模型概述
模型特點
模型能力
使用案例
🚀 segformer-b0-finetuned-segments-sidewalk
該模型是 nvidia/mit-b0 在 None 數據集上的微調版本,可用於圖像分割任務。在評估集上,它取得了一系列的評估指標結果,為圖像分割任務提供了有效的解決方案。
🚀 快速開始
本模型可藉助 transformers
庫使用,以下是使用示例代碼(此處暫未給出原文檔示例,你可根據實際情況補充):
# 示例代碼
from transformers import AutoModelForImageSegmentation, AutoImageProcessor
import torch
from PIL import Image
import requests
model = AutoModelForImageSegmentation.from_pretrained("segformer-b0-finetuned-segments-sidewalk")
image_processor = AutoImageProcessor.from_pretrained("segformer-b0-finetuned-segments-sidewalk")
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation_example.png"
image = Image.open(requests.get(url, stream=True).raw)
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# 後續處理輸出結果
📚 詳細文檔
模型描述
此模型是 nvidia/mit-b0 在 None 數據集上的微調版本。它在圖像分割任務的評估集上取得了如下結果:
評估指標 | 值 |
---|---|
Loss | 0.5449 |
Mean Iou | 0.3292 |
Mean Accuracy | 0.3907 |
Overall Accuracy | 0.8555 |
Accuracy Unlabeled | nan |
Accuracy Flat-road | 0.8585 |
Accuracy Flat-sidewalk | 0.9611 |
Accuracy Flat-crosswalk | 0.7673 |
Accuracy Flat-cyclinglane | 0.8223 |
Accuracy Flat-parkingdriveway | 0.5127 |
Accuracy Flat-railtrack | nan |
Accuracy Flat-curb | 0.4937 |
Accuracy Human-person | 0.7164 |
Accuracy Human-rider | 0.0 |
Accuracy Vehicle-car | 0.9332 |
Accuracy Vehicle-truck | 0.0 |
Accuracy Vehicle-bus | nan |
Accuracy Vehicle-tramtrain | nan |
Accuracy Vehicle-motorcycle | 0.0 |
Accuracy Vehicle-bicycle | 0.3858 |
Accuracy Vehicle-caravan | 0.0 |
Accuracy Vehicle-cartrailer | 0.0 |
Accuracy Construction-building | 0.9040 |
Accuracy Construction-door | 0.0 |
Accuracy Construction-wall | 0.5848 |
Accuracy Construction-fenceguardrail | 0.4417 |
Accuracy Construction-bridge | 0.0 |
Accuracy Construction-tunnel | nan |
Accuracy Construction-stairs | 0.0 |
Accuracy Object-pole | 0.3156 |
Accuracy Object-trafficsign | 0.0 |
Accuracy Object-trafficlight | 0.0 |
Accuracy Nature-vegetation | 0.9413 |
Accuracy Nature-terrain | 0.8456 |
Accuracy Sky | 0.9600 |
Accuracy Void-ground | 0.0 |
Accuracy Void-dynamic | 0.0 |
Accuracy Void-static | 0.2780 |
Accuracy Void-unclear | 0.0 |
Iou Unlabeled | nan |
Iou Flat-road | 0.7447 |
Iou Flat-sidewalk | 0.8755 |
Iou Flat-crosswalk | 0.6244 |
Iou Flat-cyclinglane | 0.7325 |
Iou Flat-parkingdriveway | 0.3997 |
Iou Flat-railtrack | nan |
Iou Flat-curb | 0.3974 |
Iou Human-person | 0.4985 |
Iou Human-rider | 0.0 |
Iou Vehicle-car | 0.7798 |
Iou Vehicle-truck | 0.0 |
Iou Vehicle-bus | nan |
Iou Vehicle-tramtrain | nan |
Iou Vehicle-motorcycle | 0.0 |
Iou Vehicle-bicycle | 0.2904 |
Iou Vehicle-caravan | 0.0 |
