What can mobilenetv3_small_100.lamb_in1k do?

lightweight-image-classification-inference, transfer-learning-backbone-extraction, quantization-and-model-compression, batch-inference-with-preprocessing-pipeline, model-export-and-format-conversion, ensemble-inference-with-multiple-models

mobilenetv3_small_100.lamb_in1k

Q: What is mobilenetv3_small_100.lamb_in1k?

timm/mobilenetv3_small_100.lamb_in1k — a image-classification model on HuggingFace with 1,74,99,725 downloads

ModelFree

image-classification model by undefined. 1,74,99,725 downloads.

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

lightweight-image-classification-inference

Medium confidence

Performs ImageNet-1k classification on images using MobileNetV3-Small architecture, a depthwise-separable convolution-based model optimized for mobile and edge devices. The model uses inverted residual blocks with squeeze-and-excitation modules to achieve 75.7% top-1 accuracy while maintaining ~2.5M parameters and ~56M FLOPs. Inference runs efficiently on CPU, mobile devices, and edge hardware through PyTorch's optimized operators and can be quantized further for deployment.

Solves for

I need to classify images into 1000 ImageNet categories with minimal latency and memory footprint for mobile or edge deploymentI want to use a pre-trained backbone for transfer learning on custom image classification tasks without fine-tuning from scratchI need to run image classification inference on resource-constrained devices like smartphones, IoT cameras, or embedded systemsI want to benchmark a lightweight CNN architecture against heavier models like ResNet or Vision Transformers for production deployment

Best for

mobile app developers building on-device vision features

edge AI engineers deploying models to IoT/embedded hardware

ML practitioners prototyping lightweight classification pipelines

Requires

PyTorch 1.9+ or TensorFlow 2.4+ (model available in both frameworks via timm)

Python 3.7+

PIL/Pillow for image loading and preprocessing

Limitations

Fixed to 1000 ImageNet classes — requires fine-tuning or head replacement for custom classification tasks

Input images must be resized to 224×224 pixels; aspect ratio distortion may impact accuracy on non-square inputs

Trained on ImageNet-1k only — may have poor generalization to domain-specific images (medical, satellite, etc.) without adaptation

What makes it unique

Uses inverted residual blocks with squeeze-and-excitation (SE) modules and non-linear bottleneck layers, achieving state-of-the-art accuracy-to-parameter ratio (75.7% top-1 on ImageNet with 2.5M params). Trained with LAMB optimizer on ImageNet-1k, enabling faster convergence than SGD-based alternatives. Distributed via timm's unified model registry with automatic weight downloading and format conversion (PyTorch → ONNX → TensorRT).

vs alternatives

Outperforms EfficientNet-B0 and SqueezeNet on latency-accuracy tradeoff for mobile inference; 3-5× faster than ResNet-50 on ARM devices while maintaining competitive accuracy for general-purpose classification.

transfer-learning-backbone-extraction

Medium confidence

Extracts intermediate feature representations from MobileNetV3-Small by removing the final classification head and exposing layer outputs at multiple depths. The model's hierarchical feature pyramid (from early low-level features to semantic high-level features) can be used as a frozen or fine-tuned backbone for downstream tasks like object detection, semantic segmentation, or custom classification. Supports layer-wise learning rate scheduling and selective unfreezing for efficient transfer learning.

Solves for

I want to use MobileNetV3-Small as a feature extractor for object detection or instance segmentation on custom datasetsI need to adapt the model to a new classification task with limited labeled data by fine-tuning only the top layersI want to extract embeddings from intermediate layers for similarity search or clustering on custom image datasetsI need to build a multi-task learning model that shares MobileNetV3-Small features across detection, segmentation, and classification heads

Best for

computer vision practitioners building custom detection/segmentation pipelines

researchers experimenting with transfer learning on domain-specific image datasets

teams deploying multi-task models that share a lightweight backbone

Requires

PyTorch 1.9+ with torch.nn.Module subclassing support

timm library (pip install timm) for model loading and layer access

Custom training loop or framework (PyTorch Lightning, Hugging Face Transformers) for fine-tuning

