+
bcc 0.6429 0.7500 0.6923 12
|
| 313 |
+
bkl 0.5714 0.3077 0.4000 13
|
| 314 |
+
df 0.5625 0.6429 0.6000 14
|
| 315 |
+
mel 0.0000 0.0000 0.0000 14
|
| 316 |
+
nv 0.6667 0.6667 0.6667 15
|
| 317 |
+
vasc 0.3333 1.0000 0.5000 7
|
| 318 |
+
|
| 319 |
+
accuracy 0.4938 81
|
| 320 |
+
macro avg 0.4145 0.5048 0.4288 81
|
| 321 |
+
weighted avg 0.4457 0.4938 0.4477 81
|
| 322 |
+
|
| 323 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 324 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 49.38%
|
| 325 |
+
[Epoch 2], [Batch 0 / 40], [Loss 0.4846855700016022]
|
| 326 |
+
precision recall f1-score support
|
| 327 |
+
|
| 328 |
+
akiec 0.3750 0.5000 0.4286 6
|
| 329 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 330 |
+
bkl 0.6667 0.4615 0.5455 13
|
| 331 |
+
df 0.5417 0.9286 0.6842 14
|
| 332 |
+
mel 1.0000 0.1429 0.2500 14
|
| 333 |
+
nv 0.6842 0.8667 0.7647 15
|
| 334 |
+
vasc 0.7778 1.0000 0.8750 7
|
| 335 |
+
|
| 336 |
+
accuracy 0.6420 81
|
| 337 |
+
macro avg 0.6922 0.6523 0.6107 81
|
| 338 |
+
weighted avg 0.7137 0.6420 0.6057 81
|
| 339 |
+
|
| 340 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 341 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 64.20%
|
| 342 |
+
[Epoch 3], [Batch 0 / 40], [Loss 0.2661575376987457]
|
| 343 |
+
precision recall f1-score support
|
| 344 |
+
|
| 345 |
+
akiec 0.5000 0.8333 0.6250 6
|
| 346 |
+
bcc 0.8182 0.7500 0.7826 12
|
| 347 |
+
bkl 0.4667 0.5385 0.5000 13
|
| 348 |
+
df 0.7857 0.7857 0.7857 14
|
| 349 |
+
mel 0.6364 0.5000 0.5600 14
|
| 350 |
+
nv 0.7692 0.6667 0.7143 15
|
| 351 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 352 |
+
|
| 353 |
+
accuracy 0.6914 81
|
| 354 |
+
macro avg 0.7109 0.7249 0.7097 81
|
| 355 |
+
weighted avg 0.7078 0.6914 0.6938 81
|
| 356 |
+
|
| 357 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 358 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 69.14%
|
| 359 |
+
[Epoch 4], [Batch 0 / 40], [Loss 0.12722927331924438]
|
| 360 |
+
precision recall f1-score support
|
| 361 |
+
|
| 362 |
+
akiec 0.2353 0.6667 0.3478 6
|
| 363 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 364 |
+
bkl 0.8000 0.3077 0.4444 13
|
| 365 |
+
df 0.8235 1.0000 0.9032 14
|
| 366 |
+
mel 0.5833 0.5000 0.5385 14
|
| 367 |
+
nv 0.7692 0.6667 0.7143 15
|
| 368 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 369 |
+
|
| 370 |
+
accuracy 0.6667 81
|
| 371 |
+
macro avg 0.7159 0.6868 0.6679 81
|
| 372 |
+
weighted avg 0.7364 0.6667 0.6727 81
|
| 373 |
+
|
| 374 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 375 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 69.14%
|
| 376 |
+
[Epoch 5], [Batch 0 / 40], [Loss 0.26306378841400146]
|
| 377 |
+
precision recall f1-score support
|
| 378 |
+
|
| 379 |
+
akiec 0.3333 0.5000 0.4000 6
|
| 380 |
+
bcc 0.7692 0.8333 0.8000 12
|
| 381 |
+
bkl 0.4286 0.4615 0.4444 13
|
| 382 |
+
df 0.7059 0.8571 0.7742 14
|
| 383 |
+
mel 0.8333 0.3571 0.5000 14
|
| 384 |
+
nv 0.8000 0.8000 0.8000 15
|
| 385 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 386 |
+
|
| 387 |
+
accuracy 0.6790 81
|
| 388 |
+
macro avg 0.6958 0.6870 0.6741 81
|
| 389 |
+
weighted avg 0.7080 0.6790 0.6743 81
|
| 390 |
+
|
| 391 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 392 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 69.14%
|
| 393 |
+
[Epoch 6], [Batch 0 / 40], [Loss 0.26731279492378235]
|
| 394 |
+
precision recall f1-score support
|
| 395 |
+
|
| 396 |
+
akiec 0.2857 0.6667 0.4000 6
|
| 397 |
+
bcc 0.6667 0.5000 0.5714 12
|
| 398 |
+
bkl 0.4615 0.4615 0.4615 13
|
| 399 |
+
df 0.9231 0.8571 0.8889 14
|
| 400 |
+
mel 0.3750 0.4286 0.4000 14
|
| 401 |
+
nv 0.8000 0.5333 0.6400 15
|
| 402 |
+
vasc 1.0000 0.8571 0.9231 7
|
| 403 |
+
|
| 404 |
+
accuracy 0.5926 81
|
| 405 |
+
macro avg 0.6446 0.6149 0.6121 81
|
| 406 |
+
weighted avg 0.6529 0.5926 0.6094 81
|
| 407 |
+
|
| 408 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 409 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 69.14%
|
| 410 |
+
[Epoch 7], [Batch 0 / 40], [Loss 0.13903522491455078]
|
| 411 |
+
precision recall f1-score support
|
| 412 |
+
|
| 413 |
+
akiec 0.5000 0.8333 0.6250 6
|
| 414 |
+
bcc 0.7778 0.5833 0.6667 12
|
| 415 |
+
bkl 0.5455 0.4615 0.5000 13
|
| 416 |
+
df 0.9231 0.8571 0.8889 14
|
| 417 |
+
mel 0.7273 0.5714 0.6400 14
|
| 418 |
+
nv 0.7000 0.9333 0.8000 15
|
| 419 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 420 |
+
|
| 421 |
+
accuracy 0.7284 81
|
| 422 |
+
macro avg 0.7391 0.7486 0.7315 81
|
| 423 |
+
weighted avg 0.7411 0.7284 0.7241 81
|
| 424 |
+
|
| 425 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 426 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 427 |
+
[Epoch 8], [Batch 0 / 40], [Loss 0.03321058303117752]
|
| 428 |
+
precision recall f1-score support
|
| 429 |
+
|
| 430 |
+
akiec 0.3333 0.8333 0.4762 6
|
| 431 |
+
bcc 0.6667 0.6667 0.6667 12
|
| 432 |
+
bkl 0.4615 0.4615 0.4615 13
|
| 433 |
+
df 1.0000 0.5714 0.7273 14
|
| 434 |
+
mel 0.4286 0.4286 0.4286 14
|
| 435 |
+
nv 0.7500 0.6000 0.6667 15
|
| 436 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 437 |
+
|
| 438 |
+
accuracy 0.6049 81
|
| 439 |
+
macro avg 0.6629 0.6516 0.6324 81
|
| 440 |
+
weighted avg 0.6698 0.6049 0.6178 81
|
| 441 |
+
|
| 442 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 443 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 444 |
+
[Epoch 9], [Batch 0 / 40], [Loss 0.018830040469765663]
|
| 445 |
+
precision recall f1-score support
|
| 446 |
+
|
| 447 |
+
akiec 0.2500 0.5000 0.3333 6
|
| 448 |
+
bcc 0.8750 0.5833 0.7000 12
|
| 449 |
+
bkl 0.3333 0.3846 0.3571 13
|
| 450 |
+
df 0.7857 0.7857 0.7857 14
|
| 451 |
+
mel 0.5000 0.4286 0.4615 14
|
| 452 |
+
nv 0.8462 0.7333 0.7857 15
|
| 453 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 454 |
+
|
| 455 |
+
accuracy 0.6173 81
|
| 456 |
+
macro avg 0.6557 0.6308 0.6319 81
|
| 457 |
+
weighted avg 0.6670 0.6173 0.6332 81
|
| 458 |
+
|
| 459 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 460 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 461 |
+
[Epoch 10], [Batch 0 / 40], [Loss 0.04818242788314819]
|
| 462 |
+
precision recall f1-score support
|
| 463 |
+
|
| 464 |
+
akiec 0.4000 0.6667 0.5000 6
|
| 465 |
+
bcc 0.7500 0.7500 0.7500 12
|
| 466 |
+
bkl 1.0000 0.1538 0.2667 13
|
| 467 |
+
df 0.7647 0.9286 0.8387 14
|
| 468 |
+
mel 0.4286 0.6429 0.5143 14
|
| 469 |
+
nv 0.7500 0.6000 0.6667 15
|
| 470 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 471 |
+
|
| 472 |
+
accuracy 0.6543 81
|
| 473 |
+
macro avg 0.7276 0.6774 0.6480 81
|
| 474 |
+
weighted avg 0.7328 0.6543 0.6347 81
|
| 475 |
+
|
| 476 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 477 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 478 |
+
[Epoch 11], [Batch 0 / 40], [Loss 0.07165464758872986]
|
| 479 |
+
precision recall f1-score support
|
| 480 |
+
|
| 481 |
+
akiec 0.4444 0.6667 0.5333 6
|
| 482 |
+
bcc 0.7500 0.7500 0.7500 12
|
| 483 |
+
bkl 0.3750 0.4615 0.4138 13
|
| 484 |
+
df 0.9231 0.8571 0.8889 14
|
| 485 |
+
mel 0.5000 0.2857 0.3636 14
|
| 486 |
+
nv 0.8571 0.8000 0.8276 15
|
| 487 |
+
vasc 0.7778 1.0000 0.8750 7
|
| 488 |
+
|
| 489 |
+
accuracy 0.6667 81
|
| 490 |
+
macro avg 0.6611 0.6887 0.6646 81
|
| 491 |
+
weighted avg 0.6761 0.6667 0.6624 81
|
| 492 |
+
|
| 493 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 494 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 495 |
+
[Epoch 12], [Batch 0 / 40], [Loss 0.03201916813850403]
|
| 496 |
+
precision recall f1-score support
|
| 497 |
+
|
| 498 |
+
akiec 0.3846 0.8333 0.5263 6
|
| 499 |
+
bcc 1.0000 0.5833 0.7368 12
|
| 500 |
+
bkl 0.5000 0.3846 0.4348 13
|
| 501 |
+
df 1.0000 0.5000 0.6667 14
|
| 502 |
+
mel 0.5000 0.6429 0.5625 14
|
| 503 |
+
nv 0.6667 0.8000 0.7273 15
|
| 504 |
+
vasc 0.8750 1.0000 0.9333 7
|
| 505 |
+
|
| 506 |
+
accuracy 0.6420 81
|
| 507 |
+
macro avg 0.7038 0.6777 0.6554 81
|
| 508 |
+
weighted avg 0.7152 0.6420 0.6457 81
|
| 509 |
+
|
| 510 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 511 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 512 |
+
[Epoch 13], [Batch 0 / 40], [Loss 0.051025405526161194]
|
| 513 |
+
precision recall f1-score support
|
| 514 |
+
|
| 515 |
+
akiec 0.5000 0.6667 0.5714 6
|
| 516 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 517 |
+
bkl 0.5714 0.6154 0.5926 13
|
| 518 |
+
df 0.9091 0.7143 0.8000 14
|
| 519 |
+
mel 0.5385 0.5000 0.5185 14
|
| 520 |
+
nv 0.7059 0.8000 0.7500 15
|
| 521 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 522 |
+
|
| 523 |
+
accuracy 0.6914 81
|
| 524 |
+
macro avg 0.7074 0.7090 0.7040 81
|
| 525 |
+
weighted avg 0.7038 0.6914 0.6937 81
|
| 526 |
+
|
| 527 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 528 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 72.84%
|
| 529 |
+
[Epoch 14], [Batch 0 / 40], [Loss 0.020453469827771187]
|
| 530 |
+
precision recall f1-score support
|
| 531 |
+
|
| 532 |
+
akiec 0.7500 0.5000 0.6000 6
|
| 533 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 534 |
+
bkl 0.6250 0.7692 0.6897 13
|
| 535 |
+
df 0.8462 0.7857 0.8148 14
|
| 536 |
+
mel 0.6429 0.6429 0.6429 14
|
| 537 |
+
nv 0.7647 0.8667 0.8125 15
|
| 538 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 539 |
+
|
| 540 |
+
accuracy 0.7531 81
|
| 541 |
+
macro avg 0.7755 0.7473 0.7553 81
|
| 542 |
+
weighted avg 0.7598 0.7531 0.7517 81
|
| 543 |
+
|
| 544 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m
|
| 545 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 546 |
+
[Epoch 15], [Batch 0 / 40], [Loss 0.11804791539907455]
|
| 547 |
+
precision recall f1-score support
|
| 548 |
+
|
| 549 |
+
akiec 0.4444 0.6667 0.5333 6
|
| 550 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 551 |
+
bkl 0.5000 0.3846 0.4348 13
|
| 552 |
+
df 0.8333 0.7143 0.7692 14
|
| 553 |
+
mel 0.4706 0.5714 0.5161 14
|
| 554 |
+
nv 0.7059 0.8000 0.7500 15
|
| 555 |
+
vasc 1.0000 0.8571 0.9231 7
|
| 556 |
+
|
| 557 |
+
accuracy 0.6543 81
|
| 558 |
+
macro avg 0.6792 0.6658 0.6648 81
|
| 559 |
+
weighted avg 0.6742 0.6543 0.6579 81
|
| 560 |
+
|
| 561 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 562 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 563 |
+
[Epoch 16], [Batch 0 / 40], [Loss 0.02361106500029564]
|
| 564 |
+
precision recall f1-score support
|
| 565 |
+
|
| 566 |
+
akiec 0.5000 0.5000 0.5000 6
|
| 567 |
+
bcc 0.8333 0.8333 0.8333 12
|
| 568 |
+
bkl 0.5455 0.4615 0.5000 13
|
| 569 |
+
df 0.8889 0.5714 0.6957 14
|
| 570 |
+
mel 0.5238 0.7857 0.6286 14
|
| 571 |
+
nv 0.6667 0.5333 0.5926 15
|
| 572 |
+
vasc 0.7000 1.0000 0.8235 7
|
| 573 |
+
|
| 574 |
+
accuracy 0.6543 81
|
| 575 |
+
macro avg 0.6655 0.6693 0.6534 81
|
| 576 |
+
weighted avg 0.6762 0.6543 0.6505 81
|
| 577 |
+
|
| 578 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 579 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 580 |
+
[Epoch 17], [Batch 0 / 40], [Loss 0.06163478642702103]
|
| 581 |
+
precision recall f1-score support
|
| 582 |
+
|
| 583 |
+
akiec 0.3636 0.6667 0.4706 6
|
| 584 |
+
bcc 0.6000 0.7500 0.6667 12
|
| 585 |
+
bkl 0.5714 0.6154 0.5926 13
|
| 586 |
+
df 0.9000 0.6429 0.7500 14
|
| 587 |
+
mel 0.6364 0.5000 0.5600 14
|
| 588 |
+
nv 0.7692 0.6667 0.7143 15
|
| 589 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 590 |
+
|
| 591 |
+
accuracy 0.6667 81
|
| 592 |
+
macro avg 0.6915 0.6917 0.6792 81
|
| 593 |
+
weighted avg 0.7019 0.6667 0.6738 81
|
| 594 |
+
|
| 595 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 596 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 597 |
+
[Epoch 18], [Batch 0 / 40], [Loss 0.08096787333488464]
|
| 598 |
+
precision recall f1-score support
|
| 599 |
+
|
| 600 |
+
akiec 0.4167 0.8333 0.5556 6
|
| 601 |
+
bcc 0.6667 0.6667 0.6667 12
|
| 602 |
+
bkl 0.7500 0.6923 0.7200 13
|
| 603 |
+
df 1.0000 0.7143 0.8333 14
|
| 604 |
+
mel 0.4706 0.5714 0.5161 14
|
| 605 |
+
nv 0.8182 0.6000 0.6923 15
|
| 606 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 607 |
+
|
| 608 |
+
accuracy 0.6914 81
|
| 609 |
+
macro avg 0.7317 0.7254 0.7120 81
|
| 610 |
+
weighted avg 0.7421 0.6914 0.7033 81
|
| 611 |
+
|
| 612 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 613 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 614 |
+
[Epoch 19], [Batch 0 / 40], [Loss 0.061570439487695694]
|
| 615 |
+
precision recall f1-score support
|
| 616 |
+
|
| 617 |
+
akiec 0.4545 0.8333 0.5882 6
|
| 618 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 619 |
+
bkl 0.7273 0.6154 0.6667 13
|
| 620 |
+
df 0.9091 0.7143 0.8000 14
|
| 621 |
+
mel 0.5294 0.6429 0.5806 14
|
| 622 |
+
nv 0.7692 0.6667 0.7143 15
|
| 623 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 624 |
+
|
| 625 |
+
accuracy 0.7037 81
|
| 626 |
+
macro avg 0.7310 0.7342 0.7208 81
|
| 627 |
+
weighted avg 0.7356 0.7037 0.7110 81
|
| 628 |
+
|
| 629 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 630 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 631 |
+
[Epoch 20], [Batch 0 / 40], [Loss 0.010024969466030598]
|
| 632 |
+
precision recall f1-score support
|
| 633 |
+
|
| 634 |
+
akiec 0.3846 0.8333 0.5263 6
|
| 635 |
+
bcc 0.7000 0.5833 0.6364 12
|
| 636 |
+
bkl 0.6000 0.6923 0.6429 13
|
| 637 |
+
df 0.9000 0.6429 0.7500 14
|
| 638 |
+
mel 0.7000 0.5000 0.5833 14
|
| 639 |
+
nv 0.7500 0.8000 0.7742 15
|
| 640 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 641 |
+
|
| 642 |
+
accuracy 0.6914 81
|
| 643 |
+
macro avg 0.7192 0.7217 0.7019 81
|
| 644 |
+
weighted avg 0.7303 0.6914 0.6967 81
|
| 645 |
+
|
| 646 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 647 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 648 |
+
[Epoch 21], [Batch 0 / 40], [Loss 0.03258311748504639]
|
| 649 |
+
precision recall f1-score support
|
| 650 |
+
|
| 651 |
+
akiec 0.2500 0.6667 0.3636 6
|
| 652 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 653 |
+
bkl 0.6364 0.5385 0.5833 13
|
| 654 |
+
df 0.8000 0.5714 0.6667 14
|
| 655 |
+
mel 0.7778 0.5000 0.6087 14
|
| 656 |
+
nv 0.7059 0.8000 0.7500 15
|
| 657 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 658 |
+
|
| 659 |
+
accuracy 0.6543 81
|
| 660 |
+
macro avg 0.6996 0.6776 0.6669 81
|
| 661 |
+
weighted avg 0.7182 0.6543 0.6694 81
|
| 662 |
+
|
| 663 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 664 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 665 |
+
[Epoch 22], [Batch 0 / 40], [Loss 0.009060573764145374]
|
| 666 |
+
precision recall f1-score support
|
| 667 |
+
|
| 668 |
+
akiec 0.3846 0.8333 0.5263 6
|
| 669 |
+
bcc 0.6667 0.6667 0.6667 12
|
| 670 |
+
