言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
A DINOv2 based image feature model. Supervisely pre-trained on animal re-identification datasets. Original model: timm/vit_large_patch14_dinov2.lvd142m - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 304.4 - GMACs: 507.1 - Activations (M): 1058.8 - Image size: 518 x 518
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A Swin-L image feature model. Supervisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 228.6 - Image size: 224 x 224 - Architecture: swin_large_patch4_window7_224 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
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A Swin-L image feature model. Superwisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 228.8 - Image size: 384 x 384 - Architecture: swin_large_patch4_window12_384 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
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A Swin-S image feature model. Supervisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 49.6 - Image size: 224 x 224 - Architecture: swin_small_patch4_window7_224 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
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A Swin-T image feature model. Superwisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Params (M): 28.3 - Image size: 224 x 224 - Architecture: swin_tiny_patch4_window7_224 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows - DINOv2: Learning Robust Visual Features without Supervision - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
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A EfficientNetB3 based image feature model. Supervisely pre-trained on animal re-identification datasets. - **Model Type:** Animal re-identification / feature backbone - **Model Stats:** - Architecture: EfficientNetB3 - Params (M): 12.2 - GMACs: 1.6 - Activations (M): 21.5 - Image size: 288 x 288 - **Paper:** WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification - **Related Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
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--- tags: - image-classification - ecology - fungi - FGVC library_name: DanishFungi license: cc-by-nc-4.0 ---
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
--- base_model: motheecreator/vit-Facial-Expression-Recognition tags: - generated_from_trainer - facial-expression-recognition - image-classification - vision-transformer metrics: - accuracy model-index: - name: vit-Facial-Expression-Recognitio results: [] --- This model is based on a Vision Transformer (ViT) architecture fine-tuned for facial expression recognition. The associated research, *Facial Expression Recognition with CNNs and Vision Transformers: Full Images, Landmarks, and a Hybrid Method on FER-2013, FER+ and RAF-DB*, investigates different approaches to facial expression recognition using Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).
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The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species. The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include: These metrics indicate a high level of performance, making the model reliable for practical applications.
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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