Iou Vehicle-cartrailer | 0.0 |
Iou Construction-building | 0.7233 |
Iou Construction-door | 0.0 |
Iou Construction-wall | 0.4555 |
Iou Construction-fenceguardrail | 0.3734 |
Iou Construction-bridge | 0.0 |
Iou Construction-tunnel | nan |
Iou Construction-stairs | 0.0 |
Iou Object-pole | 0.2484 |
Iou Object-trafficsign | 0.0 |
Iou Object-trafficlight | 0.0 |
Iou Nature-vegetation | 0.8451 |
Iou Nature-terrain | 0.7346 |
Iou Sky | 0.9161 |
Iou Void-ground | 0.0 |
Iou Void-dynamic | 0.0 |
Iou Void-static | 0.2359 |
Iou Void-unclear | 0.0 |
訓練和評估數據
更多信息待補充。
訓練過程
訓練超參數
訓練過程中使用了以下超參數:
超參數 | 值 |
---|---|
learning_rate | 6e - 05 |
train_batch_size | 8 |
eval_batch_size | 8 |
seed | 42 |
optimizer | Adam with betas=(0.9, 0.999) and epsilon=1e - 08 |
lr_scheduler_type | linear |
num_epochs | 20 |
訓練結果
| 訓練損失 | 輪數 | 步數 | 驗證損失 | 平均交併比 | 平均準確率 | 總體準確率 | 未標記準確率 | 平坦道路準確率 | 平坦人行道準確率 | 平坦人行橫道準確率 | 平坦自行車道準確率 | 平坦停車場車道準確率 | 平坦鐵軌準確率 | 平坦路緣準確率 | 人類 - 行人準確率 | 人類 - 騎手準確率 | 車輛 - 汽車準確率 | 車輛 - 卡車準確率 | 車輛 - 公交車準確率 | 車輛 - 電車/火車準確率 | 車輛 - 摩托車準確率 | 車輛 - 自行車準確率 | 車輛 - 大篷車準確率 | 車輛 - 汽車拖車準確率 | 建築 - 建築物準確率 | 建築 - 門準確率 | 建築 - 牆準確率 | 建築 - 圍欄/護欄準確率 | 建築 - 橋樑準確率 | 建築 - 隧道準確率 | 建築 - 樓梯準確率 | 物體 - 杆子準確率 | 物體 - 交通標誌準確率 | 物體 - 交通燈準確率 | 自然 - 植被準確率 | 自然 - 地形準確率 | 天空準確率 | 空白 - 地面準確率 | 空白 - 動態準確率 | 空白 - 靜態準確率 | 空白 - 不明確準確率 | 未標記交併比 | 平坦道路交併比 | 平坦人行道交併比 | 平坦人行橫道交併比 | 平坦自行車道交併比 | 平坦停車場車道交併比 | 平坦鐵軌交併比 | 平坦路緣交併比 | 人類 - 行人交併比 | 人類 - 騎手交併比 | 車輛 - 汽車交併比 | 車輛 - 卡車交併比 | 車輛 - 公交車交併比 | 車輛 - 電車/火車交併比 | 車輛 - 摩托車交併比 | 車輛 - 自行車交併比 | 車輛 - 大篷車交併比 | 車輛 - 汽車拖車交併比 | 建築 - 建築物交併比 | 建築 - 門交併比 | 建築 - 牆交併比 | 建築 - 圍欄/護欄交併比 | 建築 - 橋樑交併比 | 建築 - 隧道交併比 | 建築 - 樓梯交併比 | 物體 - 杆子交併比 | 物體 - 交通標誌交併比 | 物體 - 交通燈交併比 | 自然 - 植被交併比 | 自然 - 地形交併比 | 天空交併比 | 空白 - 地面交併比 | 空白 - 動態交併比 | 空白 - 靜態交併比 | 空白 - 不明確交併比 | | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | | 1.4172 | 1.87 | 200 | 1.2183 | 0.1696 | 0.2214 | 0.7509 | nan | 0.8882 | 0.9199 | 0.0 | 0.4200 | 0.0164 | nan | 0.0 | 0.0 | 0.0 | 0.8778 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8448 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9430 | 0.8044 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5435 | 0.8135 | 0.0 | 0.3743 | 0.0160 | nan | 0.0 | 0.0 | 0.0 | 0.6044 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5373 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7516 | 0.6550 | 0.7928 | 0.0 | 0.0 | 0.0 | 0.0 | | 1.1152 | 3.74 | 400 | 0.8946 | 0.1947 | 0.2441 | 0.7852 | nan | 0.8535 | 0.9471 | 0.0 | 0.7379 | 0.2453 | nan | 0.0398 | 0.0 | 0.0 | 0.8882 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8746 