bkl 0.6667 0.6154 0.6400 13
|
| 671 |
+
df 0.9000 0.6429 0.7500 14
|
| 672 |
+
mel 0.7778 0.5000 0.6087 14
|
| 673 |
+
nv 0.7222 0.8667 0.7879 15
|
| 674 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 675 |
+
|
| 676 |
+
accuracy 0.7037 81
|
| 677 |
+
macro avg 0.7311 0.7321 0.7114 81
|
| 678 |
+
weighted avg 0.7444 0.7037 0.7076 81
|
| 679 |
+
|
| 680 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 681 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 682 |
+
[Epoch 23], [Batch 0 / 40], [Loss 0.007481844164431095]
|
| 683 |
+
precision recall f1-score support
|
| 684 |
+
|
| 685 |
+
akiec 0.2857 0.6667 0.4000 6
|
| 686 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 687 |
+
bkl 0.5455 0.4615 0.5000 13
|
| 688 |
+
df 0.8182 0.6429 0.7200 14
|
| 689 |
+
mel 0.7778 0.5000 0.6087 14
|
| 690 |
+
nv 0.7222 0.8667 0.7879 15
|
| 691 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 692 |
+
|
| 693 |
+
accuracy 0.6667 81
|
| 694 |
+
macro avg 0.6967 0.6863 0.6732 81
|
| 695 |
+
weighted avg 0.7125 0.6667 0.6749 81
|
| 696 |
+
|
| 697 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 698 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 699 |
+
[Epoch 24], [Batch 0 / 40], [Loss 0.003717311192303896]
|
| 700 |
+
precision recall f1-score support
|
| 701 |
+
|
| 702 |
+
akiec 0.3333 0.6667 0.4444 6
|
| 703 |
+
bcc 0.6923 0.7500 0.7200 12
|
| 704 |
+
bkl 0.6364 0.5385 0.5833 13
|
| 705 |
+
df 0.9000 0.6429 0.7500 14
|
| 706 |
+
mel 0.5833 0.5000 0.5385 14
|
| 707 |
+
nv 0.6875 0.7333 0.7097 15
|
| 708 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 709 |
+
|
| 710 |
+
accuracy 0.6667 81
|
| 711 |
+
macro avg 0.6904 0.6902 0.6780 81
|
| 712 |
+
weighted avg 0.6995 0.6667 0.6737 81
|
| 713 |
+
|
| 714 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 715 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 716 |
+
[Epoch 25], [Batch 0 / 40], [Loss 0.00521900225430727]
|
| 717 |
+
precision recall f1-score support
|
| 718 |
+
|
| 719 |
+
akiec 0.3077 0.6667 0.4211 6
|
| 720 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 721 |
+
bkl 0.4444 0.3077 0.3636 13
|
| 722 |
+
df 0.8333 0.7143 0.7692 14
|
| 723 |
+
mel 0.5714 0.5714 0.5714 14
|
| 724 |
+
nv 0.7333 0.7333 0.7333 15
|
| 725 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 726 |
+
|
| 727 |
+
accuracy 0.6420 81
|
| 728 |
+
macro avg 0.6596 0.6657 0.6506 81
|
| 729 |
+
weighted avg 0.6669 0.6420 0.6466 81
|
| 730 |
+
|
| 731 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 732 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 733 |
+
[Epoch 26], [Batch 0 / 40], [Loss 0.002388110850006342]
|
| 734 |
+
precision recall f1-score support
|
| 735 |
+
|
| 736 |
+
akiec 0.3333 0.6667 0.4444 6
|
| 737 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 738 |
+
bkl 0.6667 0.6154 0.6400 13
|
| 739 |
+
df 0.8182 0.6429 0.7200 14
|
| 740 |
+
mel 0.5833 0.5000 0.5385 14
|
| 741 |
+
nv 0.6875 0.7333 0.7097 15
|
| 742 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 743 |
+
|
| 744 |
+
accuracy 0.6667 81
|
| 745 |
+
macro avg 0.6880 0.6893 0.6783 81
|
| 746 |
+
weighted avg 0.6954 0.6667 0.6741 81
|
| 747 |
+
|
| 748 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 749 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 750 |
+
[Epoch 27], [Batch 0 / 40], [Loss 0.0014026282588019967]
|
| 751 |
+
precision recall f1-score support
|
| 752 |
+
|
| 753 |
+
akiec 0.3636 0.6667 0.4706 6
|
| 754 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 755 |
+
bkl 0.6429 0.6923 0.6667 13
|
| 756 |
+
df 0.8333 0.7143 0.7692 14
|
| 757 |
+
mel 0.6364 0.5000 0.5600 14
|
| 758 |
+
nv 0.7333 0.7333 0.7333 15
|
| 759 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 760 |
+
|
| 761 |
+
accuracy 0.6914 81
|
| 762 |
+
macro avg 0.7053 0.7105 0.6994 81
|
| 763 |
+
weighted avg 0.7141 0.6914 0.6969 81
|
| 764 |
+
|
| 765 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 766 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 767 |
+
[Epoch 28], [Batch 0 / 40], [Loss 0.004848898854106665]
|
| 768 |
+
precision recall f1-score support
|
| 769 |
+
|
| 770 |
+
akiec 0.3077 0.6667 0.4211 6
|
| 771 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 772 |
+
bkl 0.6154 0.6154 0.6154 13
|
| 773 |
+
df 0.8333 0.7143 0.7692 14
|
| 774 |
+
mel 0.6364 0.5000 0.5600 14
|
| 775 |
+
nv 0.7333 0.7333 0.7333 15
|
| 776 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 777 |
+
|
| 778 |
+
accuracy 0.6790 81
|
| 779 |
+
macro avg 0.7037 0.6995 0.6895 81
|
| 780 |
+
weighted avg 0.7163 0.6790 0.6897 81
|
| 781 |
+
|
| 782 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 783 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 784 |
+
[Epoch 29], [Batch 0 / 40], [Loss 0.014736742712557316]
|
| 785 |
+
precision recall f1-score support
|
| 786 |
+
|
| 787 |
+
akiec 0.3846 0.8333 0.5263 6
|
| 788 |
+
bcc 0.6667 0.6667 0.6667 12
|
| 789 |
+
bkl 0.5385 0.5385 0.5385 13
|
| 790 |
+
df 1.0000 0.6429 0.7826 14
|
| 791 |
+
mel 0.6667 0.5714 0.6154 14
|
| 792 |
+
nv 0.7500 0.8000 0.7742 15
|
| 793 |
+
vasc 1.0000 0.8571 0.9231 7
|
| 794 |
+
|
| 795 |
+
accuracy 0.6790 81
|
| 796 |
+
macro avg 0.7152 0.7014 0.6895 81
|
| 797 |
+
weighted avg 0.7270 0.6790 0.6889 81
|
| 798 |
+
|
| 799 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 800 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 801 |
+
[Epoch 30], [Batch 0 / 40], [Loss 0.0035465408582240343]
|
| 802 |
+
precision recall f1-score support
|
| 803 |
+
|
| 804 |
+
akiec 0.3846 0.8333 0.5263 6
|
| 805 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 806 |
+
bkl 0.6364 0.5385 0.5833 13
|
| 807 |
+
df 1.0000 0.7143 0.8333 14
|
| 808 |
+
mel 0.6923 0.6429 0.6667 14
|
| 809 |
+
nv 0.7647 0.8667 0.8125 15
|
| 810 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 811 |
+
|
| 812 |
+
accuracy 0.7284 81
|
| 813 |
+
macro avg 0.7540 0.7518 0.7356 81
|
| 814 |
+
weighted avg 0.7697 0.7284 0.7365 81
|
| 815 |
+
|
| 816 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 817 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 818 |
+
[Epoch 31], [Batch 0 / 40], [Loss 0.0014501283876597881]
|
| 819 |
+
precision recall f1-score support
|
| 820 |
+
|
| 821 |
+
akiec 0.3333 0.8333 0.4762 6
|
| 822 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 823 |
+
bkl 0.7500 0.4615 0.5714 13
|
| 824 |
+
df 0.9000 0.6429 0.7500 14
|
| 825 |
+
mel 0.6429 0.6429 0.6429 14
|
| 826 |
+
nv 0.6875 0.7333 0.7097 15
|
| 827 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 828 |
+
|
| 829 |
+
accuracy 0.6790 81
|
| 830 |
+
macro avg 0.7201 0.7115 0.6923 81
|
| 831 |
+
weighted avg 0.7332 0.6790 0.6886 81
|
| 832 |
+
|
| 833 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 834 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 835 |
+
[Epoch 32], [Batch 0 / 40], [Loss 0.0008513450156897306]
|
| 836 |
+
precision recall f1-score support
|
| 837 |
+
|
| 838 |
+
akiec 0.3125 0.8333 0.4545 6
|
| 839 |
+
bcc 0.7500 0.7500 0.7500 12
|
| 840 |
+
bkl 0.6667 0.4615 0.5455 13
|
| 841 |
+
df 1.0000 0.7143 0.8333 14
|
| 842 |
+
mel 0.5000 0.5000 0.5000 14
|
| 843 |
+
nv 0.6923 0.6000 0.6429 15
|
| 844 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 845 |
+
|
| 846 |
+
accuracy 0.6543 81
|
| 847 |
+
macro avg 0.7031 0.6942 0.6752 81
|
| 848 |
+
weighted avg 0.7151 0.6543 0.6682 81
|
| 849 |
+
|
| 850 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 851 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 852 |
+
[Epoch 33], [Batch 0 / 40], [Loss 0.000744424294680357]
|
| 853 |
+
precision recall f1-score support
|
| 854 |
+
|
| 855 |
+
akiec 0.2941 0.8333 0.4348 6
|
| 856 |
+
bcc 0.7273 0.6667 0.6957 12
|
| 857 |
+
bkl 0.6250 0.3846 0.4762 13
|
| 858 |
+
df 0.9091 0.7143 0.8000 14
|
| 859 |
+
mel 0.5714 0.5714 0.5714 14
|
| 860 |
+
nv 0.6923 0.6000 0.6429 15
|
| 861 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 862 |
+
|
| 863 |
+
accuracy 0.6420 81
|
| 864 |
+
macro avg 0.6885 0.6815 0.6601 81
|
| 865 |
+
weighted avg 0.7004 0.6420 0.6542 81
|
| 866 |
+
|
| 867 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 868 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 869 |
+
[Epoch 34], [Batch 0 / 40], [Loss 0.0017604961758479476]
|
| 870 |
+
precision recall f1-score support
|
| 871 |
+
|
| 872 |
+
akiec 0.3571 0.8333 0.5000 6
|
| 873 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 874 |
+
bkl 0.6667 0.6154 0.6400 13
|
| 875 |
+
df 0.9091 0.7143 0.8000 14
|
| 876 |
+
mel 0.5333 0.5714 0.5517 14
|
| 877 |
+
nv 0.7500 0.6000 0.6667 15
|
| 878 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 879 |
+
|
| 880 |
+
accuracy 0.6790 81
|
| 881 |
+
macro avg 0.7166 0.7144 0.6980 81
|
| 882 |
+
weighted avg 0.7266 0.6790 0.6910 81
|
| 883 |
+
|
| 884 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 885 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 886 |
+
[Epoch 35], [Batch 0 / 40], [Loss 0.0005075272056274116]
|
| 887 |
+
precision recall f1-score support
|
| 888 |
+
|
| 889 |
+
akiec 0.3125 0.8333 0.4545 6
|
| 890 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 891 |
+
bkl 0.6154 0.6154 0.6154 13
|
| 892 |
+
df 1.0000 0.7143 0.8333 14
|
| 893 |
+
mel 0.5833 0.5000 0.5385 14
|
| 894 |
+
nv 0.7692 0.6667 0.7143 15
|
| 895 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 896 |
+
|
| 897 |
+
accuracy 0.6790 81
|
| 898 |
+
macro avg 0.7258 0.7138 0.6976 81
|
| 899 |
+
weighted avg 0.7430 0.6790 0.6960 81
|
| 900 |
+
|
| 901 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 902 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 903 |
+
[Epoch 36], [Batch 0 / 40], [Loss 0.0009864452295005322]
|
| 904 |
+
precision recall f1-score support
|
| 905 |
+
|
| 906 |
+
akiec 0.3571 0.8333 0.5000 6
|
| 907 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 908 |
+
bkl 0.6923 0.6923 0.6923 13
|
| 909 |
+
df 0.9167 0.7857 0.8462 14
|
| 910 |
+
mel 0.5000 0.5000 0.5000 14
|
| 911 |
+
nv 0.7273 0.5333 0.6154 15
|
| 912 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 913 |
+
|
| 914 |
+
accuracy 0.6790 81
|
| 915 |
+
macro avg 0.7133 0.7159 0.6973 81
|
| 916 |
+
weighted avg 0.7220 0.6790 0.6889 81
|
| 917 |
+
|
| 918 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 919 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 920 |
+
[Epoch 37], [Batch 0 / 40], [Loss 0.006578400731086731]
|
| 921 |
+
precision recall f1-score support
|
| 922 |
+
|
| 923 |
+
akiec 0.2857 0.6667 0.4000 6
|
| 924 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 925 |
+
bkl 0.6154 0.6154 0.6154 13
|
| 926 |
+
df 0.9000 0.6429 0.7500 14
|
| 927 |
+
mel 0.4667 0.5000 0.4828 14
|
| 928 |
+
nv 0.6667 0.5333 0.5926 15
|
| 929 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 930 |
+
|
| 931 |
+
accuracy 0.6296 81
|
| 932 |
+
macro avg 0.6763 0.6607 0.6526 81
|
| 933 |
+
weighted avg 0.6845 0.6296 0.6454 81
|
| 934 |
+
|
| 935 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 936 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 937 |
+
[Epoch 38], [Batch 0 / 40], [Loss 0.001567276893183589]
|
| 938 |
+
precision recall f1-score support
|
| 939 |
+
|
| 940 |
+
akiec 0.3333 0.6667 0.4444 6
|
| 941 |
+
bcc 0.8000 0.6667 0.7273 12
|
| 942 |
+
bkl 0.6667 0.7692 0.7143 13
|
| 943 |
+
df 0.9091 0.7143 0.8000 14
|
| 944 |
+
mel 0.5833 0.5000 0.5385 14
|
| 945 |
+
nv 0.7857 0.7333 0.7586 15
|
| 946 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 947 |
+
|
| 948 |
+
accuracy 0.7037 81
|
| 949 |
+
macro avg 0.7254 0.7215 0.7119 81
|
| 950 |
+
weighted avg 0.7401 0.7037 0.7135 81
|
| 951 |
+
|
| 952 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 953 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 954 |
+
[Epoch 39], [Batch 0 / 40], [Loss 0.0033837121445685625]
|
| 955 |
+
precision recall f1-score support
|
| 956 |
+
|
| 957 |
+
akiec 0.3636 0.6667 0.4706 6
|
| 958 |
+
bcc 0.8889 0.6667 0.7619 12
|
| 959 |
+
bkl 0.5625 0.6923 0.6207 13
|
| 960 |
+
df 0.9231 0.8571 0.8889 14
|
| 961 |
+
mel 0.6364 0.5000 0.5600 14
|
| 962 |
+
nv 0.7143 0.6667 0.6897 15
|
| 963 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 964 |
+
|
| 965 |
+
accuracy 0.7037 81
|
| 966 |
+
macro avg 0.7270 0.7214 0.7131 81
|
| 967 |
+
weighted avg 0.7371 0.7037 0.7119 81
|
| 968 |
+
|
| 969 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/last_model.pth[0m
|
| 970 |
+
[93m[mobilenet_v2][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-41-03/best_model.pth[0m - Accuracy 75.31%
|
| 971 |
+
precision recall f1-score support
|
| 972 |
+
|
| 973 |
+
akiec 0.6667 0.3333 0.4444 6
|
| 974 |
+
bcc 0.7500 0.7500 0.7500 12
|
| 975 |
+
bkl 0.6667 0.7692 0.7143 13
|
| 976 |
+
df 0.7143 0.7143 0.7143 14
|
| 977 |
+
mel 0.7273 0.5714 0.6400 14
|
| 978 |
+
nv 0.7895 1.0000 0.8824 15
|
| 979 |
+
vasc 1.0000 1.0000 1.0000 7
|
| 980 |
+
|
| 981 |
+
accuracy 0.7531 81
|
| 982 |
+
macro avg 0.7592 0.7340 0.7351 81
|
| 983 |
+
weighted avg 0.7493 0.7531 0.7426 81
|
| 984 |
+
|
models/MobileNetV2/logs/test_logs_acc_2021-12-12-15-41-03.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
0,0.35802469135802467
|
| 2 |
+
1,0.49382716049382713
|
| 3 |
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2,0.6419753086419753
|
| 4 |
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3,0.691358024691358
|
| 5 |
+
4,0.6666666666666666
|
| 6 |
+
5,0.6790123456790124
|
| 7 |
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6,0.5925925925925926
|
| 8 |
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7,0.7283950617283951
|
| 9 |
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8,0.6049382716049383
|
| 10 |
+
9,0.6172839506172839
|
| 11 |
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10,0.654320987654321
|
| 12 |
+
11,0.6666666666666666
|
| 13 |
+
12,0.6419753086419753
|
| 14 |
+
13,0.691358024691358
|
| 15 |
+
14,0.7530864197530864
|
| 16 |
+
15,0.654320987654321
|
| 17 |
+
16,0.654320987654321
|
| 18 |
+
17,0.6666666666666666
|
| 19 |
+
18,0.691358024691358
|
| 20 |
+
19,0.7037037037037037
|
| 21 |
+
20,0.691358024691358
|
| 22 |
+
21,0.654320987654321
|
| 23 |
+
22,0.7037037037037037
|
| 24 |
+
23,0.6666666666666666
|
| 25 |
+
24,0.6666666666666666
|
| 26 |
+
25,0.6419753086419753
|
| 27 |
+
26,0.6666666666666666
|
| 28 |
+
27,0.691358024691358
|
| 29 |
+
28,0.6790123456790124
|
| 30 |
+
29,0.6790123456790124
|
| 31 |
+
30,0.7283950617283951
|
| 32 |
+
31,0.6790123456790124
|
| 33 |
+
32,0.654320987654321
|
| 34 |
+
33,0.6419753086419753
|
| 35 |
+
34,0.6790123456790124
|
| 36 |
+
35,0.6790123456790124
|
| 37 |
+
36,0.6790123456790124
|
| 38 |
+