Limitations

Feature maps from early layers are spatially large (56×56 for layer 1) — memory-intensive for high-resolution inputs or large batches

ImageNet pre-training bias may not transfer well to non-natural images (medical, thermal, synthetic); domain adaptation required

Removing the classification head requires manual architecture modification — no built-in API for automatic head swapping

What makes it unique

MobileNetV3-Small's inverted residual architecture with SE modules creates a feature pyramid with strong semantic information at shallow depths, enabling effective transfer learning with minimal fine-tuning. The model's depthwise-separable convolutions reduce parameter count in the backbone, leaving capacity for task-specific heads. timm's model registry provides automatic layer naming and access patterns (e.g., model.features[i] for block i, model.global_pool for pooling layer).

vs alternatives

Requires 10-20× fewer parameters to fine-tune than ResNet-50 backbones while maintaining competitive transfer learning accuracy; enables faster adaptation on edge devices and lower memory footprint during training.

quantization-and-model-compression

Medium confidence

Supports post-training quantization (PTQ) and quantization-aware training (QAT) to reduce model size and inference latency by 4-8× through int8 or int4 weight/activation quantization. The model's depthwise-separable convolutions and small parameter count (2.5M) make it amenable to aggressive quantization with minimal accuracy loss (<1% top-1 drop). Compatible with ONNX quantization tools, TensorRT, and mobile frameworks (TFLite, CoreML) for deployment on resource-constrained devices.

Solves for

I need to compress MobileNetV3-Small to <10MB for on-device deployment on smartphones or IoT devicesI want to reduce inference latency by 4-8× through int8 quantization while maintaining >74% ImageNet accuracyI need to convert the model to TFLite or CoreML format with quantization for iOS/Android deploymentI want to benchmark quantization impact on accuracy and latency for production deployment decisions

Best for

mobile app developers deploying models to iOS/Android with strict size constraints (<20MB)

edge AI engineers optimizing inference on ARM processors or specialized accelerators (NPU, TPU)

teams deploying models to bandwidth-constrained environments (IoT, embedded systems)

Requires

PyTorch 1.9+ with torch.quantization module or TensorFlow 2.4+ with tf.lite.TFLiteConverter

Calibration dataset (100-1000 representative images from target domain) for accurate quantization

ONNX Runtime or TensorRT for optimized quantized inference (optional but recommended)

Limitations

Post-training quantization (PTQ) may cause 1-3% accuracy drop on ImageNet; QAT requires retraining with calibration data

int4 quantization (extreme compression) risks >3% accuracy loss; int8 is recommended for production

Quantized models are framework-specific (ONNX int8 ≠ TFLite int8); conversion between formats may introduce precision mismatches

What makes it unique

MobileNetV3-Small's depthwise-separable convolutions and small parameter count (2.5M) enable aggressive int8 quantization with <1% accuracy loss, compared to 2-3% loss for ResNet-50. The model's architecture naturally separates spatial and channel-wise operations, reducing quantization sensitivity. timm provides pre-quantized checkpoints and integration with PyTorch's native quantization APIs (torch.quantization.quantize_dynamic, torch.quantization.prepare_qat).

vs alternatives

Achieves 4-8× compression and latency reduction with minimal accuracy loss, outperforming knowledge distillation approaches that require teacher models; compatible with all major mobile frameworks (TFLite, CoreML, ONNX) without custom conversion logic.

batch-inference-with-preprocessing-pipeline

Medium confidence

Processes multiple images in batches through an optimized preprocessing pipeline (resize, normalize, augmentation) and inference loop, leveraging PyTorch's batched operations and GPU parallelism for throughput optimization. The model integrates with timm's data loading utilities (timm.data.create_loader) to handle variable image sizes, aspect ratio preservation, and efficient batching. Supports dynamic batching for variable-size inputs and prefetching for reduced I/O bottlenecks.

