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SegFormer model fine-tuned on ATR dataset for clothes segmentation but can also be used for human segmentation. The dataset on hugging face is called "mattmdjaga/human_parsing_dataset". **NEW** - **Training code**. Right now it only contains the pure code with some comments, but soon I'll add a colab notebook version and a blog post with it to make it more friendly. Labels: 0: "Background", 1: "Hat", 2: "Hair", 3: "Sunglasses", 4: "Upper-clothes", 5: "Skirt", 6: "Pants", 7: "Dress", 8: "Belt", 9: "Left-shoe", 10: "Right-shoe", 11: "Face", 12: "Left-leg", 13: "Right-leg", 14: "Left-arm", 15: "Right-arm", 16: "Bag", 17: "Scarf"

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This model is a fine-tuned version of nvidia/mit-b3 on the sayeed99/fashion_segmentation dataset using original image sizes without resizing. Labels : {"0":"Unlabelled", "1": "shirt, blouse", "2": "top, t-shirt, sweatshirt", "3": "sweater", "4": "cardigan", "5": "jacket", "6": "vest", "7": "pants", "8": "shorts", "9": "skirt", "10": "coat", "11": "dress", "12": "jumpsuit", "13": "cape", "14": "glasses", "15": "hat", "16": "headband, head covering, hair accessory", "17": "tie", "18": "glove", "19": "watch", "20": "belt", "21": "leg warmer", "22": "tights, stockings", "23": "sock", "24": "shoe", "25": "bag, wallet", "26": "scarf", "27": "umbrella", "28": "hood", "29": "collar", "30": "lapel", "31": "epaulette", "32": "sleeve", "33": "pocket", "34": "neckline", "35": "buckle", "36": "zipper", "37": "applique", "38": "bead", "39": "bow", "40": "flower", "41": "fringe", "42": "ribbon", "43": "rivet", "44": "ruffle", "45": "sequin", "46": "tassel"} ```bibtex @article{DBLP:journals/corr/abs-2105-15203, author = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo}, title = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, journal = {CoRR}, volume = {abs/2105.15203}, year = {2021}, url = {https://arxiv.org/abs/2105.15203}, eprinttype = {arXiv}, eprint = {2105.15203}, timestamp = {Wed, 02 Jun 2021 11:46:42 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2105-15203.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

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COST EXPLORER

好模型,也要算好成本。

费用分析

调整月度 Token 用量,直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照;不计缓存优惠、阶梯价、推理额外输出与税费,请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较?+

先区分输入、输出和缓存价格,再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费,不能直接与 Token 单价比较。

开放权重,就代表可以免费商用吗?+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一,就是最适合我的模型吗?+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本,再选择模型。

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