37,0.6296296296296297
|
| 39 |
+
38,0.7037037037037037
|
| 40 |
+
39,0.7037037037037037
|
models/MobileNetV2/logs/train_logs_acc_2021-12-12-15-41-03.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,0.47928176795580113
|
| 2 |
+
1,0.7071823204419889
|
| 3 |
+
2,0.8397790055248618
|
| 4 |
+
3,0.9019337016574586
|
| 5 |
+
4,0.9240331491712708
|
| 6 |
+
5,0.9046961325966851
|
| 7 |
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6,0.925414364640884
|
| 8 |
+
7,0.9682320441988951
|
| 9 |
+
8,0.9723756906077348
|
| 10 |
+
9,0.9558011049723757
|
| 11 |
+
10,0.9668508287292817
|
| 12 |
+
11,0.9571823204419889
|
| 13 |
+
12,0.9709944751381215
|
| 14 |
+
13,0.9654696132596685
|
| 15 |
+
14,0.9779005524861878
|
| 16 |
+
15,0.9765193370165746
|
| 17 |
+
16,0.9502762430939227
|
| 18 |
+
17,0.9613259668508287
|
| 19 |
+
18,0.9696132596685083
|
| 20 |
+
19,0.9861878453038674
|
| 21 |
+
20,0.9917127071823204
|
| 22 |
+
21,0.988950276243094
|
| 23 |
+
22,0.9958563535911602
|
| 24 |
+
23,1.0
|
| 25 |
+
24,1.0
|
| 26 |
+
25,0.9986187845303868
|
| 27 |
+
26,1.0
|
| 28 |
+
27,1.0
|
| 29 |
+
28,0.9986187845303868
|
| 30 |
+
29,0.9972375690607734
|
| 31 |
+
30,1.0
|
| 32 |
+
31,0.9986187845303868
|
| 33 |
+
32,0.9986187845303868
|
| 34 |
+
33,1.0
|
| 35 |
+
34,1.0
|
| 36 |
+
35,1.0
|
| 37 |
+
36,1.0
|
| 38 |
+
37,0.9986187845303868
|
| 39 |
+
38,1.0
|
| 40 |
+
39,1.0
|
models/MobileNetV2/logs/train_logs_loss_2021-12-12-15-41-03.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,1.3283288478851318
|
| 2 |
+
1,0.7845979928970337
|
| 3 |
+
2,0.4683820903301239
|
| 4 |
+
3,0.3241419792175293
|
| 5 |
+
4,0.24197718501091003
|
| 6 |
+
5,0.29220694303512573
|
| 7 |
+
6,0.19436803460121155
|
| 8 |
+
7,0.09668131172657013
|
| 9 |
+
8,0.08244006335735321
|
| 10 |
+
9,0.11392326653003693
|
| 11 |
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10,0.13123558461666107
|
| 12 |
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11,0.12716802954673767
|
| 13 |
+
12,0.11016254127025604
|
| 14 |
+
13,0.1108265221118927
|
| 15 |
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14,0.06764746457338333
|
| 16 |
+
15,0.08966778963804245
|
| 17 |
+
16,0.1984165906906128
|
| 18 |
+
17,0.12155874818563461
|
| 19 |
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18,0.07818058878183365
|
| 20 |
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19,0.05533997341990471
|
| 21 |
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20,0.042471401393413544
|
| 22 |
+
21,0.0365070179104805
|
| 23 |
+
22,0.018686367198824883
|
| 24 |
+
23,0.009167143143713474
|
| 25 |
+
24,0.004488933831453323
|
| 26 |
+
25,0.005246465560048819
|
| 27 |
+
26,0.0034387907944619656
|
| 28 |
+
27,0.0022085290402173996
|
| 29 |
+
28,0.006266098469495773
|
| 30 |
+
29,0.008525695651769638
|
| 31 |
+
30,0.002915620803833008
|
| 32 |
+
31,0.004936204757541418
|
| 33 |
+
32,0.0033519347198307514
|
| 34 |
+
33,0.0034256051294505596
|
| 35 |
+
34,0.0018130912212654948
|
| 36 |
+
35,0.0034908554516732693
|
| 37 |
+
36,0.0012501556193456054
|
| 38 |
+
37,0.004713095258921385
|
| 39 |
+
38,0.0031446516513824463
|
| 40 |
+
39,0.001865896163508296
|
models/ShuffleNetV2/best_model.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c6e9fe950aecd14a5af927ebd6f367dce4a31e72028c1cdc4e3fc66989ba99fb
|
| 3 |
+
size 5237725
|
models/ShuffleNetV2/config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
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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 |
+
{
|
| 2 |
+
"num_classes": 7,
|
| 3 |
+
"hidden_size": 1024,
|
| 4 |
+
"id2label": {
|
| 5 |
+
"0": "akiec",
|
| 6 |
+
"1": "bcc",
|
| 7 |
+
"2": "bkl",
|
| 8 |
+
"3": "df",
|
| 9 |
+
"4": "mel",
|
| 10 |
+
"5": "nv",
|
| 11 |
+
"6": "vasc"
|
| 12 |
+
},
|
| 13 |
+
"label2id": {
|
| 14 |
+
"akiec": "0",
|
| 15 |
+
"bcc": "1",
|
| 16 |
+
"bkl": "2",
|
| 17 |
+
"df": "3",
|
| 18 |
+
"mel": "4",
|
| 19 |
+
"nv": "5",
|
| 20 |
+
"vasc": "6"
|
| 21 |
+
},
|
| 22 |
+
"architectures": [
|
| 23 |
+
"shufflenet_v2_x1_0"
|
| 24 |
+
]
|
| 25 |
+
}
|
models/ShuffleNetV2/logs/logs_2021-12-12-15-31-56.txt
ADDED
|
@@ -0,0 +1,945 @@
|
|
|
|
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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 |
+
==================================================
|
| 2 |
+
Model architecture:
|
| 3 |
+
==================================================
|
| 4 |
+
ShuffleNetV2(
|
| 5 |
+
(conv1): Sequential(
|
| 6 |
+
(0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
|
| 7 |
+
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 8 |
+
(2): ReLU(inplace=True)
|
| 9 |
+
)
|
| 10 |
+
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
| 11 |
+
(stage2): Sequential(
|
| 12 |
+
(0): InvertedResidual(
|
| 13 |
+
(branch1): Sequential(
|
| 14 |
+
(0): Conv2d(24, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=24, bias=False)
|
| 15 |
+
(1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 16 |
+
(2): Conv2d(24, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 17 |
+
(3): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 18 |
+
(4): ReLU(inplace=True)
|
| 19 |
+
)
|
| 20 |
+
(branch2): Sequential(
|
| 21 |
+
(0): Conv2d(24, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 22 |
+
(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 23 |
+
(2): ReLU(inplace=True)
|
| 24 |
+
(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=58, bias=False)
|
| 25 |
+
(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 26 |
+
(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 27 |
+
(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 28 |
+
(7): ReLU(inplace=True)
|
| 29 |
+
)
|
| 30 |
+
)
|
| 31 |
+
(1): InvertedResidual(
|
| 32 |
+
(branch1): Sequential()
|
| 33 |
+
(branch2): Sequential(
|
| 34 |
+
(0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 35 |
+
(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 36 |
+
(2): ReLU(inplace=True)
|
| 37 |
+
(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)
|
| 38 |
+
(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 39 |
+
(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 40 |
+
(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 41 |
+
(7): ReLU(inplace=True)
|
| 42 |
+
)
|
| 43 |
+
)
|
| 44 |
+
(2): InvertedResidual(
|
| 45 |
+
(branch1): Sequential()
|
| 46 |
+
(branch2): Sequential(
|
| 47 |
+
(0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 48 |
+
(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 49 |
+
(2): ReLU(inplace=True)
|
| 50 |
+
(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)
|
| 51 |
+
(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 52 |
+
(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 53 |
+
(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 54 |
+
(7): ReLU(inplace=True)
|
| 55 |
+
)
|
| 56 |
+
)
|
| 57 |
+
(3): InvertedResidual(
|
| 58 |
+
(branch1): Sequential()
|
| 59 |
+
(branch2): Sequential(
|
| 60 |
+
(0): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 61 |
+
(1): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 62 |
+
(2): ReLU(inplace=True)
|
| 63 |
+
(3): Conv2d(58, 58, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=58, bias=False)
|
| 64 |
+
(4): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 65 |
+
(5): Conv2d(58, 58, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 66 |
+
(6): BatchNorm2d(58, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 67 |
+
(7): ReLU(inplace=True)
|
| 68 |
+
)
|
| 69 |
+
)
|
| 70 |
+
)
|
| 71 |
+
(stage3): Sequential(
|
| 72 |
+
(0): InvertedResidual(
|
| 73 |
+
(branch1): Sequential(
|
| 74 |
+
(0): Conv2d(116, 116, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=116, bias=False)
|
| 75 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 76 |
+
(2): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 77 |
+
(3): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 78 |
+
(4): ReLU(inplace=True)
|
| 79 |
+
)
|
| 80 |
+
(branch2): Sequential(
|
| 81 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 82 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 83 |
+
(2): ReLU(inplace=True)
|
| 84 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=116, bias=False)
|
| 85 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 86 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 87 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 88 |
+
(7): ReLU(inplace=True)
|
| 89 |
+
)
|
| 90 |
+
)
|
| 91 |
+
(1): InvertedResidual(
|
| 92 |
+
(branch1): Sequential()
|
| 93 |
+
(branch2): Sequential(
|
| 94 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 95 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 96 |
+
(2): ReLU(inplace=True)
|
| 97 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 98 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 99 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 100 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 101 |
+
(7): ReLU(inplace=True)
|
| 102 |
+
)
|
| 103 |
+
)
|
| 104 |
+
(2): InvertedResidual(
|
| 105 |
+
(branch1): Sequential()
|
| 106 |
+
(branch2): Sequential(
|
| 107 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 108 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 109 |
+
(2): ReLU(inplace=True)
|
| 110 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 111 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 112 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 113 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 114 |
+
(7): ReLU(inplace=True)
|
| 115 |
+
)
|
| 116 |
+
)
|
| 117 |
+
(3): InvertedResidual(
|
| 118 |
+
(branch1): Sequential()
|
| 119 |
+
(branch2): Sequential(
|
| 120 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 121 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 122 |
+
(2): ReLU(inplace=True)
|
| 123 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 124 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 125 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 126 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 127 |
+
(7): ReLU(inplace=True)
|
| 128 |
+
)
|
| 129 |
+
)
|
| 130 |
+
(4): InvertedResidual(
|
| 131 |
+
(branch1): Sequential()
|
| 132 |
+
(branch2): Sequential(
|
| 133 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 134 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 135 |
+
(2): ReLU(inplace=True)
|
| 136 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 137 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 138 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 139 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 140 |
+
(7): ReLU(inplace=True)
|
| 141 |
+
)
|
| 142 |
+
)
|
| 143 |
+
(5): InvertedResidual(
|
| 144 |
+
(branch1): Sequential()
|
| 145 |
+
(branch2): Sequential(
|
| 146 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 147 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 148 |
+
(2): ReLU(inplace=True)
|
| 149 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 150 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 151 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 152 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 153 |
+
(7): ReLU(inplace=True)
|
| 154 |
+
)
|
| 155 |
+
)
|
| 156 |
+
(6): InvertedResidual(
|
| 157 |
+
(branch1): Sequential()
|
| 158 |
+
(branch2): Sequential(
|
| 159 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 160 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 161 |
+
(2): ReLU(inplace=True)
|
| 162 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 163 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 164 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 165 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 166 |
+
(7): ReLU(inplace=True)
|
| 167 |
+
)
|
| 168 |
+
)
|
| 169 |
+
(7): InvertedResidual(
|
| 170 |
+
(branch1): Sequential()
|
| 171 |
+
(branch2): Sequential(
|
| 172 |
+
(0): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 173 |
+
(1): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 174 |
+
(2): ReLU(inplace=True)
|
| 175 |
+
(3): Conv2d(116, 116, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=116, bias=False)
|
| 176 |
+
(4): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 177 |
+
(5): Conv2d(116, 116, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 178 |
+
(6): BatchNorm2d(116, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 179 |
+
(7): ReLU(inplace=True)
|
| 180 |
+
)
|
| 181 |
+
)
|
| 182 |
+
)
|
| 183 |
+
(stage4): Sequential(
|
| 184 |
+
(0): InvertedResidual(
|
| 185 |
+
(branch1): Sequential(
|
| 186 |
+
(0): Conv2d(232, 232, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=232, bias=False)
|
| 187 |
+
(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 188 |
+
(2): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 189 |
+
(3): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 190 |
+
(4): ReLU(inplace=True)
|
| 191 |
+
)
|
| 192 |
+
(branch2): Sequential(
|
| 193 |
+
(0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 194 |
+
(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 195 |
+
(2): ReLU(inplace=True)
|
| 196 |
+
(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=232, bias=False)
|
| 197 |
+
(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 198 |
+
(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 199 |
+
(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 200 |
+
(7): ReLU(inplace=True)
|
| 201 |
+
)
|
| 202 |
+
)
|
| 203 |
+
(1): InvertedResidual(
|
| 204 |
+
(branch1): Sequential()
|
| 205 |
+
(branch2): Sequential(
|
| 206 |
+
(0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 207 |
+
(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 208 |
+
(2): ReLU(inplace=True)
|
| 209 |
+
(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)
|
| 210 |
+
(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 211 |
+
(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 212 |
+
(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 213 |
+
(7): ReLU(inplace=True)
|
| 214 |
+
)
|
| 215 |
+
)
|
| 216 |
+
(2): InvertedResidual(
|
| 217 |
+
(branch1): Sequential()
|
| 218 |
+
(branch2): Sequential(
|
| 219 |
+
(0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 220 |
+
(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 221 |
+
(2): ReLU(inplace=True)
|
| 222 |
+
(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)
|
| 223 |
+
(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 224 |
+
(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 225 |
+
(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 226 |
+
(7): ReLU(inplace=True)
|
| 227 |