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.9526 | 0.8285 | 0.9448 | 0.0 | 0.0 | 0.0019 | 0.0 | nan | 0.6355 | 0.8321 | 0.0 | 0.5529 | 0.1940 | nan | 0.0392 | 0.0 | 0.0 | 0.6807 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5913 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0014 | 0.0 | 0.0 | 0.7701 | 0.6777 | 0.8567 | 0.0 | 0.0 | 0.0019 | 0.0 | | 0.6637 | 5.61 | 600 | 0.7447 | 0.2349 | 0.2841 | 0.8104 | nan | 0.8589 | 0.9451 | 0.4455 | 0.8008 | 0.3753 | nan | 0.3267 | 0.0380 | 0.0 | 0.8920 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9227 | 0.0 | 0.0938 | 0.0 | 0.0 | nan | 0.0 | 0.0167 | 0.0 | 0.0 | 0.9291 | 0.8677 | 0.9557 | 0.0 | 0.0 | 0.0562 | 0.0 | nan | 0.6768 | 0.8543 | 0.4064 | 0.6414 | 0.2914 | nan | 0.2749 | 0.0376 | 0.0 | 0.7268 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6078 | 0.0 | 0.0879 | 0.0 | 0.0 | nan | 0.0 | 0.0164 | 0.0 | 0.0 | 0.8005 | 0.6817 | 0.8918 | 0.0 | 0.0 | 0.0525 | 0.0 | | 0.673 | 7.48 | 800 | 0.6631 | 0.2691 | 0.3202 | 0.8278 | nan | 0.8387 | 0.9575 | 0.6176 | 0.7938 | 0.4208 | nan | 0.3575 | 0.3977 | 0.0 | 0.9264 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9068 | 0.0 | 0.4035 | 0.0 | 0.0 | nan | 0.0 | 0.1137 | 0.0 | 0.0 | 0.9495 | 0.8165 | 0.9453 | 0.0 | 0.0 | 0.1599 | 0.0 | nan | 0.7042 | 0.8567 | 0.5239 | 0.6600 | 0.3246 | nan | 0.3003 | 0.3212 | 0.0 | 0.7246 | 0.0 | nan | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6749 | 0.0 | 0.3113 | 0.0 | 0.0 | nan | 0.0 | 0.1038 | 0.0 | 0.0 | 0.8147 | 0.7070 | 0.9008 | 0.0 | 0.0 | 0.1445 | 0.0 | | 0.502 | 9.35 | 1000 | 0.6249 | 0.2818 | 0.3371 | 0.8345 | nan | 0.8332 | 0.9538 | 0.7158 | 0.8344 | 0.4079 | nan | 0.4420 | 0.4941 | 0.0 | 0.9275 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.9102 | 0.0 | 0.4787 | 0.0253 | 0.0 | nan | 0.0 | 0.1454 | 0.0 | 0.0 | 0.9460 | 0.8350 | 0.9588 | 0.0 | 0.0 | 0.1887 | 0.0 | nan | 0.7176 | 0.8635 | 0.6035 | 0.6519 | 0.3246 | nan | 0.3545 | 0.3720 | 0.0 | 0.7524 | 0.0 | nan | nan | 0.0 | 0.0172 | 0.0 | 0.0 | 0.6861 | 0.0 | 0.3286 | 0.0250 | 0.0 | nan | 0.0 | 0.1309 | 0.0 | 0.0 | 0.8335 | 0.7300 | 0.9037 | 0.0 | 0.0 | 0.1584 | 0.0 | | 0.9687 | 11.21 | 1200 | 0.5786 | 0.3093 | 0.3675 | 0.8471 | nan | 0.8703 | 0.9504 | 0.7382 | 0.7705 | 0.5297 | nan | 0.4804 | 0.6250 | 0.0 | 0.9168 | 0.0 | nan | nan | 0.0 | 0.1397 | 0.0 | 0.0 | 0.9228 | 0.0 | 0.5710 | 0.3183 | 0.0 | nan | 0.0 | 0.2252 | 0.0 | 0.0 | 0.9314 | 0.8840 | 0.9536 | 0.0 | 0.0 | 0.1981 | 0.0 | nan | 0.7380 | 0.8743 | 0.5825 | 0.7093 | 0.3829 | nan | 0.3743 | 0.4600 | 0.0 | 0.7727 | 0.0 | nan | nan | 0.0 | 0.1372 | 0.0 | 0.0 | 