Solves for

I need to classify 10,000+ images efficiently in batch mode for dataset annotation or bulk inferenceI want to optimize throughput on GPU by batching inference and minimizing data transfer overheadI need to handle variable-resolution images without resizing to 224×224 (e.g., preserve aspect ratio with padding)I want to integrate image preprocessing (augmentation, normalization) into the inference pipeline for consistency

Best for

data scientists processing large image datasets for classification or feature extraction

ML engineers building batch inference pipelines for cloud or on-premise deployment

teams optimizing throughput for inference serving (e.g., batch processing jobs)

Requires

PyTorch 1.9+ with torch.utils.data.DataLoader

timm library for data loading utilities (timm.data.create_loader, timm.data.ImageDataset)

GPU with 4GB+ VRAM for batch sizes >32 (8GB+ recommended for batch sizes 64-256)

Limitations

Batch size is limited by GPU memory; typical batch sizes are 32-256 depending on GPU (8GB-40GB VRAM)

Variable-size inputs require padding or resizing, which may distort aspect ratios and reduce accuracy by 0.5-1%

Preprocessing overhead (resize, normalize) adds ~5-10ms per batch; GPU utilization may be suboptimal for small batches (<16)

What makes it unique

timm's DataLoader integration provides automatic image resizing, normalization, and augmentation with ImageNet-1k statistics pre-configured. The model supports mixed-precision inference (FP16) via torch.cuda.amp, reducing memory footprint by 50% and latency by 20-30% on modern GPUs. Batch processing leverages PyTorch's optimized CUDA kernels for depthwise-separable convolutions, achieving near-linear scaling with batch size up to GPU memory limits.

vs alternatives

Achieves 10-20× higher throughput than single-image inference through batching and GPU parallelism; timm's preprocessing pipeline eliminates manual normalization errors and ensures consistency with training data distribution.

model-export-and-format-conversion

Medium confidence

Exports MobileNetV3-Small from PyTorch to multiple deployment formats (ONNX, TorchScript, TFLite, CoreML, NCNN) with automatic graph optimization and operator fusion. The export process includes shape inference, constant folding, and operator replacement to ensure compatibility with target runtimes. Supports both eager and traced execution modes, with optional quantization during export for reduced model size and inference latency.

Solves for

I need to export the model to ONNX format for cross-framework deployment (TensorFlow, ONNX Runtime, etc.)I want to convert the model to TFLite for Android deployment or CoreML for iOS without manual conversionI need to export to TorchScript for C++ inference in production servers without Python dependencyI want to optimize the exported model for specific hardware (ARM, x86, mobile NPU) through operator fusion and quantization

Best for

mobile app developers deploying to iOS/Android with framework-specific model formats

ML engineers building cross-platform inference services (Python, C++, JavaScript)

teams optimizing models for specific hardware accelerators (ARM, x86, mobile NPU)

Requires

PyTorch 1.9+ with torch.onnx module

ONNX and onnx-simplifier for ONNX export and optimization

TensorFlow 2.4+ and tf2onnx for TFLite conversion

Limitations

ONNX export requires opset version compatibility; older runtimes may not support all operators (e.g., depthwise convolution in opset <11)

TFLite conversion requires TensorFlow installation; PyTorch → TFLite requires intermediate ONNX conversion, introducing potential precision mismatches

TorchScript tracing captures specific input shapes; dynamic batch sizes require scripting mode, which is more complex

What makes it unique

timm provides unified export utilities (timm.models.convert_to_onnx, timm.models.convert_to_tflite) that handle operator fusion, constant folding, and shape inference automatically. The export pipeline supports quantization-aware export, enabling int8 models without separate QAT. ONNX export includes graph optimization via onnx-simplifier, reducing model size by 10-20% and improving inference speed.

vs alternatives

Automated export pipeline eliminates manual operator mapping and shape inference errors; supports more target formats (ONNX, TFLite, CoreML, NCNN, TorchScript) than single-framework converters, reducing conversion complexity.

ensemble-inference-with-multiple-models

Medium confidence

Combines predictions from multiple MobileNetV3-Small variants (different training seeds, augmentation strategies, or checkpoints) through voting or averaging to improve robustness and accuracy. The ensemble approach leverages the model's small parameter count (2.5M) to maintain reasonable memory footprint even with 3-5 models. Supports weighted averaging based on per-model confidence scores or validation accuracy.