+
)
|
| 228 |
+
)
|
| 229 |
+
(3): InvertedResidual(
|
| 230 |
+
(branch1): Sequential()
|
| 231 |
+
(branch2): Sequential(
|
| 232 |
+
(0): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 233 |
+
(1): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 234 |
+
(2): ReLU(inplace=True)
|
| 235 |
+
(3): Conv2d(232, 232, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=232, bias=False)
|
| 236 |
+
(4): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 237 |
+
(5): Conv2d(232, 232, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 238 |
+
(6): BatchNorm2d(232, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 239 |
+
(7): ReLU(inplace=True)
|
| 240 |
+
)
|
| 241 |
+
)
|
| 242 |
+
)
|
| 243 |
+
(conv5): Sequential(
|
| 244 |
+
(0): Conv2d(464, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
| 245 |
+
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
| 246 |
+
(2): ReLU(inplace=True)
|
| 247 |
+
)
|
| 248 |
+
(fc): Linear(in_features=1024, out_features=7, bias=True)
|
| 249 |
+
)
|
| 250 |
+
==================================================
|
| 251 |
+
|
| 252 |
+
[Epoch 0], [Batch 0 / 40], [Loss 1.9472624063491821]
|
| 253 |
+
precision recall f1-score support
|
| 254 |
+
|
| 255 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 256 |
+
bcc 1.0000 0.1429 0.2500 14
|
| 257 |
+
bkl 0.0000 0.0000 0.0000 11
|
| 258 |
+
df 0.1500 0.2727 0.1935 11
|
| 259 |
+
mel 0.0000 0.0000 0.0000 10
|
| 260 |
+
nv 0.1667 0.7778 0.2745 9
|
| 261 |
+
vasc 0.5294 0.8182 0.6429 11
|
| 262 |
+
|
| 263 |
+
accuracy 0.2593 81
|
| 264 |
+
macro avg 0.2637 0.2874 0.1944 81
|
| 265 |
+
weighted avg 0.2836 0.2593 0.1873 81
|
| 266 |
+
|
| 267 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 268 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 25.93%
|
| 269 |
+
[Epoch 1], [Batch 0 / 40], [Loss 1.6928582191467285]
|
| 270 |
+
precision recall f1-score support
|
| 271 |
+
|
| 272 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 273 |
+
bcc 0.4118 0.5000 0.4516 14
|
| 274 |
+
bkl 0.3077 0.3636 0.3333 11
|
| 275 |
+
df 0.2381 0.4545 0.3125 11
|
| 276 |
+
mel 0.0000 0.0000 0.0000 10
|
| 277 |
+
nv 0.3846 0.5556 0.4545 9
|
| 278 |
+
vasc 0.5294 0.8182 0.6429 11
|
| 279 |
+
|
| 280 |
+
accuracy 0.3704 81
|
| 281 |
+
macro avg 0.2674 0.3846 0.3135 81
|
| 282 |
+
weighted avg 0.2599 0.3704 0.3036 81
|
| 283 |
+
|
| 284 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 285 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 37.04%
|
| 286 |
+
[Epoch 2], [Batch 0 / 40], [Loss 1.1930665969848633]
|
| 287 |
+
precision recall f1-score support
|
| 288 |
+
|
| 289 |
+
akiec 0.0000 0.0000 0.0000 15
|
| 290 |
+
bcc 0.7000 0.5000 0.5833 14
|
| 291 |
+
bkl 0.2800 0.6364 0.3889 11
|
| 292 |
+
df 0.4000 0.5455 0.4615 11
|
| 293 |
+
mel 0.0000 0.0000 0.0000 10
|
| 294 |
+
nv 0.3571 0.5556 0.4348 9
|
| 295 |
+
vasc 0.6667 0.9091 0.7692 11
|
| 296 |
+
|
| 297 |
+
accuracy 0.4321 81
|
| 298 |
+
macro avg 0.3434 0.4495 0.3768 81
|
| 299 |
+
weighted avg 0.3436 0.4321 0.3691 81
|
| 300 |
+
|
| 301 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 302 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 43.21%
|
| 303 |
+
[Epoch 3], [Batch 0 / 40], [Loss 0.8380683064460754]
|
| 304 |
+
precision recall f1-score support
|
| 305 |
+
|
| 306 |
+
akiec 0.3333 0.0667 0.1111 15
|
| 307 |
+
bcc 0.7778 0.5000 0.6087 14
|
| 308 |
+
bkl 0.2857 0.7273 0.4103 11
|
| 309 |
+
df 0.5714 0.7273 0.6400 11
|
| 310 |
+
mel 0.7143 0.5000 0.5882 10
|
| 311 |
+
nv 0.7500 0.6667 0.7059 9
|
| 312 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 313 |
+
|
| 314 |
+
accuracy 0.5556 81
|
| 315 |
+
macro avg 0.6094 0.5853 0.5620 81
|
| 316 |
+
weighted avg 0.5972 0.5556 0.5376 81
|
| 317 |
+
|
| 318 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 319 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 55.56%
|
| 320 |
+
[Epoch 4], [Batch 0 / 40], [Loss 0.7404563426971436]
|
| 321 |
+
precision recall f1-score support
|
| 322 |
+
|
| 323 |
+
akiec 0.6000 0.6000 0.6000 15
|
| 324 |
+
bcc 0.3913 0.6429 0.4865 14
|
| 325 |
+
bkl 0.6000 0.5455 0.5714 11
|
| 326 |
+
df 0.8000 0.3636 0.5000 11
|
| 327 |
+
mel 0.6667 0.4000 0.5000 10
|
| 328 |
+
nv 0.6000 0.6667 0.6316 9
|
| 329 |
+
vasc 0.7500 0.8182 0.7826 11
|
| 330 |
+
|
| 331 |
+
accuracy 0.5802 81
|
| 332 |
+
macro avg 0.6297 0.5767 0.5817 81
|
| 333 |
+
weighted avg 0.6197 0.5802 0.5789 81
|
| 334 |
+
|
| 335 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 336 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 58.02%
|
| 337 |
+
[Epoch 5], [Batch 0 / 40], [Loss 0.3303229510784149]
|
| 338 |
+
precision recall f1-score support
|
| 339 |
+
|
| 340 |
+
akiec 0.7273 0.5333 0.6154 15
|
| 341 |
+
bcc 0.5625 0.6429 0.6000 14
|
| 342 |
+
bkl 0.4118 0.6364 0.5000 11
|
| 343 |
+
df 0.7000 0.6364 0.6667 11
|
| 344 |
+
mel 0.6667 0.2000 0.3077 10
|
| 345 |
+
nv 0.5833 0.7778 0.6667 9
|
| 346 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 347 |
+
|
| 348 |
+
accuracy 0.6173 81
|
| 349 |
+
macro avg 0.6407 0.6194 0.6037 81
|
| 350 |
+
weighted avg 0.6432 0.6173 0.6062 81
|
| 351 |
+
|
| 352 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 353 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 61.73%
|
| 354 |
+
[Epoch 6], [Batch 0 / 40], [Loss 0.2551670968532562]
|
| 355 |
+
precision recall f1-score support
|
| 356 |
+
|
| 357 |
+
akiec 0.5882 0.6667 0.6250 15
|
| 358 |
+
bcc 0.7273 0.5714 0.6400 14
|
| 359 |
+
bkl 0.4444 0.3636 0.4000 11
|
| 360 |
+
df 0.6364 0.6364 0.6364 11
|
| 361 |
+
mel 0.6250 0.5000 0.5556 10
|
| 362 |
+
nv 0.6154 0.8889 0.7273 9
|
| 363 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 364 |
+
|
| 365 |
+
accuracy 0.6420 81
|
| 366 |
+
macro avg 0.6386 0.6480 0.6363 81
|
| 367 |
+
weighted avg 0.6401 0.6420 0.6346 81
|
| 368 |
+
|
| 369 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 370 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 64.20%
|
| 371 |
+
[Epoch 7], [Batch 0 / 40], [Loss 0.1678694188594818]
|
| 372 |
+
precision recall f1-score support
|
| 373 |
+
|
| 374 |
+
akiec 0.6923 0.6000 0.6429 15
|
| 375 |
+
bcc 0.7333 0.7857 0.7586 14
|
| 376 |
+
bkl 0.5000 0.4545 0.4762 11
|
| 377 |
+
df 0.7273 0.7273 0.7273 11
|
| 378 |
+
mel 0.7500 0.3000 0.4286 10
|
| 379 |
+
nv 0.5385 0.7778 0.6364 9
|
| 380 |
+
vasc 0.7333 1.0000 0.8462 11
|
| 381 |
+
|
| 382 |
+
accuracy 0.6667 81
|
| 383 |
+
macro avg 0.6678 0.6636 0.6451 81
|
| 384 |
+
weighted avg 0.6736 0.6667 0.6521 81
|
| 385 |
+
|
| 386 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 387 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 388 |
+
[Epoch 8], [Batch 0 / 40], [Loss 0.3083508014678955]
|
| 389 |
+
precision recall f1-score support
|
| 390 |
+
|
| 391 |
+
akiec 0.5789 0.7333 0.6471 15
|
| 392 |
+
bcc 1.0000 0.2857 0.4444 14
|
| 393 |
+
bkl 0.4706 0.7273 0.5714 11
|
| 394 |
+
df 0.6364 0.6364 0.6364 11
|
| 395 |
+
mel 0.5000 0.3000 0.3750 10
|
| 396 |
+
nv 0.5385 0.7778 0.6364 9
|
| 397 |
+
vasc 0.8182 0.8182 0.8182 11
|
| 398 |
+
|
| 399 |
+
accuracy 0.6049 81
|
| 400 |
+
macro avg 0.6489 0.6112 0.5898 81
|
| 401 |
+
weighted avg 0.6630 0.6049 0.5888 81
|
| 402 |
+
|
| 403 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 404 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 405 |
+
[Epoch 9], [Batch 0 / 40], [Loss 0.11866040527820587]
|
| 406 |
+
precision recall f1-score support
|
| 407 |
+
|
| 408 |
+
akiec 0.5000 0.2000 0.2857 15
|
| 409 |
+
bcc 0.6875 0.7857 0.7333 14
|
| 410 |
+
bkl 0.3810 0.7273 0.5000 11
|
| 411 |
+
df 0.5833 0.6364 0.6087 11
|
| 412 |
+
mel 0.3750 0.3000 0.3333 10
|
| 413 |
+
nv 0.6667 0.6667 0.6667 9
|
| 414 |
+
vasc 0.8889 0.7273 0.8000 11
|
| 415 |
+
|
| 416 |
+
accuracy 0.5679 81
|
| 417 |
+
macro avg 0.5832 0.5776 0.5611 81
|
| 418 |
+
weighted avg 0.5835 0.5679 0.5541 81
|
| 419 |
+
|
| 420 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 421 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 422 |
+
[Epoch 10], [Batch 0 / 40], [Loss 0.09714502841234207]
|
| 423 |
+
precision recall f1-score support
|
| 424 |
+
|
| 425 |
+
akiec 0.5000 0.5333 0.5161 15
|
| 426 |
+
bcc 0.6429 0.6429 0.6429 14
|
| 427 |
+
bkl 0.4118 0.6364 0.5000 11
|
| 428 |
+
df 0.7778 0.6364 0.7000 11
|
| 429 |
+
mel 0.4286 0.3000 0.3529 10
|
| 430 |
+
nv 0.7000 0.7778 0.7368 9
|
| 431 |
+
vasc 1.0000 0.7273 0.8421 11
|
| 432 |
+
|
| 433 |
+
accuracy 0.6049 81
|
| 434 |
+
macro avg 0.6373 0.6077 0.6130 81
|
| 435 |
+
weighted avg 0.6317 0.6049 0.6095 81
|
| 436 |
+
|
| 437 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 438 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 439 |
+
[Epoch 11], [Batch 0 / 40], [Loss 0.11753970384597778]
|
| 440 |
+
precision recall f1-score support
|
| 441 |
+
|
| 442 |
+
akiec 0.6000 0.6000 0.6000 15
|
| 443 |
+
bcc 0.7500 0.4286 0.5455 14
|
| 444 |
+
bkl 0.4211 0.7273 0.5333 11
|
| 445 |
+
df 0.5833 0.6364 0.6087 11
|
| 446 |
+
mel 0.5000 0.2000 0.2857 10
|
| 447 |
+
nv 0.7000 0.7778 0.7368 9
|
| 448 |
+
vasc 0.7692 0.9091 0.8333 11
|
| 449 |
+
|
| 450 |
+
accuracy 0.6049 81
|
| 451 |
+
macro avg 0.6177 0.6113 0.5919 81
|
| 452 |
+
weighted avg 0.6211 0.6049 0.5908 81
|
| 453 |
+
|
| 454 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 455 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 456 |
+
[Epoch 12], [Batch 0 / 40], [Loss 0.04757305607199669]
|
| 457 |
+
precision recall f1-score support
|
| 458 |
+
|
| 459 |
+
akiec 0.7143 0.3333 0.4545 15
|
| 460 |
+
bcc 0.4762 0.7143 0.5714 14
|
| 461 |
+
bkl 0.5833 0.6364 0.6087 11
|
| 462 |
+
df 0.6250 0.4545 0.5263 11
|
| 463 |
+
mel 0.4444 0.4000 0.4211 10
|
| 464 |
+
nv 0.5833 0.7778 0.6667 9
|
| 465 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 466 |
+
|
| 467 |
+
accuracy 0.5926 81
|
| 468 |
+
macro avg 0.6086 0.6036 0.5883 81
|
| 469 |
+
weighted avg 0.6115 0.5926 0.5812 81
|
| 470 |
+
|
| 471 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 472 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 473 |
+
[Epoch 13], [Batch 0 / 40], [Loss 0.05110578238964081]
|
| 474 |
+
precision recall f1-score support
|
| 475 |
+
|
| 476 |
+
akiec 0.6667 0.5333 0.5926 15
|
| 477 |
+
bcc 0.5000 0.8571 0.6316 14
|
| 478 |
+
bkl 0.6667 0.5455 0.6000 11
|
| 479 |
+
df 0.7778 0.6364 0.7000 11
|
| 480 |
+
mel 0.5000 0.5000 0.5000 10
|
| 481 |
+
nv 0.8333 0.5556 0.6667 9
|
| 482 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 483 |
+
|
| 484 |
+
accuracy 0.6543 81
|
| 485 |
+
macro avg 0.6934 0.6481 0.6571 81
|
| 486 |
+
weighted avg 0.6838 0.6543 0.6547 81
|
| 487 |
+
|
| 488 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 489 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 66.67%
|
| 490 |
+
[Epoch 14], [Batch 0 / 40], [Loss 0.05120206996798515]
|
| 491 |
+
precision recall f1-score support
|
| 492 |
+
|
| 493 |
+
akiec 0.7500 0.6000 0.6667 15
|
| 494 |
+
bcc 0.7500 0.8571 0.8000 14
|
| 495 |
+
bkl 0.5000 0.6364 0.5600 11
|
| 496 |
+
df 0.8889 0.7273 0.8000 11
|
| 497 |
+
mel 0.6000 0.6000 0.6000 10
|
| 498 |
+
nv 0.6667 0.6667 0.6667 9
|
| 499 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 500 |
+
|
| 501 |
+
accuracy 0.7160 81
|
| 502 |
+
macro avg 0.7235 0.7138 0.7146 81
|
| 503 |
+
weighted avg 0.7287 0.7160 0.7180 81
|
| 504 |
+
|
| 505 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 506 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 71.60%
|
| 507 |
+
[Epoch 15], [Batch 0 / 40], [Loss 0.021679097786545753]
|
| 508 |
+
precision recall f1-score support
|
| 509 |
+
|
| 510 |
+
akiec 0.9091 0.6667 0.7692 15
|
| 511 |
+
bcc 0.7143 0.7143 0.7143 14
|
| 512 |
+
bkl 0.5333 0.7273 0.6154 11
|
| 513 |
+
df 0.7273 0.7273 0.7273 11
|
| 514 |
+
mel 0.5556 0.5000 0.5263 10
|
| 515 |
+
nv 0.5833 0.7778 0.6667 9
|
| 516 |
+
vasc 0.8889 0.7273 0.8000 11
|
| 517 |
+
|
| 518 |
+
accuracy 0.6914 81
|
| 519 |
+
macro avg 0.7017 0.6915 0.6885 81
|
| 520 |
+
weighted avg 0.7171 0.6914 0.6959 81
|
| 521 |
+
|
| 522 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 523 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 71.60%
|
| 524 |
+
[Epoch 16], [Batch 0 / 40], [Loss 0.01891976408660412]
|
| 525 |
+
precision recall f1-score support
|
| 526 |
+
|
| 527 |
+
akiec 0.5714 0.5333 0.5517 15
|
| 528 |
+
bcc 0.8750 0.5000 0.6364 14
|
| 529 |
+
bkl 0.5000 0.4545 0.4762 11
|
| 530 |
+
df 0.5556 0.9091 0.6897 11
|
| 531 |
+
mel 0.4545 0.5000 0.4762 10
|
| 532 |
+
nv 0.6250 0.5556 0.5882 9
|
| 533 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 534 |
+
|
| 535 |
+
accuracy 0.6173 81
|
| 536 |
+
macro avg 0.6307 0.6231 0.6126 81
|
| 537 |
+
weighted avg 0.6391 0.6173 0.6127 81
|
| 538 |
+
|
| 539 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 540 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 71.60%
|
| 541 |
+
[Epoch 17], [Batch 0 / 40], [Loss 0.017482126131653786]
|
| 542 |
+
precision recall f1-score support
|
| 543 |
+
|
| 544 |
+
akiec 0.5789 0.7333 0.6471 15
|
| 545 |
+
bcc 1.0000 0.5714 0.7273 14
|
| 546 |
+
bkl 0.5714 0.7273 0.6400 11
|
| 547 |
+
df 0.6923 0.8182 0.7500 11
|
| 548 |
+
mel 0.7500 0.3000 0.4286 10
|
| 549 |
+
nv 0.7273 0.8889 0.8000 9
|
| 550 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 551 |
+
|
| 552 |
+
accuracy 0.7037 81
|
| 553 |
+
macro avg 0.7362 0.7069 0.6946 81
|
| 554 |
+
weighted avg 0.7382 0.7037 0.6942 81
|
| 555 |
+
|
| 556 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 557 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 71.60%
|
| 558 |
+
[Epoch 18], [Batch 0 / 40], [Loss 0.036634355783462524]
|
| 559 |
+
precision recall f1-score support
|
| 560 |
+
|
| 561 |
+
akiec 0.8182 0.6000 0.6923 15
|
| 562 |
+
bcc 0.9091 0.7143 0.8000 14
|
| 563 |
+
bkl 0.5294 0.8182 0.6429 11
|
| 564 |
+
df 0.6667 0.9091 0.7692 11
|
| 565 |
+
mel 0.6667 0.4000 0.5000 10
|
| 566 |
+
nv 0.7500 0.6667 0.7059 9
|
| 567 |
+
vasc 0.8462 1.0000 0.9167 11
|
| 568 |
+
|
| 569 |
+
accuracy 0.7284 81
|
| 570 |
+
macro avg 0.7409 0.7297 0.7181 81
|
| 571 |
+
weighted avg 0.7516 0.7284 0.7229 81
|
| 572 |
+
|
| 573 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 574 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 575 |
+
[Epoch 19], [Batch 0 / 40], [Loss 0.0149515550583601]
|
| 576 |
+
precision recall f1-score support
|
| 577 |
+
|
| 578 |
+
akiec 0.6111 0.7333 0.6667 15
|
| 579 |
+
bcc 0.7143 0.7143 0.7143 14
|
| 580 |
+
bkl 0.6250 0.4545 0.5263 11
|
| 581 |
+
df 0.6667 0.7273 0.6957 11
|
| 582 |
+
mel 0.6667 0.4000 0.5000 10
|
| 583 |
+
nv 0.6667 0.8889 0.7619 9
|
| 584 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 585 |
+
|
| 586 |
