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Resnet50.a1 In1k model by JaxNN
⭐ 37.4
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Model

Resnet50.a1 In1k

by JaxNN hf-model--jaxnn--resnet50.a1_in1k
Nexus Index
37.4 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 2
R: Recency 97
Q: Quality 50
Tech Context
1.024K Ctx
Vital Performance
13 DL / 30D
0.0%
Source →
Audited 37.4 FNI Score
Tiny - Params
1k Context
13 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--jaxnn--resnet50.a1_in1k
License Apache-2.0
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__jaxnn__resnet50.a1_in1k,
  author = {JaxNN},
  title = {Resnet50.a1 In1k Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/jaxnn/resnet50.a1_in1k}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
JaxNN. (2026). Resnet50.a1 In1k [Model]. Free2AITools. https://huggingface.co/jaxnn/resnet50.a1_in1k

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download jaxnn/resnet50.a1_in1k

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.4
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 2
Recency (R) 97
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Resnet50.a1 In1k: Semantic (S:50), Authority (A:0), Popularity (P:2), Recency (R:97), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

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---

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Technical Deep Dive

Model card for resnet50.a1_in1k

A ResNet-B image classification model.

This model features:

  • ReLU activations
  • single layer 7x7 convolution with pooling
  • 1x1 convolution shortcut downsample

Trained on ImageNet-1k in timm using recipe template described below.

Recipe details:

  • ResNet Strikes Back A1 recipe
  • LAMB optimizer with BCE loss
  • Cosine LR schedule with warmup

Model Details

Model Usage

Image Classification

python 
from urllib.request import urlopen
from PIL import Image
import jax

import jaxnn

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg'
))

model = jaxnn.create_model('resnet50.a1_in1k', pretrained=True)
model.eval()

# get model specific transforms (normalization, resize)
data_config = jaxnn.data.resolve_model_data_config(model)
transforms = jaxnn.data.create_transform(**data_config, is_training=False)

output = model(jax.numpy.expand_dims(transforms(img), 0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = jax.lax.top_k(jax.nn.softmax(output, axis=-1) * 100, k=5)

Feature Map Extraction

python 
from urllib.request import urlopen
from PIL import Image
import jax

import jaxnn

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg'
))

model = jaxnn.create_model(
    'resnet50.a1_in1k',
    pretrained=True,
    features_only=True,
)
model.eval()

# get model specific transforms (normalization, resize)
data_config = jaxnn.data.resolve_model_data_config(model)
transforms = jaxnn.data.create_transform(**data_config, is_training=False)

output = model(jax.numpy.expand_dims(transforms(img), 0))  # jax.numpy.expand_dims single image into batch of 1

for o in output:
    # print shape of each feature map in output in format [Batch, Height, Width, Channels]
    # e.g.:
    #  (1, 112, 112, 64)
    #  (1, 56, 56, 64)
    #  (1, 28, 28, 128)
    #  (1, 14, 14, 256)
    #  (1, 7, 7, 512)

    print(o.shape)

Image Embeddings

python 
from urllib.request import urlopen
from PIL import Image
import jax

import jaxnn

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/cats-image/resolve/main/cats_image.jpeg'
))

model = jaxnn.create_model(
    'resnet50.a1_in1k',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model.eval()

# get model specific transforms (normalization, resize)
data_config = jaxnn.data.resolve_model_data_config(model)
transforms = jaxnn.data.create_transform(**data_config, is_training=False)

output = model(jax.numpy.expand_dims(transforms(img), 0))   # output is (batch_size, num_features) shaped Array

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(jax.numpy.expand_dims(transforms(img), 0))
# output is unpooled, a (1, 7, 7, 512) shaped tensor

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
13Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--jaxnn--resnet50.a1_in1k
slug
jaxnn--resnet50.a1_in1k
source
huggingface
author
JaxNN
license
Apache-2.0
tags
jaxnn, image-classification, jax, arxiv:2110.00476, arxiv:1512.03385, license:apache-2.0, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
1,024
pipeline tag
image-classification

📊 Engagement & Metrics

downloads
13
stars
0
forks
0

Data indexed from public sources. Updated daily.