Solves for

I want to improve ImageNet classification accuracy by 1-2% through ensemble voting without increasing model complexityI need to build a robust classification system that handles distribution shift by combining multiple model predictionsI want to reduce inference latency variance by averaging predictions from models trained with different random seedsI need to implement uncertainty quantification by analyzing prediction disagreement across ensemble members

Best for

practitioners building production classification systems requiring high accuracy and robustness

teams deploying models in safety-critical applications (medical imaging, autonomous systems)

researchers studying model uncertainty and ensemble methods

Requires

PyTorch 1.9+ with torch.nn.ModuleList for managing multiple models

Multiple model checkpoints (3-5 variants trained with different seeds or augmentation)

GPU with 8GB+ VRAM for loading 3+ models simultaneously

Limitations

Ensemble inference requires N× memory and N× latency compared to single model (e.g., 3 models = 3× slower)

Ensemble benefits plateau at 3-5 models; diminishing returns beyond 5 models for accuracy improvement

Voting/averaging assumes model diversity; identical models trained with same seed provide no benefit

What makes it unique

MobileNetV3-Small's small parameter count (2.5M) enables practical ensemble deployment with 3-5 models while maintaining <50MB total size and <200ms latency on CPU. The model's depthwise-separable architecture provides natural diversity when trained with different seeds, improving ensemble effectiveness. Custom ensemble averaging with confidence weighting can improve accuracy by 1-2% on ImageNet with minimal latency overhead.

vs alternatives

Ensemble of lightweight models (3× MobileNetV3-Small) achieves higher accuracy than single ResNet-50 with similar latency; enables practical uncertainty quantification without Bayesian approximations or dropout-based methods.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓mobile app developers building on-device vision features
✓edge AI engineers deploying models to IoT/embedded hardware
✓ML practitioners prototyping lightweight classification pipelines
✓teams optimizing inference latency and memory for real-time applications
✓computer vision practitioners building custom detection/segmentation pipelines
✓researchers experimenting with transfer learning on domain-specific image datasets
✓teams deploying multi-task models that share a lightweight backbone
✓developers optimizing inference cost by reusing pre-trained features across multiple tasks

Known Limitations

⚠Fixed to 1000 ImageNet classes — requires fine-tuning or head replacement for custom classification tasks
⚠Input images must be resized to 224×224 pixels; aspect ratio distortion may impact accuracy on non-square inputs
⚠Trained on ImageNet-1k only — may have poor generalization to domain-specific images (medical, satellite, etc.) without adaptation
⚠No built-in batch processing optimization; inference speed scales linearly with batch size on CPU
⚠Depthwise-separable convolutions have lower hardware utilization on GPUs compared to standard convolutions, making GPU speedup modest
⚠Feature maps from early layers are spatially large (56×56 for layer 1) — memory-intensive for high-resolution inputs or large batches

Requirements

PyTorch 1.9+ or TensorFlow 2.4+ (model available in both frameworks via timm)Python 3.7+PIL/Pillow for image loading and preprocessingtorchvision or equivalent for image normalization (ImageNet mean/std: [0.485, 0.456, 0.406] / [0.229, 0.224, 0.225])Optional: ONNX Runtime or TensorRT for optimized inference on edge devicesPyTorch 1.9+ with torch.nn.Module subclassing supporttimm library (pip install timm) for model loading and layer accessCustom training loop or framework (PyTorch Lightning, Hugging Face Transformers) for fine-tuning

Input / Output

Accepts: image (JPEG, PNG, WebP), tensor (torch.Tensor or tf.Tensor with shape [batch, 3, 224, 224] in RGB format), image (JPEG, PNG, WebP, resized to 224×224), tensor (torch.Tensor with shape [batch, 3, 224, 224]), image (JPEG, PNG, resized to 224×224), tensor (torch.Tensor or tf.Tensor with shape [batch, 3, 224, 224]), image directory (JPEG, PNG, WebP files), image list (CSV or JSON with file paths), tensor batch (torch.Tensor with shape [batch, 3, 224, 224]), PyTorch model (torch.nn.Module), model checkpoint (safetensors or .pth file)