+
accuracy 0.6914 81
|
| 587 |
+
macro avg 0.6942 0.6896 0.6820 81
|
| 588 |
+
weighted avg 0.6919 0.6914 0.6827 81
|
| 589 |
+
|
| 590 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 591 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 592 |
+
[Epoch 20], [Batch 0 / 40], [Loss 0.04696013033390045]
|
| 593 |
+
precision recall f1-score support
|
| 594 |
+
|
| 595 |
+
akiec 0.5652 0.8667 0.6842 15
|
| 596 |
+
bcc 0.8750 0.5000 0.6364 14
|
| 597 |
+
bkl 0.3750 0.5455 0.4444 11
|
| 598 |
+
df 1.0000 0.6364 0.7778 11
|
| 599 |
+
mel 0.6667 0.6000 0.6316 10
|
| 600 |
+
nv 0.7143 0.5556 0.6250 9
|
| 601 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 602 |
+
|
| 603 |
+
accuracy 0.6667 81
|
| 604 |
+
macro avg 0.7293 0.6590 0.6726 81
|
| 605 |
+
weighted avg 0.7278 0.6667 0.6735 81
|
| 606 |
+
|
| 607 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 608 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 609 |
+
[Epoch 21], [Batch 0 / 40], [Loss 0.015502412803471088]
|
| 610 |
+
precision recall f1-score support
|
| 611 |
+
|
| 612 |
+
akiec 0.6111 0.7333 0.6667 15
|
| 613 |
+
bcc 0.8750 0.5000 0.6364 14
|
| 614 |
+
bkl 0.3571 0.4545 0.4000 11
|
| 615 |
+
df 1.0000 0.6364 0.7778 11
|
| 616 |
+
mel 0.5000 0.7000 0.5833 10
|
| 617 |
+
nv 0.7778 0.7778 0.7778 9
|
| 618 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 619 |
+
|
| 620 |
+
accuracy 0.6667 81
|
| 621 |
+
macro avg 0.7186 0.6730 0.6787 81
|
| 622 |
+
weighted avg 0.7203 0.6667 0.6753 81
|
| 623 |
+
|
| 624 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 625 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 626 |
+
[Epoch 22], [Batch 0 / 40], [Loss 0.01586947590112686]
|
| 627 |
+
precision recall f1-score support
|
| 628 |
+
|
| 629 |
+
akiec 0.7059 0.8000 0.7500 15
|
| 630 |
+
bcc 0.7857 0.7857 0.7857 14
|
| 631 |
+
bkl 0.5556 0.4545 0.5000 11
|
| 632 |
+
df 0.8000 0.7273 0.7619 11
|
| 633 |
+
mel 0.7143 0.5000 0.5882 10
|
| 634 |
+
nv 0.5385 0.7778 0.6364 9
|
| 635 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 636 |
+
|
| 637 |
+
accuracy 0.7160 81
|
| 638 |
+
macro avg 0.7156 0.7078 0.7045 81
|
| 639 |
+
weighted avg 0.7221 0.7160 0.7128 81
|
| 640 |
+
|
| 641 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 642 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 643 |
+
[Epoch 23], [Batch 0 / 40], [Loss 0.0149539140984416]
|
| 644 |
+
precision recall f1-score support
|
| 645 |
+
|
| 646 |
+
akiec 0.7500 0.8000 0.7742 15
|
| 647 |
+
bcc 0.7857 0.7857 0.7857 14
|
| 648 |
+
bkl 0.4545 0.4545 0.4545 11
|
| 649 |
+
df 0.8889 0.7273 0.8000 11
|
| 650 |
+
mel 0.6667 0.4000 0.5000 10
|
| 651 |
+
nv 0.6154 0.8889 0.7273 9
|
| 652 |
+
vasc 0.8333 0.9091 0.8696 11
|
| 653 |
+
|
| 654 |
+
accuracy 0.7160 81
|
| 655 |
+
macro avg 0.7135 0.7094 0.7016 81
|
| 656 |
+
weighted avg 0.7210 0.7160 0.7102 81
|
| 657 |
+
|
| 658 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 659 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 660 |
+
[Epoch 24], [Batch 0 / 40], [Loss 0.007495847996324301]
|
| 661 |
+
precision recall f1-score support
|
| 662 |
+
|
| 663 |
+
akiec 0.6667 0.6667 0.6667 15
|
| 664 |
+
bcc 0.6923 0.6429 0.6667 14
|
| 665 |
+
bkl 0.4615 0.5455 0.5000 11
|
| 666 |
+
df 0.8333 0.9091 0.8696 11
|
| 667 |
+
mel 0.4444 0.4000 0.4211 10
|
| 668 |
+
nv 0.8750 0.7778 0.8235 9
|
| 669 |
+
vasc 0.8182 0.8182 0.8182 11
|
| 670 |
+
|
| 671 |
+
accuracy 0.6790 81
|
| 672 |
+
macro avg 0.6845 0.6800 0.6808 81
|
| 673 |
+
weighted avg 0.6822 0.6790 0.6793 81
|
| 674 |
+
|
| 675 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 676 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 677 |
+
[Epoch 25], [Batch 0 / 40], [Loss 0.007480575703084469]
|
| 678 |
+
precision recall f1-score support
|
| 679 |
+
|
| 680 |
+
akiec 0.7692 0.6667 0.7143 15
|
| 681 |
+
bcc 0.8182 0.6429 0.7200 14
|
| 682 |
+
bkl 0.4706 0.7273 0.5714 11
|
| 683 |
+
df 0.7143 0.9091 0.8000 11
|
| 684 |
+
mel 0.6667 0.4000 0.5000 10
|
| 685 |
+
nv 0.7000 0.7778 0.7368 9
|
| 686 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 687 |
+
|
| 688 |
+
accuracy 0.7037 81
|
| 689 |
+
macro avg 0.7199 0.7060 0.7000 81
|
| 690 |
+
weighted avg 0.7271 0.7037 0.7030 81
|
| 691 |
+
|
| 692 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 693 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 694 |
+
[Epoch 26], [Batch 0 / 40], [Loss 0.009873191826045513]
|
| 695 |
+
precision recall f1-score support
|
| 696 |
+
|
| 697 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 698 |
+
bcc 0.6154 0.5714 0.5926 14
|
| 699 |
+
bkl 0.4667 0.6364 0.5385 11
|
| 700 |
+
df 0.8000 0.7273 0.7619 11
|
| 701 |
+
mel 0.6667 0.4000 0.5000 10
|
| 702 |
+
nv 0.5385 0.7778 0.6364 9
|
| 703 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 704 |
+
|
| 705 |
+
accuracy 0.6543 81
|
| 706 |
+
macro avg 0.6716 0.6568 0.6537 81
|
| 707 |
+
weighted avg 0.6750 0.6543 0.6556 81
|
| 708 |
+
|
| 709 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 710 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 711 |
+
[Epoch 27], [Batch 0 / 40], [Loss 0.019343027845025063]
|
| 712 |
+
precision recall f1-score support
|
| 713 |
+
|
| 714 |
+
akiec 0.6111 0.7333 0.6667 15
|
| 715 |
+
bcc 0.6667 0.5714 0.6154 14
|
| 716 |
+
bkl 0.4545 0.4545 0.4545 11
|
| 717 |
+
df 0.7273 0.7273 0.7273 11
|
| 718 |
+
mel 0.4545 0.5000 0.4762 10
|
| 719 |
+
nv 0.7500 0.6667 0.7059 9
|
| 720 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 721 |
+
|
| 722 |
+
accuracy 0.6420 81
|
| 723 |
+
macro avg 0.6520 0.6388 0.6433 81
|
| 724 |
+
weighted avg 0.6506 0.6420 0.6439 81
|
| 725 |
+
|
| 726 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 727 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 728 |
+
[Epoch 28], [Batch 0 / 40], [Loss 0.011302443221211433]
|
| 729 |
+
precision recall f1-score support
|
| 730 |
+
|
| 731 |
+
akiec 0.6000 0.6000 0.6000 15
|
| 732 |
+
bcc 0.6923 0.6429 0.6667 14
|
| 733 |
+
bkl 0.4615 0.5455 0.5000 11
|
| 734 |
+
df 0.7778 0.6364 0.7000 11
|
| 735 |
+
mel 0.5000 0.3000 0.3750 10
|
| 736 |
+
nv 0.5333 0.8889 0.6667 9
|
| 737 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 738 |
+
|
| 739 |
+
accuracy 0.6296 81
|
| 740 |
+
macro avg 0.6379 0.6331 0.6236 81
|
| 741 |
+
weighted avg 0.6423 0.6296 0.6261 81
|
| 742 |
+
|
| 743 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 744 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 745 |
+
[Epoch 29], [Batch 0 / 40], [Loss 0.014127632603049278]
|
| 746 |
+
precision recall f1-score support
|
| 747 |
+
|
| 748 |
+
akiec 0.6875 0.7333 0.7097 15
|
| 749 |
+
bcc 0.6250 0.7143 0.6667 14
|
| 750 |
+
bkl 0.5000 0.4545 0.4762 11
|
| 751 |
+
df 0.8182 0.8182 0.8182 11
|
| 752 |
+
mel 0.5000 0.3000 0.3750 10
|
| 753 |
+
nv 0.5833 0.7778 0.6667 9
|
| 754 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 755 |
+
|
| 756 |
+
accuracy 0.6667 81
|
| 757 |
+
macro avg 0.6591 0.6595 0.6528 81
|
| 758 |
+
weighted avg 0.6631 0.6667 0.6592 81
|
| 759 |
+
|
| 760 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 761 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 762 |
+
[Epoch 30], [Batch 0 / 40], [Loss 0.011675378307700157]
|
| 763 |
+
precision recall f1-score support
|
| 764 |
+
|
| 765 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 766 |
+
bcc 0.7692 0.7143 0.7407 14
|
| 767 |
+
bkl 0.5455 0.5455 0.5455 11
|
| 768 |
+
df 0.6667 0.7273 0.6957 11
|
| 769 |
+
mel 0.7500 0.6000 0.6667 10
|
| 770 |
+
nv 0.5833 0.7778 0.6667 9
|
| 771 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 772 |
+
|
| 773 |
+
accuracy 0.7037 81
|
| 774 |
+
macro avg 0.7054 0.7058 0.7020 81
|
| 775 |
+
weighted avg 0.7107 0.7037 0.7041 81
|
| 776 |
+
|
| 777 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 778 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 779 |
+
[Epoch 31], [Batch 0 / 40], [Loss 0.006650794763118029]
|
| 780 |
+
precision recall f1-score support
|
| 781 |
+
|
| 782 |
+
akiec 0.6923 0.6000 0.6429 15
|
| 783 |
+
bcc 0.7273 0.5714 0.6400 14
|
| 784 |
+
bkl 0.5000 0.6364 0.5600 11
|
| 785 |
+
df 0.6429 0.8182 0.7200 11
|
| 786 |
+
mel 1.0000 0.5000 0.6667 10
|
| 787 |
+
nv 0.6154 0.8889 0.7273 9
|
| 788 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 789 |
+
|
| 790 |
+
accuracy 0.6914 81
|
| 791 |
+
macro avg 0.7267 0.7034 0.6951 81
|
| 792 |
+
weighted avg 0.7244 0.6914 0.6901 81
|
| 793 |
+
|
| 794 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 795 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 796 |
+
[Epoch 32], [Batch 0 / 40], [Loss 0.03025425598025322]
|
| 797 |
+
precision recall f1-score support
|
| 798 |
+
|
| 799 |
+
akiec 0.8182 0.6000 0.6923 15
|
| 800 |
+
bcc 0.8182 0.6429 0.7200 14
|
| 801 |
+
bkl 0.4286 0.5455 0.4800 11
|
| 802 |
+
df 0.5882 0.9091 0.7143 11
|
| 803 |
+
mel 1.0000 0.4000 0.5714 10
|
| 804 |
+
nv 0.6154 0.8889 0.7273 9
|
| 805 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 806 |
+
|
| 807 |
+
accuracy 0.6914 81
|
| 808 |
+
macro avg 0.7397 0.6993 0.6878 81
|
| 809 |
+
weighted avg 0.7463 0.6914 0.6896 81
|
| 810 |
+
|
| 811 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 812 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 813 |
+
[Epoch 33], [Batch 0 / 40], [Loss 0.007708387915045023]
|
| 814 |
+
precision recall f1-score support
|
| 815 |
+
|
| 816 |
+
akiec 0.7692 0.6667 0.7143 15
|
| 817 |
+
bcc 0.6429 0.6429 0.6429 14
|
| 818 |
+
bkl 0.7500 0.5455 0.6316 11
|
| 819 |
+
df 0.5882 0.9091 0.7143 11
|
| 820 |
+
mel 0.6667 0.4000 0.5000 10
|
| 821 |
+
nv 0.6154 0.8889 0.7273 9
|
| 822 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 823 |
+
|
| 824 |
+
accuracy 0.6914 81
|
| 825 |
+
macro avg 0.7046 0.6959 0.6839 81
|
| 826 |
+
weighted avg 0.7082 0.6914 0.6851 81
|
| 827 |
+
|
| 828 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 829 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 830 |
+
[Epoch 34], [Batch 0 / 40], [Loss 0.006280002649873495]
|
| 831 |
+
precision recall f1-score support
|
| 832 |
+
|
| 833 |
+
akiec 0.7857 0.7333 0.7586 15
|
| 834 |
+
bcc 0.7143 0.7143 0.7143 14
|
| 835 |
+
bkl 0.5455 0.5455 0.5455 11
|
| 836 |
+
df 0.7143 0.9091 0.8000 11
|
| 837 |
+
mel 0.5714 0.4000 0.4706 10
|
| 838 |
+
nv 0.6364 0.7778 0.7000 9
|
| 839 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 840 |
+
|
| 841 |
+
accuracy 0.7037 81
|
| 842 |
+
macro avg 0.6954 0.6997 0.6923 81
|
| 843 |
+
weighted avg 0.7035 0.7037 0.6989 81
|
| 844 |
+
|
| 845 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 846 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 847 |
+
[Epoch 35], [Batch 0 / 40], [Loss 0.007977456785738468]
|
| 848 |
+
precision recall f1-score support
|
| 849 |
+
|
| 850 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 851 |
+
bcc 0.6667 0.5714 0.6154 14
|
| 852 |
+
bkl 0.5455 0.5455 0.5455 11
|
| 853 |
+
df 0.5625 0.8182 0.6667 11
|
| 854 |
+
mel 0.6667 0.4000 0.5000 10
|
| 855 |
+
nv 0.6667 0.8889 0.7619 9
|
| 856 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 857 |
+
|
| 858 |
+
accuracy 0.6667 81
|
| 859 |
+
macro avg 0.6746 0.6727 0.6623 81
|
| 860 |
+
weighted avg 0.6766 0.6667 0.6615 81
|
| 861 |
+
|
| 862 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 863 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 864 |
+
[Epoch 36], [Batch 0 / 40], [Loss 0.005339793860912323]
|
| 865 |
+
precision recall f1-score support
|
| 866 |
+
|
| 867 |
+
akiec 0.6111 0.7333 0.6667 15
|
| 868 |
+
bcc 0.7273 0.5714 0.6400 14
|
| 869 |
+
bkl 0.4545 0.4545 0.4545 11
|
| 870 |
+
df 0.6667 0.9091 0.7692 11
|
| 871 |
+
mel 0.7143 0.5000 0.5882 10
|
| 872 |
+
nv 0.7500 0.6667 0.7059 9
|
| 873 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 874 |
+
|
| 875 |
+
accuracy 0.6790 81
|
| 876 |
+
macro avg 0.6904 0.6777 0.6762 81
|
| 877 |
+
weighted avg 0.6861 0.6790 0.6748 81
|
| 878 |
+
|
| 879 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 880 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 72.84%
|
| 881 |
+
[Epoch 37], [Batch 0 / 40], [Loss 0.006963111460208893]
|
| 882 |
+
precision recall f1-score support
|
| 883 |
+
|
| 884 |
+
akiec 0.7647 0.8667 0.8125 15
|
| 885 |
+
bcc 0.7500 0.6429 0.6923 14
|
| 886 |
+
bkl 0.6000 0.5455 0.5714 11
|
| 887 |
+
df 0.7333 1.0000 0.8462 11
|
| 888 |
+
mel 0.7500 0.6000 0.6667 10
|
| 889 |
+
nv 0.8750 0.7778 0.8235 9
|
| 890 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 891 |
+
|
| 892 |
+
accuracy 0.7654 81
|
| 893 |
+
macro avg 0.7689 0.7631 0.7602 81
|
| 894 |
+
weighted avg 0.7656 0.7654 0.7599 81
|
| 895 |
+
|
| 896 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m
|
| 897 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 76.54%
|
| 898 |
+
[Epoch 38], [Batch 0 / 40], [Loss 0.003127896459773183]
|
| 899 |
+
precision recall f1-score support
|
| 900 |
+
|
| 901 |
+
akiec 0.7333 0.7333 0.7333 15
|
| 902 |
+
bcc 0.7692 0.7143 0.7407 14
|
| 903 |
+
bkl 0.5455 0.5455 0.5455 11
|
| 904 |
+
df 0.6667 0.9091 0.7692 11
|
| 905 |
+
mel 0.6250 0.5000 0.5556 10
|
| 906 |
+
nv 0.7500 0.6667 0.7059 9
|
| 907 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 908 |
+
|
| 909 |
+
accuracy 0.7160 81
|
| 910 |
+
macro avg 0.7141 0.7111 0.7085 81
|
| 911 |
+
weighted avg 0.7173 0.7160 0.7128 81
|
| 912 |
+
|
| 913 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 914 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 76.54%
|
| 915 |
+
[Epoch 39], [Batch 0 / 40], [Loss 0.008776417002081871]
|
| 916 |
+
precision recall f1-score support
|
| 917 |
+
|
| 918 |
+
akiec 0.6667 0.6667 0.6667 15
|
| 919 |
+
bcc 0.6923 0.6429 0.6667 14
|
| 920 |
+
bkl 0.5000 0.5455 0.5217 11
|
| 921 |
+
df 0.6923 0.8182 0.7500 11
|
| 922 |
+
mel 0.5000 0.5000 0.5000 10
|
| 923 |
+
nv 0.8571 0.6667 0.7500 9
|
| 924 |
+
vasc 0.9091 0.9091 0.9091 11
|
| 925 |
+
|
| 926 |
+
accuracy 0.6790 81
|
| 927 |
+
macro avg 0.6882 0.6784 0.6806 81
|
| 928 |
+
weighted avg 0.6855 0.6790 0.6799 81
|
| 929 |
+
|
| 930 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/last_model.pth[0m
|
| 931 |
+
[93m[shufflenet_v2_x1_0][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-31-56/best_model.pth[0m - Accuracy 76.54%
|
| 932 |
+
precision recall f1-score support
|
| 933 |
+
|
| 934 |
+
akiec 0.7143 0.6667 0.6897 15
|
| 935 |
+
bcc 0.6667 0.7143 0.6897 14
|
| 936 |
+
bkl 0.5455 0.5455 0.5455 11
|
| 937 |
+
df 0.6429 0.8182 0.7200 11
|
| 938 |
+
mel 0.5714 0.4000 0.4706 10
|
| 939 |
+
nv 0.6000 0.6667 0.6316 9
|
| 940 |
+
vasc 0.9000 0.8182 0.8571 11
|
| 941 |
+
|
| 942 |
+
accuracy 0.6667 81
|
| 943 |
+
macro avg 0.6630 0.6613 0.6577 81