0.7008 | 0.0 | 0.4315 | 0.2847 | 0.0 | nan | 0.0 | 0.1930 | 0.0 | 0.0 | 0.8397 | 0.7121 | 0.9109 | 0.0 | 0.0 | 0.1761 | 0.0 | | 0.4681 | 13.08 | 1400 | 0.5759 | 0.3106 | 0.3665 | 0.8462 | nan | 0.8586 | 0.9572 | 0.5158 | 0.8121 | 0.5195 | nan | 0.4539 | 0.6944 | 0.0 | 0.9308 | 0.0 | nan | nan | 0.0 | 0.2759 | 0.0 | 0.0 | 0.9126 | 0.0 | 0.4927 | 0.3145 | 0.0 | nan | 0.0 | 0.2566 | 0.0 | 0.0 | 0.9396 | 0.8736 | 0.9644 | 0.0 | 0.0 | 0.2226 | 0.0 | nan | 0.7134 | 0.8742 | 0.5009 | 0.7146 | 0.4018 | nan | 0.3726 | 0.4661 | 0.0 | 0.7674 | 0.0 | nan | nan | 0.0 | 0.2501 | 0.0 | 0.0 | 0.6997 | 0.0 | 0.3933 | 0.2827 | 0.0 | nan | 0.0 | 0.2137 | 0.0 | 0.0 | 0.8377 | 0.7212 | 0.9109 | 0.0 | 0.0 | 0.1964 | 0.0 | | 0.5374 | 14.95 | 1600 | 0.5534 | 0.3232 | 0.3823 | 0.8518 | nan | 0.8607 | 0.9545 | 0.7138 | 0.8398 | 0.5129 | nan | 0.4823 | 0.7055 | 0.0 | 0.9225 | 0.0 | nan | nan | 0.0 | 0.3058 | 0.0 | 0.0 | 0.8999 | 0.0 | 0.5436 | 0.3798 | 0.0 | nan | 0.0 | 0.2878 | 0.0 | 0.0 | 0.9485 | 0.8388 | 0.9598 | 0.0 | 0.0 | 0.3145 | 0.0 | nan | 0.7336 | 0.8788 | 0.6094 | 0.7062 | 0.3966 | nan | 0.3854 | 0.4897 | 0.0 | 0.7823 | 0.0 | nan | nan | 0.0 | 0.2782 | 0.0 | 0.0 | 0.7148 | 0.0 | 0.4182 | 0.3304 | 0.0 | nan | 0.0 | 0.2324 | 0.0 | 0.0 | 0.8415 | 0.7356 | 0.9130 | 0.0 | 0.0 | 0.2491 | 0.0 | | 0.6115 | 16.82 | 1800 | 0.5528 | 0.3266 | 0.3849 | 0.8539 | nan | 0.8521 | 0.9611 | 0.6840 | 0.8291 | 0.5057 | nan | 0.5070 | 0.7165 | 0.0 | 0.9267 | 0.0 | nan | nan | 0.0 | 0.3659 | 0.0 | 0.0 | 0.9007 | 0.0 | 0.5844 | 0.3961 | 0.0 | nan | 0.0 | 0.2827 | 0.0 | 0.0 | 0.9517 | 0.8371 | 0.9602 | 0.0 | 0.0 | 0.2848 | 0.0 | nan | 0.7414 | 0.8721 | 0.6312 | 0.7245 | 0.3979 | nan | 0.3987 | 0.4932 | 0.0 | 0.7799 | 0.0 | nan | nan | 0.0 | 0.2788 | 0.0 | 0.0 | 0.7242 | 0.0 | 0.4542 | 0.3464 | 0.0 | nan | 0.0 | 0.2326 | 0.0 | 0.0 | 0.8384 | 0.7318 | 0.9141 | 0.0 | 0.0 | 0.2386 | 0.0 | | 0.4766 | 18.69 | 2000 | 0.5449 | 0.3292 | 0.3907 | 0.8555 | nan | 0.8585 | 0.9611 | 0.7673 | 0.8223 | 0.5127 | nan | 0.4937 | 0.7164 | 0.0 | 0.9332 | 0.0 | nan | nan | 0.0 | 0.3858 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.5848 | 0.4417 | 0.0 | nan | 0.0 | 0.3156 | 0.0 | 0.0 | 0.9413 | 0.8456 | 0.9600 | 0.0 | 0.0 | 0.2780 | 0.0 | nan | 0.7447 | 0.8755 | 0.6244 | 0.7325 | 0.3997 | nan | 0.3974 | 0.4985 | 0.0 | 0.7798 | 0.0 | nan | nan | 0.0 | 0.2904 | 0.0 | 0.0 | 0.7233 | 0.0 | 0.4555 | 0.3734 | 0.0 | nan | 0.0 | 0.2484 | 0.0 | 0.0 | 0.8451 | 0.7346 | 0.9161 | 0.0 | 0.0 | 0.2359 | 0.0 |
框架版本
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
📄 許可證
本模型使用其他許可證,具體詳情待補充。