Produces: logits (tensor of shape [batch, 1000]), class probabilities (softmax-normalized, shape [batch, 1000]), top-k predictions (class indices and confidence scores), feature maps (intermediate layer outputs, shape varies by layer: [batch, 16, 112, 112] to [batch, 576, 7, 7]), embeddings (flattened features from penultimate layer, shape [batch, 1024]), logits (if classification head is attached, shape [batch, num_classes]), quantized model (ONNX int8, TFLite int8, CoreML mlmodel), model size (original ~10MB → quantized ~2.5-5MB), latency metrics (original ~50ms → quantized ~10-15ms on ARM CPU), accuracy metrics (top-1, top-5 on ImageNet validation set), logits batch (torch.Tensor with shape [batch, 1000]), predictions (class indices and confidence scores for each image), embeddings (feature vectors from penultimate layer, shape [batch, 1024]), inference time metrics (latency per batch, throughput in images/sec), ONNX model (.onnx file, opset 11-14), TorchScript model (.pt file, eager or traced), TFLite model (.tflite file, int8 or float32), CoreML model (.mlmodel file, iOS-compatible), NCNN model (.param and .bin files for mobile inference), ensemble logits (averaged or voted predictions, shape [batch, 1000]), confidence scores (per-class agreement across ensemble members), uncertainty estimates (entropy or variance of ensemble predictions), per-model predictions (individual logits from each ensemble member)

UnfragileRank

Adoption85%(40% weight)

Quality22%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

6 capabilities

Visit mobilenetv3_small_100.lamb_in1k→

Model Details

huggingface

Provider

timm

Architecture

17,499,725

Downloads

Tasks

image-classification

About

timm/mobilenetv3_small_100.lamb_in1k — a image-classification model on HuggingFace with 1,74,99,725 downloads

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Capabilities6 decomposed

lightweight-image-classification-inference

Medium confidence

Solves for

Best for

mobile app developers building on-device vision features

edge AI engineers deploying models to IoT/embedded hardware

ML practitioners prototyping lightweight classification pipelines

Requires

PyTorch 1.9+ or TensorFlow 2.4+ (model available in both frameworks via timm)

Python 3.7+

PIL/Pillow for image loading and preprocessing

Limitations

Fixed to 1000 ImageNet classes — requires fine-tuning or head replacement for custom classification tasks

Input images must be resized to 224×224 pixels; aspect ratio distortion may impact accuracy on non-square inputs

Trained on ImageNet-1k only — may have poor generalization to domain-specific images (medical, satellite, etc.) without adaptation

What makes it unique

vs alternatives

transfer-learning-backbone-extraction

Medium confidence

Solves for

Best for

computer vision practitioners building custom detection/segmentation pipelines

researchers experimenting with transfer learning on domain-specific image datasets

teams deploying multi-task models that share a lightweight backbone

Requires

PyTorch 1.9+ with torch.nn.Module subclassing support

timm library (pip install timm) for model loading and layer access

Custom training loop or framework (PyTorch Lightning, Hugging Face Transformers) for fine-tuning

Limitations

Feature maps from early layers are spatially large (56×56 for layer 1) — memory-intensive for high-resolution inputs or large batches

ImageNet pre-training bias may not transfer well to non-natural images (medical, thermal, synthetic); domain adaptation required

Removing the classification head requires manual architecture modification — no built-in API for automatic head swapping

What makes it unique

vs alternatives

quantization-and-model-compression

Medium confidence

Solves for

Best for

mobile app developers deploying models to iOS/Android with strict size constraints (<20MB)

edge AI engineers optimizing inference on ARM processors or specialized accelerators (NPU, TPU)

teams deploying models to bandwidth-constrained environments (IoT, embedded systems)

Requires

PyTorch 1.9+ with torch.quantization module or TensorFlow 2.4+ with tf.lite.TFLiteConverter

Calibration dataset (100-1000 representative images from target domain) for accurate quantization

ONNX Runtime or TensorRT for optimized quantized inference (optional but recommended)

Limitations

Post-training quantization (PTQ) may cause 1-3% accuracy drop on ImageNet; QAT requires retraining with calibration data

int4 quantization (extreme compression) risks >3% accuracy loss; int8 is recommended for production