|
| 944 |
+
weighted avg 0.6683 0.6667 0.6634 81
|
| 945 |
+
|
models/ShuffleNetV2/logs/test_logs_acc_2021-12-12-15-31-56.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
0,0.25925925925925924
|
| 2 |
+
1,0.37037037037037035
|
| 3 |
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2,0.43209876543209874
|
| 4 |
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3,0.5555555555555556
|
| 5 |
+
4,0.5802469135802469
|
| 6 |
+
5,0.6172839506172839
|
| 7 |
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6,0.6419753086419753
|
| 8 |
+
7,0.6666666666666666
|
| 9 |
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8,0.6049382716049383
|
| 10 |
+
9,0.5679012345679012
|
| 11 |
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10,0.6049382716049383
|
| 12 |
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11,0.6049382716049383
|
| 13 |
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12,0.5925925925925926
|
| 14 |
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13,0.654320987654321
|
| 15 |
+
14,0.7160493827160493
|
| 16 |
+
15,0.691358024691358
|
| 17 |
+
16,0.6172839506172839
|
| 18 |
+
17,0.7037037037037037
|
| 19 |
+
18,0.7283950617283951
|
| 20 |
+
19,0.691358024691358
|
| 21 |
+
20,0.6666666666666666
|
| 22 |
+
21,0.6666666666666666
|
| 23 |
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22,0.7160493827160493
|
| 24 |
+
23,0.7160493827160493
|
| 25 |
+
24,0.6790123456790124
|
| 26 |
+
25,0.7037037037037037
|
| 27 |
+
26,0.654320987654321
|
| 28 |
+
27,0.6419753086419753
|
| 29 |
+
28,0.6296296296296297
|
| 30 |
+
29,0.6666666666666666
|
| 31 |
+
30,0.7037037037037037
|
| 32 |
+
31,0.691358024691358
|
| 33 |
+
32,0.691358024691358
|
| 34 |
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33,0.691358024691358
|
| 35 |
+
34,0.7037037037037037
|
| 36 |
+
35,0.6666666666666666
|
| 37 |
+
36,0.6790123456790124
|
| 38 |
+
37,0.7654320987654321
|
| 39 |
+
38,0.7160493827160493
|
| 40 |
+
39,0.6790123456790124
|
models/ShuffleNetV2/logs/train_logs_acc_2021-12-12-15-31-56.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,0.4350828729281768
|
| 2 |
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1,0.6919889502762431
|
| 3 |
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2,0.7265193370165746
|
| 4 |
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3,0.7983425414364641
|
| 5 |
+
4,0.8466850828729282
|
| 6 |
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5,0.8964088397790055
|
| 7 |
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6,0.9350828729281768
|
| 8 |
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7,0.9461325966850829
|
| 9 |
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8,0.9488950276243094
|
| 10 |
+
9,0.9751381215469613
|
| 11 |
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10,0.9599447513812155
|
| 12 |
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11,0.9779005524861878
|
| 13 |
+
12,0.988950276243094
|
| 14 |
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13,0.9834254143646409
|
| 15 |
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14,0.9903314917127072
|
| 16 |
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15,0.9903314917127072
|
| 17 |
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16,0.9903314917127072
|
| 18 |
+
17,0.9903314917127072
|
| 19 |
+
18,0.9903314917127072
|
| 20 |
+
19,0.9930939226519337
|
| 21 |
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20,0.9875690607734806
|
| 22 |
+
21,0.994475138121547
|
| 23 |
+
22,1.0
|
| 24 |
+
23,1.0
|
| 25 |
+
24,0.9986187845303868
|
| 26 |
+
25,0.9930939226519337
|
| 27 |
+
26,0.994475138121547
|
| 28 |
+
27,0.9958563535911602
|
| 29 |
+
28,0.9903314917127072
|
| 30 |
+
29,0.9986187845303868
|
| 31 |
+
30,1.0
|
| 32 |
+
31,0.9958563535911602
|
| 33 |
+
32,0.9958563535911602
|
| 34 |
+
33,1.0
|
| 35 |
+
34,0.9986187845303868
|
| 36 |
+
35,1.0
|
| 37 |
+
36,1.0
|
| 38 |
+
37,0.9986187845303868
|
| 39 |
+
38,1.0
|
| 40 |
+
39,1.0
|
models/ShuffleNetV2/logs/train_logs_loss_2021-12-12-15-31-56.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,1.8709386587142944
|
| 2 |
+
1,1.4948400259017944
|
| 3 |
+
2,1.084162712097168
|
| 4 |
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3,0.7621438503265381
|
| 5 |
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4,0.5374770760536194
|
| 6 |
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5,0.36186251044273376
|
| 7 |
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6,0.2559089660644531
|
| 8 |
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7,0.24779772758483887
|
| 9 |
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8,0.20248866081237793
|
| 10 |
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9,0.13955894112586975
|
| 11 |
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10,0.15823791921138763
|
| 12 |
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11,0.10914398729801178
|
| 13 |
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12,0.08004051446914673
|
| 14 |
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13,0.061323583126068115
|
| 15 |
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14,0.08058642596006393
|
| 16 |
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15,0.04892304167151451
|
| 17 |
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16,0.050276126712560654
|
| 18 |
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17,0.04263928160071373
|
| 19 |
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18,0.05711854621767998
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| 20 |
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19,0.04539179429411888
|
| 21 |
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20,0.05247002840042114
|
| 22 |
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21,0.03884650021791458
|
| 23 |
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22,0.021214909851551056
|
| 24 |
+
23,0.01267595961689949
|
| 25 |
+
24,0.027989603579044342
|
| 26 |
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25,0.04051186889410019
|
| 27 |
+
26,0.05948528274893761
|
| 28 |
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27,0.026193585246801376
|
| 29 |
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28,0.038456693291664124
|
| 30 |
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29,0.01921292580664158
|
| 31 |
+
30,0.018016455695033073
|
| 32 |
+
31,0.02133302390575409
|
| 33 |
+
32,0.026172570884227753
|
| 34 |
+
33,0.013053493574261665
|
| 35 |
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34,0.009445875883102417
|
| 36 |
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35,0.009853013791143894
|
| 37 |
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36,0.009784508496522903
|
| 38 |
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37,0.011207174509763718
|
| 39 |
+
38,0.006571437232196331
|
| 40 |
+
39,0.010078544728457928
|
models/VGG16/best_model.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8e560d061ded8ceba5ebd92eb51e304bba0fdd703396b85b21709aa51febbf7f
|
| 3 |
+
size 537174661
|
models/VGG16/config.json
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"num_classes": 7,
|
| 3 |
+
"hidden_size": 4096,
|
| 4 |
+
"id2label": {
|
| 5 |
+
"0": "akiec",
|
| 6 |
+
"1": "bcc",
|
| 7 |
+
"2": "bkl",
|
| 8 |
+
"3": "df",
|
| 9 |
+
"4": "mel",
|
| 10 |
+
"5": "nv",
|
| 11 |
+
"6": "vasc"
|
| 12 |
+
},
|
| 13 |
+
"label2id": {
|
| 14 |
+
"akiec": "0",
|
| 15 |
+
"bcc": "1",
|
| 16 |
+
"bkl": "2",
|
| 17 |
+
"df": "3",
|
| 18 |
+
"mel": "4",
|
| 19 |
+
"nv": "5",
|
| 20 |
+
"vasc": "6"
|
| 21 |
+
},
|
| 22 |
+
"architectures": [
|
| 23 |
+
"vgg16"
|
| 24 |
+
]
|
| 25 |
+
}
|
models/VGG16/logs/logs_2021-12-12-15-09-07.txt
ADDED
|
@@ -0,0 +1,744 @@
|
|
|
|
|
|
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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 |
+
==================================================
|
| 2 |
+
Model architecture:
|
| 3 |
+
==================================================
|
| 4 |
+
VGG(
|
| 5 |
+
(features): Sequential(
|
| 6 |
+
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 7 |
+
(1): ReLU(inplace=True)
|
| 8 |
+
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 9 |
+
(3): ReLU(inplace=True)
|
| 10 |
+
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 11 |
+
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 12 |
+
(6): ReLU(inplace=True)
|
| 13 |
+
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 14 |
+
(8): ReLU(inplace=True)
|
| 15 |
+
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 16 |
+
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 17 |
+
(11): ReLU(inplace=True)
|
| 18 |
+
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 19 |
+
(13): ReLU(inplace=True)
|
| 20 |
+
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 21 |
+
(15): ReLU(inplace=True)
|
| 22 |
+
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 23 |
+
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 24 |
+
(18): ReLU(inplace=True)
|
| 25 |
+
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 26 |
+
(20): ReLU(inplace=True)
|
| 27 |
+
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 28 |
+
(22): ReLU(inplace=True)
|
| 29 |
+
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 30 |
+
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 31 |
+
(25): ReLU(inplace=True)
|
| 32 |
+
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 33 |
+
(27): ReLU(inplace=True)
|
| 34 |
+
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
| 35 |
+
(29): ReLU(inplace=True)
|
| 36 |
+
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
|
| 37 |
+
)
|
| 38 |
+
(avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
|
| 39 |
+
(classifier): Sequential(
|
| 40 |
+
(0): Linear(in_features=25088, out_features=4096, bias=True)
|
| 41 |
+
(1): ReLU(inplace=True)
|
| 42 |
+
(2): Dropout(p=0.5, inplace=False)
|
| 43 |
+
(3): Linear(in_features=4096, out_features=4096, bias=True)
|
| 44 |
+
(4): ReLU(inplace=True)
|
| 45 |
+
(5): Dropout(p=0.5, inplace=False)
|
| 46 |
+
(6): Linear(in_features=4096, out_features=7, bias=True)
|
| 47 |
+
)
|
| 48 |
+
)
|
| 49 |
+
==================================================
|
| 50 |
+
|
| 51 |
+
[Epoch 0], [Batch 0 / 40], [Loss 2.061110496520996]
|
| 52 |
+
precision recall f1-score support
|
| 53 |
+
|
| 54 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 55 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 56 |
+
bkl 0.0988 1.0000 0.1798 8
|
| 57 |
+
df 0.0000 0.0000 0.0000 17
|
| 58 |
+
mel 0.0000 0.0000 0.0000 13
|
| 59 |
+
nv 0.0000 0.0000 0.0000 9
|
| 60 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 61 |
+
|
| 62 |
+
accuracy 0.0988 81
|
| 63 |
+
macro avg 0.0141 0.1429 0.0257 81
|
| 64 |
+
weighted avg 0.0098 0.0988 0.0178 81
|
| 65 |
+
|
| 66 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 67 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 9.88%
|
| 68 |
+
[Epoch 1], [Batch 0 / 40], [Loss 1.964955449104309]
|
| 69 |
+
precision recall f1-score support
|
| 70 |
+
|
| 71 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 72 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 73 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 74 |
+
df 0.0000 0.0000 0.0000 17
|
| 75 |
+
mel 0.0000 0.0000 0.0000 13
|
| 76 |
+
nv 0.0000 0.0000 0.0000 9
|
| 77 |
+
vasc 0.1467 0.8462 0.2500 13
|
| 78 |
+
|
| 79 |
+
accuracy 0.1358 81
|
| 80 |
+
macro avg 0.0210 0.1209 0.0357 81
|
| 81 |
+
weighted avg 0.0235 0.1358 0.0401 81
|
| 82 |
+
|
| 83 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 84 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 13.58%
|
| 85 |
+
[Epoch 2], [Batch 0 / 40], [Loss 1.9537009000778198]
|
| 86 |
+
precision recall f1-score support
|
| 87 |
+
|
| 88 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 89 |
+
bcc 0.0625 0.1111 0.0800 9
|
| 90 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 91 |
+
df 0.0000 0.0000 0.0000 17
|
| 92 |
+
mel 0.1538 0.7692 0.2564 13
|
| 93 |
+
nv 0.0000 0.0000 0.0000 9
|
| 94 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 95 |
+
|
| 96 |
+
accuracy 0.1358 81
|
| 97 |
+
macro avg 0.0309 0.1258 0.0481 81
|
| 98 |
+
weighted avg 0.0316 0.1358 0.0500 81
|
| 99 |
+
|
| 100 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 101 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 13.58%
|
| 102 |
+
[Epoch 3], [Batch 0 / 40], [Loss 1.9671849012374878]
|
| 103 |
+
precision recall f1-score support
|
| 104 |
+
|
| 105 |
+
akiec 0.1481 1.0000 0.2581 12
|
| 106 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 107 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 108 |
+
df 0.0000 0.0000 0.0000 17
|
| 109 |
+
mel 0.0000 0.0000 0.0000 13
|
| 110 |
+
nv 0.0000 0.0000 0.0000 9
|
| 111 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 112 |
+
|
| 113 |
+
accuracy 0.1481 81
|
| 114 |
+
macro avg 0.0212 0.1429 0.0369 81
|
| 115 |
+
weighted avg 0.0219 0.1481 0.0382 81
|
| 116 |
+
|
| 117 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 118 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 119 |
+
[Epoch 4], [Batch 0 / 40], [Loss 1.9357523918151855]
|
| 120 |
+
precision recall f1-score support
|
| 121 |
+
|
| 122 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 123 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 124 |
+
bkl 0.0769 0.5000 0.1333 8
|
| 125 |
+
df 0.2414 0.4118 0.3043 17
|
| 126 |
+
mel 0.0000 0.0000 0.0000 13
|
| 127 |
+
nv 0.0000 0.0000 0.0000 9
|
| 128 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 129 |
+
|
| 130 |
+
accuracy 0.1358 81
|
| 131 |
+
macro avg 0.0455 0.1303 0.0625 81
|
| 132 |
+
weighted avg 0.0583 0.1358 0.0770 81
|
| 133 |
+
|
| 134 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 135 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 136 |
+
[Epoch 5], [Batch 0 / 40], [Loss 1.9422740936279297]
|
| 137 |
+
precision recall f1-score support
|
| 138 |
+
|
| 139 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 140 |
+
bcc 0.1184 1.0000 0.2118 9
|
| 141 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 142 |
+
df 0.0000 0.0000 0.0000 17
|
| 143 |
+
mel 0.0000 0.0000 0.0000 13
|
| 144 |
+
nv 0.0000 0.0000 0.0000 9
|
| 145 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 146 |
+
|
| 147 |
+
accuracy 0.1111 81
|
| 148 |
+
macro avg 0.0169 0.1429 0.0303 81
|
| 149 |
+
weighted avg 0.0132 0.1111 0.0235 81
|
| 150 |
+
|
| 151 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 152 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 153 |
+
[Epoch 6], [Batch 0 / 40], [Loss 2.1157145500183105]
|
| 154 |
+
precision recall f1-score support
|
| 155 |
+
|
| 156 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 157 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 158 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 159 |
+
df 0.0000 0.0000 0.0000 17
|
| 160 |
+
mel 0.0000 0.0000 0.0000 13
|
| 161 |
+
nv 0.1176 0.8889 0.2078 9
|
| 162 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 163 |
+
|
| 164 |
+
accuracy 0.0988 81
|
| 165 |
+
macro avg 0.0168 0.1270 0.0297 81
|
| 166 |
+
weighted avg 0.0131 0.0988 0.0231 81
|
| 167 |
+
|
| 168 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 169 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 170 |
+
[Epoch 7], [Batch 0 / 40], [Loss 1.9586039781570435]
|
| 171 |
+
precision recall f1-score support
|
| 172 |
+
|
| 173 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 174 |
+
bcc 0.1125 1.0000 0.2022 9
|
| 175 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 176 |
+
df 0.0000 0.0000 0.0000 17
|
| 177 |
+
mel 0.0000 0.0000 0.0000 13
|
| 178 |
+
nv 0.0000 0.0000 0.0000 9
|
| 179 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 180 |
+
|
| 181 |
+
accuracy 0.1111 81
|
| 182 |
+
macro avg 0.0161 0.1429 0.0289 81
|
| 183 |
+
weighted avg 0.0125 0.1111 0.0225 81
|
| 184 |
+
|
| 185 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 186 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 187 |
+
[Epoch 8], [Batch 0 / 40], [Loss 1.9570530652999878]
|
| 188 |
+
precision recall f1-score support
|
| 189 |
+
|
| 190 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 191 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 192 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 193 |
+
df 0.1429 0.1176 0.1290 17
|
| 194 |
+
mel 0.3333 0.1538 0.2105 13
|
| 195 |
+
nv 0.1250 0.7778 0.2154 9
|
| 196 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 197 |
+
|
| 198 |
+
accuracy 0.1358 81
|
| 199 |
+
macro avg 0.0859 0.1499 0.0793 81
|
| 200 |
+
weighted avg 0.0974 0.1358 0.0848 81
|
| 201 |
+
|
| 202 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 203 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 14.81%
|
| 204 |
+
[Epoch 9], [Batch 0 / 40], [Loss 1.9447191953659058]
|
| 205 |
+
precision recall f1-score support
|
| 206 |
+
|
| 207 |
+
akiec 0.3333 0.0833 0.1333 12
|
| 208 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 209 |
+
bkl 0.2000 0.2500 0.2222 8
|
| 210 |
+
df 0.7500 0.1765 0.2857 17
|
| 211 |
+
mel 0.2222 0.1538 0.1818 13
|
| 212 |
+
nv 0.1538 0.8889 0.2623 9
|
| 213 |
+
vasc 0.6667 0.1538 0.2500 13
|
| 214 |
+
|
| 215 |
+
accuracy 0.2222 81
|
| 216 |
+
macro avg 0.3323 0.2438 0.1908 81
|
| 217 |
+
weighted avg 0.3863 0.2222 0.2001 81
|
| 218 |
+
|
| 219 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 220 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 221 |
+
[Epoch 10], [Batch 0 / 40], [Loss 1.8850387334823608]
|
| 222 |
+
precision recall f1-score support
|
| 223 |
+
|
| 224 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 225 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 226 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 227 |
+
df 0.0000 0.0000 0.0000 17
|
| 228 |
+
mel 0.0000 0.0000 0.0000 13
|
| 229 |
+
nv 0.0000 0.0000 0.0000 9
|
| 230 |
+
vasc 0.1818 0.9231 0.3038 13
|
| 231 |
+
|
| 232 |
+
accuracy 0.1481 81
|
| 233 |
+
macro avg 0.0260 0.1319 0.0434 81
|
| 234 |
+
weighted avg 0.0292 0.1481 0.0488 81
|
| 235 |
+
|
| 236 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 237 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 238 |
+
[Epoch 11], [Batch 0 / 40], [Loss 1.9406187534332275]
|
| 239 |
+
precision recall f1-score support
|
| 240 |
+
|
| 241 |
+
akiec 0.1429 0.0833 0.1053 12
|
| 242 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 243 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 244 |
+
df 0.0000 0.0000 0.0000 17
|
| 245 |
+
mel 0.2105 0.3077 0.2500 13
|
| 246 |
+
nv 0.1282 0.5556 0.2083 9
|
| 247 |
+
vasc 0.3000 0.2308 0.2609 13
|
| 248 |
+
|
| 249 |
+
accuracy 0.1605 81
|
| 250 |
+
macro avg 0.1117 0.1682 0.1178 81
|
| 251 |
+
weighted avg 0.1173 0.1605 0.1207 81
|
| 252 |
+
|
| 253 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 254 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 255 |
+
[Epoch 12], [Batch 0 / 40], [Loss 1.8705354928970337]
|
| 256 |
+
precision recall f1-score support
|
| 257 |
+
|
| 258 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 259 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 260 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 261 |
+
df 0.0000 0.0000 0.0000 17
|
| 262 |
+
mel 0.0000 0.0000 0.0000 13
|
| 263 |
+
nv 0.1039 0.8889 0.1860 9
|
| 264 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 265 |
+
|
| 266 |
+
accuracy 0.0988 81
|
| 267 |
+
macro avg 0.0148 0.1270 0.0266 81
|
| 268 |
+
weighted avg 0.0115 0.0988 0.0207 81
|
| 269 |
+
|
| 270 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 271 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 272 |
+
[Epoch 13], [Batch 0 / 40], [Loss 1.9052350521087646]
|
| 273 |
+
precision recall f1-score support
|
| 274 |
+
|
| 275 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 276 |
+
bcc 0.1212 0.8889 0.2133 9
|
| 277 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 278 |
+
df 0.0000 0.0000 0.0000 17
|
| 279 |
+
mel 0.2667 0.3077 0.2857 13
|
| 280 |
+
nv 0.0000 0.0000 0.0000 9
|
| 281 |
+
vasc 0.0000 0.0000 0.0000 13
|
| 282 |
+
|
| 283 |
+
accuracy 0.1481 81
|
| 284 |
+
macro avg 0.0554 0.1709 0.0713 81
|
| 285 |
+
weighted avg 0.0563 0.1481 0.0696 81
|
| 286 |
+
|
| 287 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 288 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 289 |
+
[Epoch 14], [Batch 0 / 40], [Loss 1.9385014772415161]
|
| 290 |
+
precision recall f1-score support
|
| 291 |
+
|
| 292 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 293 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 294 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 295 |
+
df 1.0000 0.0588 0.1111 17
|
| 296 |
+
mel 0.1714 0.4615 0.2500 13
|
| 297 |
+
nv 0.0000 0.0000 0.0000 9
|
| 298 |
+
vasc 0.2286 0.6154 0.3333 13
|
| 299 |
+
|
| 300 |
+
accuracy 0.1852 81
|
| 301 |
+
macro avg 0.2000 0.1622 0.0992 81
|
| 302 |
+
weighted avg 0.2741 0.1852 0.1169 81
|
| 303 |
+
|
| 304 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 305 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 306 |
+
[Epoch 15], [Batch 0 / 40], [Loss 1.876391887664795]
|
| 307 |
+
precision recall f1-score support
|
| 308 |
+
|
| 309 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 310 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 311 |
+
bkl 0.1014 0.8750 0.1818 8
|
| 312 |
+
df 0.0000 0.0000 0.0000 17
|
| 313 |
+
mel 0.0000 0.0000 0.0000 13
|
| 314 |
+
nv 0.6250 0.5556 0.5882 9
|
| 315 |
+
vasc 1.0000 0.0769 0.1429 13
|
| 316 |
+
|
| 317 |
+
accuracy 0.1605 81
|
| 318 |
+
macro avg 0.2466 0.2154 0.1304 81
|
| 319 |
+
weighted avg 0.2400 0.1605 0.1062 81
|
| 320 |
+
|
| 321 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 322 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 323 |
+
[Epoch 16], [Batch 0 / 40], [Loss 1.8834539651870728]
|
| 324 |
+
precision recall f1-score support
|
| 325 |
+
|
| 326 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 327 |
+
bcc 0.1562 0.5556 0.2439 9
|
| 328 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 329 |
+
df 0.0000 0.0000 0.0000 17
|
| 330 |
+
mel 0.2308 0.4615 0.3077 13
|
| 331 |
+
nv 0.3125 0.5556 0.4000 9
|
| 332 |
+
vasc 0.5000 0.1538 0.2353 13
|
| 333 |
+
|
| 334 |
+
accuracy 0.2222 81
|
| 335 |
+
macro avg 0.1714 0.2466 0.1696 81
|
| 336 |
+
weighted avg 0.1694 0.2222 0.1587 81
|
| 337 |
+
|
| 338 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 339 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 340 |
+
[Epoch 17], [Batch 0 / 40], [Loss 1.8836216926574707]
|
| 341 |
+
precision recall f1-score support
|
| 342 |
+
|
| 343 |
+
akiec 0.2500 0.0833 0.1250 12
|
| 344 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 345 |
+
bkl 0.1042 0.6250 0.1786 8
|
| 346 |
+
df 0.2727 0.1765 0.2143 17
|
| 347 |
+
mel 0.5000 0.1538 0.2353 13
|
| 348 |
+
nv 0.5000 0.5556 0.5263 9
|
| 349 |
+
vasc 0.2500 0.0769 0.1176 13
|
| 350 |
+
|
| 351 |
+
accuracy 0.2099 81
|
| 352 |
+
macro avg 0.2681 0.2387 0.1996 81
|
| 353 |
+
weighted avg 0.2805 0.2099 0.1963 81
|
| 354 |
+
|
| 355 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 356 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 357 |
+
[Epoch 18], [Batch 0 / 40], [Loss 1.7908109426498413]
|
| 358 |
+
precision recall f1-score support
|
| 359 |
+
|
| 360 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 361 |
+
bcc 0.0968 0.3333 0.1500 9
|
| 362 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 363 |
+
df 0.0000 0.0000 0.0000 17
|
| 364 |
+
mel 0.2000 0.0769 0.1111 13
|
| 365 |
+
nv 0.3571 0.5556 0.4348 9
|
| 366 |
+
vasc 0.1852 0.3846 0.2500 13
|
| 367 |
+
|
| 368 |
+
accuracy 0.1728 81
|
| 369 |
+
macro avg 0.1199 0.1929 0.1351 81
|
| 370 |
+
weighted avg 0.1123 0.1728 0.1229 81
|
| 371 |
+
|
| 372 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 373 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 374 |
+
[Epoch 19], [Batch 0 / 40], [Loss 1.788246750831604]
|
| 375 |
+
precision recall f1-score support
|
| 376 |
+
|
| 377 |
+
akiec 0.0833 0.0833 0.0833 12
|
| 378 |
+
bcc 0.1395 0.6667 0.2308 9
|
| 379 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 380 |
+
df 0.2857 0.1176 0.1667 17
|
| 381 |
+
mel 0.0000 0.0000 0.0000 13
|
| 382 |
+
nv 0.5556 0.5556 0.5556 9
|
| 383 |
+
vasc 0.3333 0.1538 0.2105 13
|
| 384 |
+
|
| 385 |
+
accuracy 0.1975 81
|
| 386 |
+
macro avg 0.1996 0.2253 0.1781 81
|
| 387 |
+
weighted avg 0.2030 0.1975 0.1685 81
|
| 388 |
+
|
| 389 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 390 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 391 |
+
[Epoch 20], [Batch 0 / 40], [Loss 1.675390362739563]
|
| 392 |
+
precision recall f1-score support
|
| 393 |
+
|
| 394 |
+
akiec 0.5000 0.0833 0.1429 12
|
| 395 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 396 |
+
bkl 0.1034 0.7500 0.1818 8
|
| 397 |
+
df 0.5000 0.0588 0.1053 17
|
| 398 |
+
mel 0.0000 0.0000 0.0000 13
|
| 399 |
+
nv 0.7143 0.5556 0.6250 9
|
| 400 |
+
vasc 1.0000 0.0769 0.1429 13
|
| 401 |
+
|
| 402 |
+
accuracy 0.1728 81
|
| 403 |
+
macro avg 0.4025 0.2178 0.1711 81
|
| 404 |
+
weighted avg 0.4291 0.1728 0.1536 81
|
| 405 |
+
|
| 406 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 407 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 22.22%
|
| 408 |
+
[Epoch 21], [Batch 0 / 40], [Loss 1.7336101531982422]
|
| 409 |
+
precision recall f1-score support
|
| 410 |
+
|
| 411 |
+
akiec 0.1463 0.5000 0.2264 12
|
| 412 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 413 |
+
bkl 0.3333 0.1250 0.1818 8
|
| 414 |
+
df 0.4000 0.1176 0.1818 17
|
| 415 |
+
mel 0.1111 0.0769 0.0909 13
|
| 416 |
+
nv 0.7500 0.6667 0.7059 9
|
| 417 |
+
vasc 0.5385 0.5385 0.5385 13
|
| 418 |
+
|
| 419 |
+
accuracy 0.2840 81
|
| 420 |
+
macro avg 0.3256 0.2892 0.2750 81
|
| 421 |
+
weighted avg 0.3261 0.2840 0.2691 81
|
| 422 |
+
|
| 423 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 424 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 28.40%
|
| 425 |
+
[Epoch 22], [Batch 0 / 40], [Loss 1.791454792022705]
|
| 426 |
+
precision recall f1-score support
|
| 427 |
+
|
| 428 |
+
akiec 0.0714 0.0833 0.0769 12
|
| 429 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 430 |
+
bkl 0.1923 0.6250 0.2941 8
|
| 431 |
+
df 0.3750 0.1765 0.2400 17
|
| 432 |
+
mel 0.1667 0.1538 0.1600 13
|
| 433 |
+
nv 0.6000 0.6667 0.6316 9
|
| 434 |
+
vasc 0.4444 0.3077 0.3636 13
|
| 435 |
+
|
| 436 |
+
accuracy 0.2593 81
|
| 437 |
+
macro avg 0.2643 0.2876 0.2523 81
|
| 438 |
+
weighted avg 0.2730 0.2593 0.2450 81
|
| 439 |
+
|
| 440 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 441 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 28.40%
|
| 442 |
+
[Epoch 23], [Batch 0 / 40], [Loss 1.5891815423965454]
|
| 443 |
+
precision recall f1-score support
|
| 444 |
+
|
| 445 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 446 |
+
bcc 0.1935 0.6667 0.3000 9
|
| 447 |
+
bkl 0.0769 0.1250 0.0952 8
|
| 448 |
+
df 0.2000 0.0588 0.0909 17
|
| 449 |
+
mel 0.1429 0.0769 0.1000 13
|
| 450 |
+
nv 0.6667 0.6667 0.6667 9
|
| 451 |
+
vasc 0.5714 0.3077 0.4000 13
|
| 452 |
+
|
| 453 |
+
accuracy 0.2346 81
|
| 454 |
+
macro avg 0.2645 0.2717 0.2361 81
|
| 455 |
+
weighted avg 0.2598 0.2346 0.2161 81
|
| 456 |
+
|
| 457 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 458 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 28.40%
|
| 459 |
+
[Epoch 24], [Batch 0 / 40], [Loss 1.7806099653244019]
|
| 460 |
+
precision recall f1-score support
|
| 461 |
+
|
| 462 |
+
akiec 0.2222 0.1667 0.1905 12
|
| 463 |
+
bcc 0.1724 0.5556 0.2632 9
|
| 464 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 465 |
+
df 0.6000 0.1765 0.2727 17
|
| 466 |
+
mel 0.2308 0.2308 0.2308 13
|
| 467 |
+
nv 0.7500 0.6667 0.7059 9
|
| 468 |
+
vasc 0.4118 0.5385 0.4667 13
|
| 469 |
+
|
| 470 |
+
accuracy 0.3210 81
|
| 471 |
+
macro avg 0.3410 0.3335 0.3042 81
|
| 472 |
+
weighted avg 0.3645 0.3210 0.3051 81
|
| 473 |
+
|
| 474 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 475 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 32.10%
|
| 476 |
+
[Epoch 25], [Batch 0 / 40], [Loss 1.550750494003296]
|
| 477 |
+
precision recall f1-score support
|
| 478 |
+
|
| 479 |
+
akiec 0.3846 0.4167 0.4000 12
|
| 480 |
+
bcc 0.3333 0.2222 0.2667 9
|
| 481 |
+
bkl 0.1538 0.5000 0.2353 8
|
| 482 |
+
df 0.3333 0.0588 0.1000 17
|
| 483 |
+
mel 0.2500 0.0769 0.1176 13
|
| 484 |
+
nv 0.5455 0.6667 0.6000 9
|
| 485 |
+
vasc 0.2222 0.3077 0.2581 13
|
| 486 |
+
|
| 487 |
+
accuracy 0.2840 81
|
| 488 |
+
macro avg 0.3175 0.3213 0.2825 81
|
| 489 |
+
weighted avg 0.3156 0.2840 0.2601 81
|
| 490 |
+
|
| 491 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 492 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 32.10%
|
| 493 |
+
[Epoch 26], [Batch 0 / 40], [Loss 1.6319453716278076]
|
| 494 |
+
precision recall f1-score support
|
| 495 |
+
|
| 496 |
+
akiec 0.2222 0.1667 0.1905 12
|
| 497 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 498 |
+
bkl 0.1026 0.5000 0.1702 8
|
| 499 |
+
df 0.5000 0.1765 0.2609 17
|
| 500 |
+
mel 0.0000 0.0000 0.0000 13
|
| 501 |
+
nv 0.7143 0.5556 0.6250 9
|
| 502 |
+
vasc 0.5385 0.5385 0.5385 13
|
| 503 |
+
|
| 504 |
+
accuracy 0.2593 81
|
| 505 |
+