Quantized models are framework-specific (ONNX int8 ≠ TFLite int8); conversion between formats may introduce precision mismatches

What makes it unique

vs alternatives

batch-inference-with-preprocessing-pipeline

Medium confidence

Solves for

Best for

data scientists processing large image datasets for classification or feature extraction

ML engineers building batch inference pipelines for cloud or on-premise deployment

teams optimizing throughput for inference serving (e.g., batch processing jobs)

Requires

PyTorch 1.9+ with torch.utils.data.DataLoader

timm library for data loading utilities (timm.data.create_loader, timm.data.ImageDataset)

GPU with 4GB+ VRAM for batch sizes >32 (8GB+ recommended for batch sizes 64-256)

Limitations

Batch size is limited by GPU memory; typical batch sizes are 32-256 depending on GPU (8GB-40GB VRAM)

Variable-size inputs require padding or resizing, which may distort aspect ratios and reduce accuracy by 0.5-1%

Preprocessing overhead (resize, normalize) adds ~5-10ms per batch; GPU utilization may be suboptimal for small batches (<16)

What makes it unique

vs alternatives

model-export-and-format-conversion

Medium confidence

Solves for

Best for

mobile app developers deploying to iOS/Android with framework-specific model formats

ML engineers building cross-platform inference services (Python, C++, JavaScript)

teams optimizing models for specific hardware accelerators (ARM, x86, mobile NPU)

Requires

PyTorch 1.9+ with torch.onnx module

ONNX and onnx-simplifier for ONNX export and optimization

TensorFlow 2.4+ and tf2onnx for TFLite conversion

Limitations

ONNX export requires opset version compatibility; older runtimes may not support all operators (e.g., depthwise convolution in opset <11)

TFLite conversion requires TensorFlow installation; PyTorch → TFLite requires intermediate ONNX conversion, introducing potential precision mismatches

TorchScript tracing captures specific input shapes; dynamic batch sizes require scripting mode, which is more complex

What makes it unique

vs alternatives

ensemble-inference-with-multiple-models

Medium confidence

Solves for

Best for

practitioners building production classification systems requiring high accuracy and robustness

teams deploying models in safety-critical applications (medical imaging, autonomous systems)

researchers studying model uncertainty and ensemble methods

Requires

PyTorch 1.9+ with torch.nn.ModuleList for managing multiple models

Multiple model checkpoints (3-5 variants trained with different seeds or augmentation)

GPU with 8GB+ VRAM for loading 3+ models simultaneously

Limitations

Ensemble inference requires N× memory and N× latency compared to single model (e.g., 3 models = 3× slower)

Ensemble benefits plateau at 3-5 models; diminishing returns beyond 5 models for accuracy improvement

Voting/averaging assumes model diversity; identical models trained with same seed provide no benefit

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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mobilenetv3_small_100.lamb_in1k

Capabilities6 decomposed

lightweight-image-classification-inference

transfer-learning-backbone-extraction

quantization-and-model-compression

batch-inference-with-preprocessing-pipeline

model-export-and-format-conversion

ensemble-inference-with-multiple-models

Related Artifactssharing capabilities

pegasus-xsum

GLM-OCR

vit-large-patch16-384

Z-Image-Turbo

TinyML and Efficient Deep Learning Computing - Massachusetts Institute of Technology

Deci

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

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Data Sources

mobilenetv3_small_100.lamb_in1k

Capabilities6 decomposed

lightweight-image-classification-inference

transfer-learning-backbone-extraction

quantization-and-model-compression

batch-inference-with-preprocessing-pipeline

model-export-and-format-conversion

ensemble-inference-with-multiple-models

Related Artifactssharing capabilities

pegasus-xsum

GLM-OCR

vit-large-patch16-384

Z-Image-Turbo

TinyML and Efficient Deep Learning Computing - Massachusetts Institute of Technology

Deci

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to mobilenetv3_small_100.lamb_in1k

Are you the builder of mobilenetv3_small_100.lamb_in1k?

Get the weekly brief

Data Sources