macro avg 0.2968 0.2767 0.2550 81
|
| 506 |
+
weighted avg 0.3138 0.2593 0.2556 81
|
| 507 |
+
|
| 508 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 509 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 32.10%
|
| 510 |
+
[Epoch 27], [Batch 0 / 40], [Loss 1.7373251914978027]
|
| 511 |
+
precision recall f1-score support
|
| 512 |
+
|
| 513 |
+
akiec 0.2143 0.2500 0.2308 12
|
| 514 |
+
bcc 0.1111 0.1111 0.1111 9
|
| 515 |
+
bkl 0.0909 0.2500 0.1333 8
|
| 516 |
+
df 0.3333 0.0588 0.1000 17
|
| 517 |
+
mel 0.0769 0.0769 0.0769 13
|
| 518 |
+
nv 0.6667 0.4444 0.5333 9
|
| 519 |
+
vasc 0.4286 0.4615 0.4444 13
|
| 520 |
+
|
| 521 |
+
accuracy 0.2222 81
|
| 522 |
+
macro avg 0.2745 0.2361 0.2328 81
|
| 523 |
+
weighted avg 0.2782 0.2222 0.2236 81
|
| 524 |
+
|
| 525 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 526 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 32.10%
|
| 527 |
+
[Epoch 28], [Batch 0 / 40], [Loss 1.520072102546692]
|
| 528 |
+
precision recall f1-score support
|
| 529 |
+
|
| 530 |
+
akiec 0.0769 0.0833 0.0800 12
|
| 531 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 532 |
+
bkl 0.1429 0.6250 0.2326 8
|
| 533 |
+
df 0.5000 0.1765 0.2609 17
|
| 534 |
+
mel 0.1429 0.0769 0.1000 13
|
| 535 |
+
nv 0.7143 0.5556 0.6250 9
|
| 536 |
+
vasc 0.5556 0.3846 0.4545 13
|
| 537 |
+
|
| 538 |
+
accuracy 0.2469 81
|
| 539 |
+
macro avg 0.3046 0.2717 0.2504 81
|
| 540 |
+
weighted avg 0.3219 0.2469 0.2480 81
|
| 541 |
+
|
| 542 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 543 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 32.10%
|
| 544 |
+
[Epoch 29], [Batch 0 / 40], [Loss 1.6450930833816528]
|
| 545 |
+
precision recall f1-score support
|
| 546 |
+
|
| 547 |
+
akiec 0.3077 0.3333 0.3200 12
|
| 548 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 549 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 550 |
+
df 0.4118 0.4118 0.4118 17
|
| 551 |
+
mel 0.3846 0.3846 0.3846 13
|
| 552 |
+
nv 0.7500 0.6667 0.7059 9
|
| 553 |
+
vasc 0.4500 0.6923 0.5455 13
|
| 554 |
+
|
| 555 |
+
accuracy 0.3827 81
|
| 556 |
+
macro avg 0.3292 0.3555 0.3382 81
|
| 557 |
+
weighted avg 0.3493 0.3827 0.3615 81
|
| 558 |
+
|
| 559 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m
|
| 560 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 561 |
+
[Epoch 30], [Batch 0 / 40], [Loss 1.5252735614776611]
|
| 562 |
+
precision recall f1-score support
|
| 563 |
+
|
| 564 |
+
akiec 0.2105 0.3333 0.2581 12
|
| 565 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 566 |
+
bkl 0.2222 0.7500 0.3429 8
|
| 567 |
+
df 0.0000 0.0000 0.0000 17
|
| 568 |
+
mel 0.1000 0.0769 0.0870 13
|
| 569 |
+
nv 0.7500 0.6667 0.7059 9
|
| 570 |
+
vasc 0.6364 0.5385 0.5833 13
|
| 571 |
+
|
| 572 |
+
accuracy 0.2963 81
|
| 573 |
+
macro avg 0.2742 0.3379 0.2824 81
|
| 574 |
+
weighted avg 0.2547 0.2963 0.2581 81
|
| 575 |
+
|
| 576 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 577 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 578 |
+
[Epoch 31], [Batch 0 / 40], [Loss 1.5033398866653442]
|
| 579 |
+
precision recall f1-score support
|
| 580 |
+
|
| 581 |
+
akiec 0.2500 0.3333 0.2857 12
|
| 582 |
+
bcc 0.1429 0.1111 0.1250 9
|
| 583 |
+
bkl 0.1579 0.3750 0.2222 8
|
| 584 |
+
df 0.4545 0.2941 0.3571 17
|
| 585 |
+
mel 0.3636 0.3077 0.3333 13
|
| 586 |
+
nv 0.6667 0.6667 0.6667 9
|
| 587 |
+
vasc 0.6250 0.3846 0.4762 13
|
| 588 |
+
|
| 589 |
+
accuracy 0.3457 81
|
| 590 |
+
macro avg 0.3801 0.3532 0.3523 81
|
| 591 |
+
weighted avg 0.3966 0.3457 0.3571 81
|
| 592 |
+
|
| 593 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 594 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 595 |
+
[Epoch 32], [Batch 0 / 40], [Loss 1.4207830429077148]
|
| 596 |
+
precision recall f1-score support
|
| 597 |
+
|
| 598 |
+
akiec 0.1333 0.1667 0.1481 12
|
| 599 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 600 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 601 |
+
df 0.3750 0.1765 0.2400 17
|
| 602 |
+
mel 0.3333 0.3846 0.3571 13
|
| 603 |
+
nv 0.6250 0.5556 0.5882 9
|
| 604 |
+
vasc 0.5000 0.5385 0.5185 13
|
| 605 |
+
|
| 606 |
+
accuracy 0.2716 81
|
| 607 |
+
macro avg 0.2810 0.2603 0.2646 81
|
| 608 |
+
weighted avg 0.3016 0.2716 0.2782 81
|
| 609 |
+
|
| 610 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 611 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 612 |
+
[Epoch 33], [Batch 0 / 40], [Loss 1.1720422506332397]
|
| 613 |
+
precision recall f1-score support
|
| 614 |
+
|
| 615 |
+
akiec 0.1875 0.2500 0.2143 12
|
| 616 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 617 |
+
bkl 0.2105 0.5000 0.2963 8
|
| 618 |
+
df 0.5000 0.1176 0.1905 17
|
| 619 |
+
mel 0.3333 0.3077 0.3200 13
|
| 620 |
+
nv 0.7143 0.5556 0.6250 9
|
| 621 |
+
vasc 0.4444 0.6154 0.5161 13
|
| 622 |
+
|
| 623 |
+
accuracy 0.3210 81
|
| 624 |
+
macro avg 0.3414 0.3352 0.3089 81
|
| 625 |
+
weighted avg 0.3577 0.3210 0.3046 81
|
| 626 |
+
|
| 627 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 628 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 629 |
+
[Epoch 34], [Batch 0 / 40], [Loss 1.3632439374923706]
|
| 630 |
+
precision recall f1-score support
|
| 631 |
+
|
| 632 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 633 |
+
bcc 0.1667 0.2222 0.1905 9
|
| 634 |
+
bkl 0.1600 0.5000 0.2424 8
|
| 635 |
+
df 0.6667 0.2353 0.3478 17
|
| 636 |
+
mel 0.1250 0.0769 0.0952 13
|
| 637 |
+
nv 0.7143 0.5556 0.6250 9
|
| 638 |
+
vasc 0.5833 0.5385 0.5600 13
|
| 639 |
+
|
| 640 |
+
accuracy 0.2840 81
|
| 641 |
+
macro avg 0.3451 0.3041 0.2944 81
|
| 642 |
+
weighted avg 0.3673 0.2840 0.2927 81
|
| 643 |
+
|
| 644 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 645 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 646 |
+
[Epoch 35], [Batch 0 / 40], [Loss 1.3819386959075928]
|
| 647 |
+
precision recall f1-score support
|
| 648 |
+
|
| 649 |
+
akiec 0.1667 0.1667 0.1667 12
|
| 650 |
+
bcc 0.1818 0.2222 0.2000 9
|
| 651 |
+
bkl 0.0526 0.1250 0.0741 8
|
| 652 |
+
df 0.5000 0.1765 0.2609 17
|
| 653 |
+
mel 0.2000 0.0769 0.1111 13
|
| 654 |
+
nv 0.3333 0.2222 0.2667 9
|
| 655 |
+
vasc 0.3182 0.5385 0.4000 13
|
| 656 |
+
|
| 657 |
+
accuracy 0.2222 81
|
| 658 |
+
macro avg 0.2504 0.2183 0.2113 81
|
| 659 |
+
weighted avg 0.2752 0.2222 0.2206 81
|
| 660 |
+
|
| 661 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 662 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 663 |
+
[Epoch 36], [Batch 0 / 40], [Loss 1.6253221035003662]
|
| 664 |
+
precision recall f1-score support
|
| 665 |
+
|
| 666 |
+
akiec 0.0000 0.0000 0.0000 12
|
| 667 |
+
bcc 0.1176 0.2222 0.1538 9
|
| 668 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 669 |
+
df 0.4444 0.2353 0.3077 17
|
| 670 |
+
mel 0.2500 0.2308 0.2400 13
|
| 671 |
+
nv 0.7500 0.6667 0.7059 9
|
| 672 |
+
vasc 0.4444 0.6154 0.5161 13
|
| 673 |
+
|
| 674 |
+
accuracy 0.2840 81
|
| 675 |
+
macro avg 0.2866 0.2815 0.2748 81
|
| 676 |
+
weighted avg 0.3011 0.2840 0.2815 81
|
| 677 |
+
|
| 678 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 679 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 680 |
+
[Epoch 37], [Batch 0 / 40], [Loss 1.507123351097107]
|
| 681 |
+
precision recall f1-score support
|
| 682 |
+
|
| 683 |
+
akiec 0.1667 0.1667 0.1667 12
|
| 684 |
+
bcc 0.2222 0.2222 0.2222 9
|
| 685 |
+
bkl 0.1364 0.3750 0.2000 8
|
| 686 |
+
df 0.5556 0.2941 0.3846 17
|
| 687 |
+
mel 0.3333 0.0769 0.1250 13
|
| 688 |
+
nv 0.5556 0.5556 0.5556 9
|
| 689 |
+
vasc 0.4706 0.6154 0.5333 13
|
| 690 |
+
|
| 691 |
+
accuracy 0.3210 81
|
| 692 |
+
macro avg 0.3486 0.3294 0.3125 81
|
| 693 |
+
weighted avg 0.3702 0.3210 0.3172 81
|
| 694 |
+
|
| 695 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 696 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 697 |
+
[Epoch 38], [Batch 0 / 40], [Loss 1.291639804840088]
|
| 698 |
+
precision recall f1-score support
|
| 699 |
+
|
| 700 |
+
akiec 0.3333 0.2500 0.2857 12
|
| 701 |
+
bcc 0.2000 0.3333 0.2500 9
|
| 702 |
+
bkl 0.1111 0.2500 0.1538 8
|
| 703 |
+
df 0.6000 0.1765 0.2727 17
|
| 704 |
+
mel 0.3333 0.2308 0.2727 13
|
| 705 |
+
nv 0.6000 0.6667 0.6316 9
|
| 706 |
+
vasc 0.4667 0.5385 0.5000 13
|
| 707 |
+
|
| 708 |
+
accuracy 0.3333 81
|
| 709 |
+
macro avg 0.3778 0.3494 0.3381 81
|
| 710 |
+
weighted avg 0.4036 0.3333 0.3367 81
|
| 711 |
+
|
| 712 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 713 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 714 |
+
[Epoch 39], [Batch 0 / 40], [Loss 1.0484699010849]
|
| 715 |
+
precision recall f1-score support
|
| 716 |
+
|
| 717 |
+
akiec 0.2500 0.2500 0.2500 12
|
| 718 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 719 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 720 |
+
df 0.5714 0.2353 0.3333 17
|
| 721 |
+
mel 0.5000 0.2308 0.3158 13
|
| 722 |
+
nv 0.5000 0.4444 0.4706 9
|
| 723 |
+
vasc 0.4000 0.6154 0.4848 13
|
| 724 |
+
|
| 725 |
+
accuracy 0.2716 81
|
| 726 |
+
macro avg 0.3173 0.2537 0.2649 81
|
| 727 |
+
weighted avg 0.3570 0.2716 0.2878 81
|
| 728 |
+
|
| 729 |
+
Model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/last_model.pth[0m
|
| 730 |
+
[93m[vgg16][0m Best model saved at: [93m./OUT_TORCHVISION/HAM10000/2021-12-12-15-09-07/best_model.pth[0m - Accuracy 38.27%
|
| 731 |
+
precision recall f1-score support
|
| 732 |
+
|
| 733 |
+
akiec 0.2500 0.2500 0.2500 12
|
| 734 |
+
bcc 0.0000 0.0000 0.0000 9
|
| 735 |
+
bkl 0.0000 0.0000 0.0000 8
|
| 736 |
+
df 0.3529 0.3529 0.3529 17
|
| 737 |
+
mel 0.2500 0.2308 0.2400 13
|
| 738 |
+
nv 0.7500 0.6667 0.7059 9
|
| 739 |
+
vasc 0.3500 0.5385 0.4242 13
|
| 740 |
+
|
| 741 |
+
accuracy 0.3086 81
|
| 742 |
+
macro avg 0.2790 0.2913 0.2819 81
|
| 743 |
+
weighted avg 0.2907 0.3086 0.2961 81
|
| 744 |
+
|
models/VGG16/logs/test_logs_acc_2021-12-12-15-09-07.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
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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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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,0.09876543209876543
|
| 2 |
+
1,0.13580246913580246
|
| 3 |
+
2,0.13580246913580246
|
| 4 |
+
3,0.14814814814814814
|
| 5 |
+
4,0.13580246913580246
|
| 6 |
+
5,0.1111111111111111
|
| 7 |
+
6,0.09876543209876543
|
| 8 |
+
7,0.1111111111111111
|
| 9 |
+
8,0.13580246913580246
|
| 10 |
+
9,0.2222222222222222
|
| 11 |
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10,0.14814814814814814
|
| 12 |
+
11,0.16049382716049382
|
| 13 |
+
12,0.09876543209876543
|
| 14 |
+
13,0.14814814814814814
|
| 15 |
+
14,0.18518518518518517
|
| 16 |
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15,0.16049382716049382
|
| 17 |
+
16,0.2222222222222222
|
| 18 |
+
17,0.20987654320987653
|
| 19 |
+
18,0.1728395061728395
|
| 20 |
+
19,0.19753086419753085
|
| 21 |
+
20,0.1728395061728395
|
| 22 |
+
21,0.2839506172839506
|
| 23 |
+
22,0.25925925925925924
|
| 24 |
+
23,0.2345679012345679
|
| 25 |
+
24,0.32098765432098764
|
| 26 |
+
25,0.2839506172839506
|
| 27 |
+
26,0.25925925925925924
|
| 28 |
+
27,0.2222222222222222
|
| 29 |
+
28,0.24691358024691357
|
| 30 |
+
29,0.38271604938271603
|
| 31 |
+
30,0.2962962962962963
|
| 32 |
+
31,0.345679012345679
|
| 33 |
+
32,0.2716049382716049
|
| 34 |
+
33,0.32098765432098764
|
| 35 |
+
34,0.2839506172839506
|
| 36 |
+
35,0.2222222222222222
|
| 37 |
+
36,0.2839506172839506
|
| 38 |
+
37,0.32098765432098764
|
| 39 |
+
38,0.3333333333333333
|
| 40 |
+
39,0.2716049382716049
|
models/VGG16/logs/train_logs_acc_2021-12-12-15-09-07.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,0.1574585635359116
|
| 2 |
+
1,0.14502762430939226
|
| 3 |
+
2,0.12845303867403315
|
| 4 |
+
3,0.13397790055248618
|
| 5 |
+
4,0.1505524861878453
|
| 6 |
+
5,0.17403314917127072
|
| 7 |
+
6,0.1574585635359116
|
| 8 |
+
7,0.13674033149171272
|
| 9 |
+
8,0.15883977900552487
|
| 10 |
+
9,0.18922651933701656
|
| 11 |
+
10,0.1643646408839779
|
| 12 |
+
11,0.2085635359116022
|
| 13 |
+
12,0.2223756906077348
|
| 14 |
+
13,0.1298342541436464
|
| 15 |
+
14,0.1671270718232044
|
| 16 |
+
15,0.2085635359116022
|
| 17 |
+
16,0.23066298342541436
|
| 18 |
+
17,0.26795580110497236
|
| 19 |
+
18,0.25828729281767954
|
| 20 |
+
19,0.27900552486187846
|
| 21 |
+
20,0.27486187845303867
|
| 22 |
+
21,0.2900552486187845
|
| 23 |
+
22,0.3328729281767956
|
| 24 |
+
23,0.2969613259668508
|
| 25 |
+
24,0.319060773480663
|
| 26 |
+
25,0.3218232044198895
|
| 27 |
+
26,0.3674033149171271
|
| 28 |
+
27,0.36049723756906077
|
| 29 |
+
28,0.36464088397790057
|
| 30 |
+
29,0.3770718232044199
|
| 31 |
+
30,0.36187845303867405
|
| 32 |
+
31,0.3825966850828729
|
| 33 |
+
32,0.393646408839779
|
| 34 |
+
33,0.4102209944751381
|
| 35 |
+
34,0.43232044198895025
|
| 36 |
+
35,0.4447513812154696
|
| 37 |
+
36,0.4488950276243094
|
| 38 |
+
37,0.48342541436464087
|
| 39 |
+
38,0.46685082872928174
|
| 40 |
+
39,0.5138121546961326
|
models/VGG16/logs/train_logs_loss_2021-12-12-15-09-07.txt
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
0,2.1033501625061035
|
| 2 |
+
1,1.9651769399642944
|
| 3 |
+
2,1.9515185356140137
|
| 4 |
+
3,1.9505870342254639
|
| 5 |
+
4,1.948213815689087
|
| 6 |
+
5,1.9330511093139648
|
| 7 |
+
6,1.9702658653259277
|
| 8 |
+
7,1.9751051664352417
|
| 9 |
+
8,1.9461535215377808
|
| 10 |
+
9,1.9179728031158447
|
| 11 |
+
10,1.9219279289245605
|
| 12 |
+
11,1.8998744487762451
|
| 13 |
+
12,1.8848860263824463
|
| 14 |
+
13,1.9483599662780762
|
| 15 |
+
14,1.9225177764892578
|
| 16 |
+
15,1.8910245895385742
|
| 17 |
+
16,1.8522323369979858
|
| 18 |
+
17,1.793922781944275
|
| 19 |
+
18,1.7878378629684448
|
| 20 |
+
19,1.7371615171432495
|
| 21 |
+
20,1.7406737804412842
|
| 22 |
+
21,1.7121121883392334
|
| 23 |
+
22,1.678593397140503
|
| 24 |
+
23,1.677600622177124
|
| 25 |
+
24,1.6530859470367432
|
| 26 |
+
25,1.6229276657104492
|
| 27 |
+
26,1.620635747909546
|
| 28 |
+
27,1.6058361530303955
|
| 29 |
+
28,1.5711954832077026
|
| 30 |
+
29,1.5438716411590576
|
| 31 |
+
30,1.5547322034835815
|
| 32 |
+
31,1.547311544418335
|
| 33 |
+
32,1.5458500385284424
|
| 34 |
+
33,1.481902837753296
|
| 35 |
+
34,1.443493366241455
|
| 36 |
+
35,1.422087550163269
|
| 37 |
+
36,1.4286044836044312
|
| 38 |
+
37,1.3534986972808838
|
| 39 |
+
38,1.3937019109725952
|
| 40 |
+
39,1.3290692567825317
|
ressources/models.csv
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
| 1 |
+
Model;Accuracy;Size
|
| 2 |
+
VGG16;38.27%;512.0 MB
|
| 3 |
+
DeiT;71.60%;327.0 MB
|
| 4 |
+
DenseNet121;77.78%;27.1 MB
|
| 5 |
+
MobileNetV2;75.31%;8.77 MB
|
| 6 |
+
ShuffleNetV2;76.54%;4.99 MB
|
ressources/thumbnail.png
ADDED
|